BR112018071556B1 - METHOD AND SYSTEM FOR DETERMINING TISSUE OXYGEN SATURATION WITH MELANIN CORRECTION - Google Patents

Abstract

The present invention relates to an oximeter probe that takes into account tissue color (e.g., skin color or melanin content) to improve accuracy when determining tissue oxygen saturation. Light is transmitted from a light source into tissue that has melanin (e.g., eumelanin or pheomelanin). The light reflected from the tissue is received by a detector. A compensation factor is determined to take into account absorption due to melanin. The oximeter uses this compensation factor and determines a melanin-corrected oxygen saturation value.

Description

[0001] CROSS REFERENCE TO RELATED ORDERS

[001] This application claims the benefit of the following Patent Applications: U.S. 62/325,919, filed on April 21, 2016, 62/326,630, 62/326,644 and 62/326,673, filed on April 22, 2016. These applications are incorporated by reference together with all other references cited in these applications.

[0002] BACKGROUND OF THE INVENTION

[002] The present invention generally relates to optical systems that monitor oxygen levels in tissue. More specifically, the present invention relates to optical probes, such as oximeters, which include sources and detectors on optical probe sensor heads and which utilize locally stored simulated reflectance curves to determine tissue oxygen saturation.

[003] Oximeters are medical devices used to measure tissue oxygen saturation in humans and living things for various purposes. For example, oximeters are used for medical and diagnostic purposes in hospitals and other medical facilities (e.g., surgery, patient monitoring, or ambulance or other mobile monitoring for, e.g., hypoxia); sport and athletics purposes in a sports arena (e.g. professional athlete monitoring); personal or home monitoring of individuals (e.g., general oxygen monitoring or training people for a marathon); and veterinary purposes (e.g., animal monitoring).

[004] Pulse oximeters and tissue oximeters are two types of oximeters that operate on different principles. A pulse oximeter requires a pulse in order to function. A pulse oximeter typically measures the absorbance of light due to pulsating arterial blood. In contrast, a tissue oximeter does not require a pulse in order to function, and can be used to take oxygen saturation measurements of a flap of tissue that has been disconnected from a blood supply.

[005] Human tissue, as an example, includes a variety of light-absorbing molecules. Such chromophores include oxygenated hemoglobin, deoxygenated hemoglobin, melanin, water, lipid and cytochrome. Oxygenated hemoglobin, deoxygenated hemoglobin, and melanin are the most dominant chromophores in tissue for much of the visible and near-infrared spectral range. Light absorption differs significantly for oxygenated and deoxygenated hemoglobins at certain wavelengths of light. Tissue oximeters can measure oxygen levels in human tissue by exploiting these differences in light absorption.

[006] Despite the success of existing oximeters, there is a continued desire to improve oximeters, for example, improving measurement accuracy; reducing measurement time; reducing cost; reducing size, weight, or form factor; reducing energy consumption; and for other reasons, and any combination of these measurements.

[007] Specifically, assessing a patient's oxygenation status, both at the regional and local level, is important as this is an indicator of the health status of the patient's local tissue. Therefore, oximeters are often used in clinical settings, such as during surgery and recovery, where it may be suspected that the patient's tissue oxygenation status is unstable. For example, during surgery, oximeters must be able to quickly provide accurate oxygen saturation measurements under a variety of non-ideal conditions. Although existing oximeters have been sufficient for post-operative tissue monitoring where absolute accuracy is not critical and trend data alone is sufficient, accuracy is nevertheless required during surgery in which spot checking can be utilized. to determine whether the tissue could remain viable or needs to be removed.

[008] Therefore, there is a need for tissue oximeter probes and methods for making measurements using these probes.

BRIEF SUMMARY OF THE INVENTION

[009] An oximeter probe that takes into account tissue color (e.g., skin color or melanin content) to improve accuracy when determining tissue oxygen saturation. Light is transmitted from a light source into tissue that has melanin (e.g., eumelanin or pheomelanin). Light reflected from tissue is received by a detector. A compensation factor is determined to take into account absorption due to melanin. The oximeter uses this compensation factor and determines a melanin-corrected oxygen saturation value.

[0010] In one implementation, to calculate oxygen saturation, an oximeter probe uses a relatively large number of simulated reflectance curves to quickly determine the optical properties of tissue under investigation. Tissue optical properties allow further determination of tissue oxygenated hemoglobin and deoxygenated hemoglobin concentrations, as well as tissue oxygen saturation.

[0011] In one implementation, the oximeter probe can measure oxygen saturation without requiring a pulse or heartbeat. An oximeter probe of the invention is applicable to many areas of medicine and surgery including plastic surgery. The oximeter probe can take oxygen saturation measurements of tissue where there is no pulse. Such tissue may have been separated from the body (e.g., a flap) and will be transplanted to another location in the body. Aspects of the invention may also be applicable to a pulse oximeter. In contrast to an oximeter probe, a pulse oximeter requires a pulse in order to function. A pulse oximeter typically measures light absorption due to pulsating arterial blood.

[0012] Tissue oximeters can generate skewed oximeter measurements for tissues that have a different melanin content. In one implementation, the oximeter probe can make tissue oximetry measurements where melanin concentrations tend not to make calculated relative oxygen saturation measurements. The oximeter probe exploits the relatively constant slope of melanin absorption coefficients, where the slope tends not to change regardless of whether the melanin content in the tissue is relatively high or relatively low. The oximeter probe uses a mathematical determinative proposal so that the melanin concentration contributions to the determined relative oxygen saturation goes to zero. Hereby, actual melanin concentrations do not need to be determined to further determine the relative oxygen saturation of target tissue.

[0013] In one implementation, a method includes transmitting light from a light source of an oximeter probe into a first tissue at a first location to be measured, wherein the first tissue comprises a first melanin component, and the first melanin component comprises at least one of eumelanin or pheomelanin; receiving light on a detector of the oximeter probe that is reflected by the first tissue in response to the transmitted light, wherein the received light comprises a first melanin absorption component due to the first melanin component; determining a melanin compensation component for a melanin absorption component due to a tissue melanin component, wherein the melanin absorption component comprises the first melanin component and the melanin component comprises the first melanin component; and using the melanin compensation component, obtaining a melanin-corrected oxygen saturation value for the first tissue, where the melanin-corrected oxygen saturation value takes into account the melanin absorption component.

[0014] In one implementation, a method includes providing an oximeter device comprising a probe tip comprising source structures and detector structures, wherein the oximeter device will measure the oxygen saturation of a tissue comprising eumelanin and pheomelanin; providing the oximeter device with an indication of a skin color of tissue to be measured; use the indication of a skin color to calculate the oxygen saturation of tissue comprising eumelanin and pheomelanin to obtain a melanin-corrected oxygen saturation value; and display the melanin-corrected oxygen saturation value on a display.

[0015] In one implementation, a system includes an oximeter device that includes a probe tip that includes source structures and a detector structures on a distal end of the device and includes a display closest to the probe tip. The oximeter device calculates a melanin-corrected oxygen saturation value, and displays the melanin-corrected oxygen saturation value on the display. The oximeter device is specially configured to utilize the probe tip to make a first measurement and a second measurement to determine the melanin-corrected oxygen saturation value and to receive first information based on the first measurement of a first tissue at a first location. . The melanin-corrected oxygen saturation value is unavailable for display after the first measurement is taken and before the second measurement is taken. The oximeter device is specially configured to receive second information based on the second measurement of a second tissue at a second location where the second location is different from the first location; use the first information and second information to determine the corrected oxygen saturation value in melanin. The melanin-corrected oxygen saturation value takes into account the melanin components of the first tissue and second tissue, and the melanin components comprise eumelanin and pheomelanin. And the oximeter device is configured to display the melanin-corrected oxygen saturation on the display.

[0016] Other objects, features, and advantages of the present invention will become apparent upon consideration of the following detailed description and accompanying drawings, in which like reference designations represent like features throughout all Figures.

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 shows an oximeter probe in one implementation.

[0018] Figure 2 shows an end view of the probe tip in one implementation.

[0019] Figure 3 shows a block diagram of an oximeter probe in one implementation.

[0020] Figure 4 shows a flowchart of a method for determining optical properties of tissue (e.g., real tissue) by the oximeter probe in one implementation.

[0021] Figure 5 shows a flowchart of a method for determining tissue optical properties by the oximeter probe in one implementation.

[0022] Figure 6 shows a flowchart of a method for determining tissue optical properties by the oximeter probe in one implementation.

[0023] Figure 7 shows an exemplary graph of a reflectance curve, which may be for a specific configuration of a source structures and a detector structures, such as the configuration of a source structures and a tip detector structures probe.

[0024] Figure 8 shows a graph of the absorption coefficient μa in arbitrary units versus wavelength of light for oxygenated hemoglobins, deoxygenated hemoglobins, melanin and water in the tissue.

[0025] Figure 9 shows a table for a database for a homogeneous tissue model of simulated reflectance curves that is stored in the memory of the oximeter probe in an implementation.

[0026] Figure 10 shows a table for a database for a layered tissue model of simulated reflectance curves that are stored in the memory of the oximeter probe in an implementation.

[0027] Figures 11A-11B show a table for a database for a layered tissue model where each row in the database is for four simulated reflectance curves for the four wavelengths of light emitted from the simulated source structures and detected by the simulated detector structures.

[0028] Figures 12A-12B show a flowchart of a method for determining the optical properties of tissue (e.g., real tissue) by the oximeter probe where the oximeter probe uses reflectance data and simulated reflectance curves to determine the properties optics.

[0029] Figure 13 shows a flowchart of another method for determining the optical properties of tissue by the oximeter probe.

[0030] Figure 14 shows a flowchart of a method for weighting reflectance data generated by selected detector structures.

[0031] Figure 15 shows a flowchart of a method for determining relative tissue parameters for the tissue measured by the oximeter probe where melanin contributions in the tissue are removed from the relative tissue parameters.

[0032] Figures 16A and 16B show exemplary plots of absorption coefficients for the first target tissue and the second target tissue illuminated by a number of wavelengths of light, such as 760 nanometers, 810 nanometers, 845 nanometers, and 895 nanometers. Other wavelengths may be utilized by the oximeter probe including longer or shorter wavelengths of light.

[0033] Figure 17A shows an exemplary curve of absorption coefficients for the second target tissue (e.g., breast being reconstructed). The exemplary curve has a negative slope along the entire length of the curve.

[0034] Figure 17B shows an exemplary curve of the first derivative of the absorption coefficients with respect to wavelength for the first target location.

[0035] Figure 17C shows an exemplary curve of the second derivative of the absorption coefficients with respect to wavelength for the first target location.

[0036] Figure 17D shows an exemplary first curve (e.g., first spectrum) and a second exemplary curve (e.g., second spectrum) of the absorption coefficients for the first target tissue (e.g., healthy breast tissue) and the second target tissue (e.g. reconstructed breast tissue).

[0037] Figure 17E shows a first exemplary graph (e.g., upper three points) of the first derivative of absorption coefficients with respect to wavelength for the first target tissue and shows a second graph (e.g., lower three points) of the first derivative of the absorption coefficients with respect to wavelength for the second target tissue.

[0038] Figure 17F shows a first exemplary graph (e.g., two upper points) of the second derivative of the absorption coefficients with respect to wavelength for the first target location and shows a second exemplary graph (e.g., two upper points) of the second derivative of the absorption coefficients with respect to wavelength for the second target location.

[0039] Figure 18 shows a vector in an "angle" space for the values of the second derivatives and plotted with respect to each other.

[0040] Figure 19 shows a first vector (θi, Φi) and a second vector 1903 (θ2, Φ2) in an "angle" space.

[0041] Figure 20 shows one of the delta angles Δθ and ΔΦ in vector space.

[0042] Figure 21A shows a graph for the absorption coefficients (e.g., spectrum) for the fully oxygenated measurements and a graph 21 for the absorption coefficients for the fully deoxygenated measurements.

[0043] Figure 21B shows a graph for the first derivative of the fully oxygenated spectrum with respect to wavelength and a graph for the first derivative with respect to wavelength of the fully deoxygenated spectrum.

[0044] Figure 21C shows a graph for the second derivative with respect to the wavelength of the fully oxygenated spectrum and a graph for the second derivative with respect to the wavelength of the fully deoxygenated spectrum.

[0045] Figure 22 shows the vector (Δθ, ΔΦ) in an angle space where Δθ and ΔΦ are plotted relative to each other.

[0046] Figure 23 shows a baseline corrected vector and the calculated vector corrected for the phantom corrected by the scaling factor for the difference in blood volume between the blood volume for the phantom and patient tissue.

[0047] Figure 24 shows the vector for patient tissue projected over the vector for the phantom.

DETAILED DESCRIPTION OF THE INVENTION

[0048] Figure 1 shows an image of an oximeter probe 101 in one implementation. The oximeter probe 101 is configured to make tissue oximetry measurements, such as intraoperatively and postoperatively. The oximeter probe 101 may be a portable device that includes a probe unit 105, probe tip 110 (also referred to as a sensor head), which may be positioned at one end of a sensing arm 111. oximeter 101 is configured to measure tissue oxygen saturation by emitting light, such as near-infrared light, from probe tip 110 into tissue, and collecting light reflected from tissue at the probe tip.

[0049] The oximeter probe 101 includes a display 115 or other notification device that notifies a user of oxygen saturation measurements made by the oximeter probe. Although probe tip 110 is described as being configured for use with oximeter probe 101, which is a portable device, probe tip 110 may be used with other oximeter probes, such as a modular oximeter probe where the probe tip is at the end of a cable device that attaches to a base unit. The cable device could be a disposable device that is configured for use with one patient and the base unit could be a device that is configured for repeated use. Such modular oximeter probes are well understood by those skilled in the art and have not yet been described.

[0050] Figure 2 shows an end view of the probe tip 110 in one implementation. The probe tip 110 is configured to contact the tissue (e.g., the skin of a patient) for which a tissue oximetry measurement is to be made. Probe tip 110 includes first and second source structures 120a and 120b (generally source structures 120) and includes first, second, third, fourth, fifth, sixth, seventh, and eighth detector structures 125a-125h (generally to detector structures 125). In alternative implementations, the oximeter probe includes more or fewer source structures, includes more or fewer detector structures, or both.

[0051] Each source structure 120 is adapted to emit light (such as infrared light) and includes one or more light sources, such as four light sources that generate the emitted light. Each light source can emit one or more wavelengths of light. Each light source may include a light-emitting diode (LED), a laser diode, an organic light-emitting diode (OLED), a quantum dot LED (QMLED), or other types of light sources.

[0052] Each source structure may include one or more optical fibers that optically connect the light sources to a face 127 of the probe tip. In one implementation, each source structure includes four LEDs and includes a single optical fiber that optically couples the four LEDs to the face of the probe tip. In alternative implementations, each source structure includes more than one optical fiber (e.g., four optical fibers) that optically couples the LEDs to the face of the probe tip.

[0053] Each detector structure includes one or more detectors. In one implementation, each detector structure includes a single detector adapted to detect light emitted from the source structures and reflected from tissue. The detectors can be photodetectors, photoresistors, or other types of detectors. The detector structures are positioned relative to the source structures so that two or more (e.g., eight) unique source-to-detector distances are created.

[0054] In one implementation, the shortest source-to-detector distances are approximately equal. For example, the shortest source-to-detector distances are approximately equal between the source structure 120a and the detector structure 125d (S1-D4) and between the source structure 120b and the detector structure 125a (S2-D8) are approximately equal. The next longest source-to-detector distances (e.g., longer than each of S1-D4 and S2-D8) between the source structure 120a and the detector structure 125e (S1-D5) and between the source 120b and detector structure 125a (S2-D1) are approximately the same. The next longest source-to-detector distances (e.g., longer than each of S1-D5 and S2-D1) between the source structure 120a and the detector structure 125c (S1-D3) and between the source 120b and detector structure 125g (S2-D7) are approximately equal. The next longest source-to-detector distances (e.g., longer than each of S1-D3 and S2-D7) between the source structure 120a and the detector structure 125f (S1-D6) and between the source 120b and detector structure 125b (S2-D2) are approximately the same. The next longest source-to-detector distances (e.g., longer than each of S1-D6 and S2-D2) between the source structure 120a and the detector structure 125c (S1-D2) and between the source 120b and detector structure 125f (S2-D6) are approximately equal. The next longest source-to-detector distances (e.g., longer than each of S1-D2 and S2-D6) between the source structure 120a and the detector structure 125g (S1-D7) and between the source 120b and detector structure 125c (S2-D3) are approximately equal. The next longest source-to-detector distances (e.g., longer than each of S1-D7 and S2-D3) between the source structure 120a and the detector structure 125a (S1-D1) and between the source 120b and detector structure 125e (S2-D5) are approximately the same. The next longest source-to-detector distances (e.g., the longest source-to-detector distance, longer than each of S1-D1 and S2-D5) between the source structure 120a and the detector structure 125h ( S1-D8) and between the source structure 120b and the detector structure 125d (S2-D4) are approximately equal. In other implementations, the source-to-detector distances may all be unique or have fewer than eight distances that are approximately equal.

[0055] Table 1 below shows the eight unique source-to-detector distances according to an implementation. The increase between closest source-to-detector distances is approximately 0.4 millimeters. Table 1

[0056] In one implementation, detector structures 125a and 125e are symmetrically positioned around a point that is on a straight line connecting sources 120a and 120b. Detector structures 125b and 125f are symmetrically positioned around the spot. Detector structures 125c and 125g are symmetrically positioned around a point. Detector structures 125d and 125h are symmetrically positioned around the spot. The point may be centered between source structures 120a and 120b on the connection line.

[0057] A plot of source-to-detector distance versus reflectance detected by detector structures 125 can provide a reflectance curve where data points are well spaced along the geometric x axis. This spacing of distances between source structures 120a and 120b, and detector structures 125 reduces data redundancy and can lead to the generation of relatively accurate reflectance curves.

[0058] In one implementation, the source structures and detector structures may be arranged in various positions on the probe surface to provide the desired distances (as indicated above). For example, the two sources form a line, and there will be an equal number of detectors above and below this line. And the position of one detector (above the line) will have point symmetry with another detector (below the line) around a selected point on the line of the two sources. As an example, the selected point may be in the middle between the two sources, but not necessarily. In other implementations, positioning may be arranged based on a shape, such as a circle, an ellipse, an ovoid, randomly, triangular, rectangular, square, or other shape.

[0059] The following patent applications describe various oximeter devices and oximetry operation, and the discussion in the following applications may be combined with aspects of the invention described in this application, in any combination. The following patent applications are incorporated by reference together with all references cited in these applications 14/944,139, filed November 17, 2015, 13/887,130 filed May 3, 2013, 15/163,565, filed May 24, 2016, 13/887.20, filed on May 3, 2013, 15/214,355, filed on July 19, 2016, 13/887,213, filed on May 3, 2013, 14/977,578, filed on December 21, 2015, 13/887,178, filed on June 7, 2013, 15/220,354, filed on July 26, 2016, 13/965,156, filed on August 12, 2013, 15/359,570, filed on November 22, 2016, 13/ 887,152, filed on May 3, 2013, 29/561,749, filed on April 16, 2016, 61/642,389, 61/642,393, 61/642,395, 61/642,399 filed on May 3, 2012, and 61/68 2,146, deposited on August 10, 2012.

[0060] Figure 3 shows a block diagram of the oximeter probe 101 in one implementation. The oximeter probe 101 includes a display 115, a processor 116, a memory 117, a speaker 118, one or more user selection devices 119 (e.g., one or more buttons, switches, associated touch input device with display 115), a set of source structures 120, a set of detector structures 125, and a power source (e.g., a battery) 127. The components listed above may be connected together via a bus 128, which may be the system bus architecture of the oximeter probe 101. Although this Figure shows a bus that is connected to each component, the bus is illustrative of any interconnection scheme that serves to connect these components or other components included in the oximeter probe. oximeter 101. For example, the speaker 118 could be connected to a subsystem through a port or have an internal direct connection to the processor 116. Furthermore, the described components are housed within a movable housing (see Figure 1) of the oximeter probe 101 in one implementation.

[0061] Processor 116 may include a microprocessor, a microcontroller, a multi-core processor, or another type of processor. The memory 117 may include a variety of memories, such as a volatile memory 117a (e.g., a RAM), a non-volatile memory 117b (e.g., a disk or FLASH). Different implementations of oximeter probe 101 may include any number of the listed components, in any combination or configuration, and may also include other components not shown.

[0062] The power source 127 may be a battery, such as a disposable battery. Disposable batteries are discarded after their stored charge has been exhausted. Some disposable battery chemistry technologies include alkaline, zinc carbon, or silver oxide. The battery has enough charge stored to allow you to use the portable device for several hours. In one implementation, the oximeter probe is a disposable

[0063] In other implementations, the battery is rechargeable, where the battery can be recharged multiple times after the stored charge is depleted. Some rechargeable battery chemistry technologies include nickel cadmium (NiCd), nickel metal hydride (NiMH), lithium ion (Li-ion), and zinc air. The battery can be recharged, for example, via an AC adapter with a cable that connects to the portable unit. The circuit in the portable unit may include a recharger circuit (not shown). Batteries with rechargeable battery chemistry can sometimes be used as disposable batteries, where the batteries are not recharged but discarded after use.

[0064] Tissue Analysis. Figure 4 shows a flowchart of a method for determining optical properties of tissue (e.g., real tissue) by oximeter probe 101 in one implementation. Oximeters are used to measure tissue with melanin, such as skin. Melanin affects oxygen saturation measurements because melanin absorbs light. Skin colors vary widely from person to person, from very dark skin to very light skin. Depending on the amount of melanin present, which will vary depending on, for example, skin color, the amount of absorption can have a substantial effect on the measurement, making the measured value inaccurate.

[0065] Therefore, there is a need for an oximeter that takes into account a melanin component of the tissue being measured, so that the measured oxygen saturation value is inaccurate regardless of skin color. The measured oxygen saturation value which takes into account and compensates for the melanin component of tissue can be referred to as a melanin-corrected oxygen saturation value.

[0066] The melanin in the skin is eumelanin and pheomelanin (e.g., two components of melanin), which are naturally occurring melanins in various relative percentages. In contrast, most internal organs and tissue in the human body do not have melanin. Thus, there is no need to take melanin into account when using an oximeter to take oxygen saturation measurements for such internal tissue. However, the human brain has neuromelanin, which is not present in other locations in the body, especially the skin.

[0067] In one implementation, the oximeter determines and corrects the absorption of melanin in skin tissue (and any other tissue) that has eumelanin and pheomelanin pigments. This oximeter does not take into account and correct neuromelanin or synthetic melanins as these are not found in the skin. In other implementations, the oximeter determines and corrects melanin absorption due to a variety of melanins, including eumelanin, pheomelanin, or neuromelanin or synthetic melanins, or any combination thereof.

[0068] The oximeter probe uses a determined melanin content (e.g., eumelanin content, pheomelanin content, or both eumelanin and pheomelanin content) for the tissue to correct various tissue parameters that are measured by the oximeter probe . The flowchart represents an exemplary implementation. Steps can be added to, removed from, or combined in the flowchart without deviating from the scope of the implementation.

[0069] At 400, a melanin reader optically couples (e.g., contacts) the tissue. Melanin readers are optoelectronic devices that are adapted to emit light, step 405, into tissue, and detect light, step 410, after it has been transmitted through tissue or reflected from tissue. The light detected by the melanin reader is converted to electrical signals, step 415, which are used by the device to determine the tissue melanin content, step 420. The melanin reader can output a value for the melanin content, step 425, on a reader display or via a wired or wireless output. The value for melanin content represents values for eumelanin content, pheomelanin content, or both eumelanin and pheomelanin content.

[0070] In one implementation, at 430, information (e.g., a numerical value) about melanin content is entered into the oximeter probe 101. The information may be entered into the oximeter probe via a user (e.g., a human user) or through wired or wireless communication between the melanin reader and the oximeter probe.

[0071] In a first implementation, at 435, the oximeter probe uses the information for melanin content to adjust one or more measured values generated by the probe. In one implementation, the oximeter probe determines a value for tissue oxygen saturation. The oximeter probe subsequently adjusts the value for oxygen saturation using the information for melanin content (e.g., information for eumelanin content, pheomelanin content, or both eumelanin and pheomelanin content). The oximeter probe can adjust the value for oxygen saturation through one or more arithmetic operations, mathematical functions, or both. For example, the information for melanin content can be used as an offset (e.g., additive offset), a scaling factor, or both to adjust the value for oxygen saturation.

[0072] In an alternative implementation, at 435, the oximeter probe determines the absorption coefficient μa(mua), the reduced scattering coefficient μs' (mus prime), or both for the tissue for a number of wavelengths of light (e.g. four wavelengths of light) emitted and detected by the oximeter probe. Subsequently, the oximeter probe adjusts the determined absorption values (μa) for each wavelength of light using information about melanin content (e.g., eumelanin content, pheomelanin content, or both eumelanin and pheomelanin content). The oximeter probe can adjust absorption values (μa) through one or more arithmetic operations, mathematical functions, or both. For example, the information for melanin content can be used as an offset (e.g., additive offset), a scaling factor, or both to adjust absorption values (μa). Subsequently, the oximeter probe uses the absorption values (μa) to determine a value for oxygen saturation for the tissue. The determination of absorption (μa) and reduced scattering (μs) are described below.

[0073] In another implementation, at 435, the oximeter probe applies one or more melanin correction functions to reflectance data generated by detector structures. Melanin correction functions are based on information for melanin content (e.g., eumelanin content, pheomelanin content, or both eumelanin and pheomelanin content). The reflectance data may be analog reflectance data generated by the detector structures before being digitized by one or more electronic components of the oximeter probe, or the reflectance data may be digitized reflectance data. Melanin correction functions can be applied to analog reflectance data or digitized reflectance data. The melanin correction function includes one or more mathematical operations that are applied to the reflectance data. The scaling factors are determined by the oximeter probe based on information for the melanin content that is input into the oximeter probe. Reflectance data can be adjusted for melanin content for each wavelength of light emitted by the oximeter probe.

[0074] In one implementation, the melanin correction function may be a combined function (e.g., having scaling factors) that is combined with one or more calibration functions (e.g., having scaling factors). The calibration function may include scaling factors to correct detector responses based on a variety of factors, such as differences that occur as a result of manufacturing, that occur as a result of temperature fluctuation of detector structures, or other considerations. After the reflectance data is adjusted by the oximeter probe, the probe can then determine the oxygen saturation of the blood in the tissue to be measured.

[0075] Figure 5 shows a flowchart of a method for determining optical properties of tissue by oximeter probe 101 in one implementation. The oximeter probe uses melanin content information (e.g., information for eumelanin content, pheomelanin content, or both eumelanin and pheomelanin content) for the tissue to correct various tissue parameters measured by the oximeter probe. The flowchart represents an exemplary implementation. Steps can be added to, removed from, or combined in the flowchart without deviating from the scope of the implementation.

[0076] At 500, the fabric color is compared to two or more color samples from a number of color samples (sometimes referred to as color samples) to determine whether the color of one of the color samples approximately matches the color of fabric. Each color sample used for color comparison is associated with a melanin content value. Information (e.g., a numerical value) identifying the melanin content for the color sample may be located on the color sample. In one implementation, the colors are the Pantone® colors of Pantone LLC of Carlstadt, New Jersey.

[0077] Comparison between the fabric color and the color of the color samples can be performed by a color comparison tool, such as one or more color comparison tools from X-Rite, Incorporated of Grand Rapids Michigan. In one implementation, the comparison may be performed visually by a human, such as the patient or a medical provider. In one implementation, the oximeter probe is adapted to determine a value for tissue melanin content, which can be displayed on the probe display.

[0078] At 505, subsequent to the comparison, the value for tissue melanin content is determined based on the comparison.

[0079] In an alternative implementation, the value for melanin content is determined from an estimate of the content based on a finite range of melanin content values. The number of values in a range for melanin content may include two or more values.

[0080] For example, the number of values in a range for melanin contents may be 2 (e.g., binary levels), such as 1 for light-colored tissue (e.g., first skin level to first skin content level). of melanin) and 2 for dark tissue (e.g. second skin level to second level of melanin content), it can be 3 (e.g. 1 for light tissue color, 2 for medium color tissue, darker than 1 , and 3 for dark-colored tissue, darker than 1 and 2), or 4, 5, 6, 7, 8, 9, 10 or more values for different skin colors. An estimate of the value for melanin content can be provided by the patient or a medical provider.

[0081] At 510, information about melanin content can be entered into the oximeter probe. Step 510 may be skipped in a method where the oximeter probe determines the value for melanin content. Button 119 can be activated a predetermined number of times to place the oximeter probe into a data input mode in which information for melanin content can be entered. Information for the melanin content can subsequently be entered into the probe by additional button activation, via wired communication with the probe, via wireless communication with the probe, via the display if the display is an interface display touch, via an audible interface (e.g., a microphone and voice recognition software on the probe), or by other input techniques. Alternatively, the button interface may provide (e.g., through interaction with the processor) selectable options on the display (or otherwise provided options, such as illuminated LEDs) for melanin content (e.g., 1 and 2 for fair skin). and dark, 1, 2, and 3 for light, medium, and dark skin color, or more user-selectable levels). Where the display is a touch interface display, user selectable options for melanin level may be displayed on the display, where a user may tap the selection there. In implementing the oximeter device operation with another user input device (e.g., mouse, external keyboard, or the like) the user may select user-selectable melanin options using one or more of these devices.

[0082] At 515, the oximeter probe is adapted to use information about melanin content to adjust one or more measurements or calculations performed by the oximeter probe. For example, the oximeter probe may use the information to adjust the oxygen saturation value for the tissue, adjust absorption (μa), adjust reduced scatter (μs'), adjust values generated by the detector or detectors, or one or more a combination of these adjustments. Each of these adjustments is described further above with respect to step 435.

[0083] Figure 6 shows a flowchart of a method for determining optical properties of tissue by oximeter probe 101 in one implementation. The oximeter probe uses the determined melanin content of tissue to correct various tissue parameters that are measured by the probe. The flowchart represents an exemplary implementation. Steps can be added to, removed from, or combined in the flowchart without deviating from the scope of the implementation.

[0084] At 600, one or more contralateral tissue measurements are made with the oximeter probe. Contralateral measurements are made using the oximeter probe over a portion of tissue (e.g., healthy breast tissue, left breast, left side of a breast) before a measurement is made using the oximeter probe over the target tissue that must be measured (e.g., breast tissue for which tissue health is to be determined, e.g., right breast, right side of a breast where the first target tissue is the left side of the breast). Contralateral tissue measurements can be made for each wavelength of light emitted by the oximeter probe.

[0085] At 605, the reflectance data generated by the detector structures is digitized by the electronic elements of the oximeter probe and is stored in memory. The reflectance data provides a basis of comparison for subsequent tissue measurement. For example, contralateral measurements provide baseline measurements of the melanin content of the contralateral tissue, where the baseline measurements can be used by the processor to correct the various measurements made by the oximeter probe.

[0086] At 610, oximetry measurements of the target tissue to be measured are made by the oximeter probe.

[0087] At 615, in one implementation, the processor generates oxygen saturation values for the target tissue using oximetry measurements. Subsequently, the processor retrieves the stored reflectance data stored at 605 for the contralateral tissue and uses the retrieved values to adjust the oxygen saturation values. That is, the processor uses the baseline measurement of melanin content for the healthy contralateral tissues to adjust the oxygen saturation values of the target tissue.

[0088] At 615, in an alternative implementation, the processor determines the absorption μa, reduced scattering coefficient μs', or both from oximetry measurements of the target tissue. Subsequently, the processor retrieves the reflectance data stored at 605 for the contralateral tissue and uses the retrieved values to adjust μa, μs, or both. The processor then uses the adjusted μa value to calculate values for oxygenated hemoglobin, deoxygenated hemoglobin, or other values for the target tissue. That is, the processor uses the baseline measurement for melanin content of the contralateral healthy tissue to adjust μa for the target tissue.

[0089] At 615, in another alternative implementation, the processor retrieves the stored reflectance data stored at 605 for the contralateral tissue and uses the retrieved values to adjust the reflectance data generated by the detector structures for the target tissue. The adjustments applied by the processor to the reflectance data may be simple offsets (e.g., addition offsets), scaling factors (e.g., multiplication offsets), functional corrections, other corrections, or any of these adjustments in any combination. That is, the processor adjusts the values generated by the detector structures using the baseline measurement for melanin content for the healthy tissue to adjust the reflectance data for the target tissue.

[0090] The Stored Simulated Reflectance Curves. According to one implementation, memory 117 stores a number of simulated Monte-Carlo reflectance curves 315 ("simulated reflectance curves") which can be generated by a computer for subsequent storage in memory. Each of the simulated reflectance curves 315 represents a simulation of light (e.g., near-infrared light) emitted from one or more simulated source structures within the simulated tissue and reflected from the simulated tissue to tooth of one or more simulated detector structures. . The simulated reflectance curves 315 are for a specific configuration of simulated source structures and simulated detector structures, such as the configuration of source structures 120a - 120b and detector structures 125a - 125h of probe tip 110 that has the spacing of source to detector described above with respect to Figure 2.

[0091] Therefore, the simulated reflectance curves 315 model the light emitted from the source structures and collected by the detector structure of the oximeter probe 101. Furthermore, each of the simulated reflectance curves 315 represents a unique real tissue condition, such as specific tissue absorption and tissue scattering values that relate to specific concentrations of tissue chromophores and specific concentrations of tissue scatterers. For example, simulated reflectance curves can be generated for a simulated tissue that has various melanin contents, various concentrations of oxygenated hemoglobin, various concentrations of deoxygenated hemoglobin, various concentrations of water, a static value for the water concentrations, various concentrations of fat, a static value for the fat concentration, or various values of absorption (μa) and reduced scattering (μs').

[0092] The number of simulated reflectance curves stored in memory 117 can be relatively large and can represent almost all, if not all, practical combinations of optical properties and tissue properties that may be present in the actual tissue that is analyzed for viability by the oximeter probe 101. Although memory 117 is described as storing simulated Monte-Carlo reflectance curves, memory 117 may store simulated reflectance curves generated by methods other than Monte-Carlo methods, such as using a diffusion approximation. .

[0093] Figure 7 shows an exemplary graph of a reflectance curve, which may be for a specific configuration of source structures 120 and detector structures 125, such as configuration source structures and probe tip detector structures. 110. The horizontal geometric axis of the graph represents the distances between the source structures 120 and the detector structures 125 (i.e., source-to-detector distances). If the distances between the source structures 120 and the detector structures 125 are appropriately chosen, and the simulated reflectance curve is a simulation for the source structures 120 and the detector structures 125, then the lateral spacings between the data points in the simulated reflectance curve they will be relatively uniform. Such uniform spacings can be seen in the simulated reflectance curve in Figure 7. The vertical axis of the graph represents the reflectance of simulated light that reflects from the tissue and is detected by the detector structures 125. As shown by the simulated reflectance curve, light The reflected light reaching the detector structures 125 varies with the distance between the source structures and the detector structures, with the reflected light detected at smaller source-to-detector distances greater than the reflected light detected at a greater source-to-detector distance. .

[0094] Figure 8 shows a graph of the absorption coefficient μa versus wavelength of light for some significant tissue chromophores: blood containing oxygenated hemoglobin, blood containing deoxygenated hemoglobin, melanin and water. In one implementation, the Monte-Carlo simulations used to generate the simulated reflectance curve are functions of one or more selected chromophores that may be present in the tissue. Chromophores may include melanin, oxygenated hemoglobin, deoxygenated hemoglobin, water, lipid, cytochrome, or other chromophores, in any combination. Oxygenated hemoglobins, deoxygenated hemoglobins, and melanin are the most dominant chromophores in tissue for much of the visible and near-infrared spectral range.

[0095] In one implementation, memory 117 stores a selected number of data points for each of the simulated reticence curves 315 and may not store all of the simulated reflectance curves. The number of data points stored for each of the simulated reflectance curves 315 may coincide with the number of source-detector pairs. For example, if probe tip 110 includes two source structures 120a-120b and includes eight detector structures 125a-125h, then oximeter probe 101 includes sixteen source-detector pairs, and memory 117 can thus store sixteen points. of selected data for each of the simulated reflectance curves for each wavelength of light emitted from the source structure 120 or source structure 120b. In one implementation, the data points stored are for the specific source-to-detector distances of the probe tip 110, such as those shown in Table 1.

[0096] Thus, the database of simulated reflectance curves stored in memory 117 could be sized 16 x 5850 where sixteen points are stored per curve that can be generated and emitted by each source structure 120 and measured by each source structure. detector 125, where there are a total of 5850 curves covering the ranges of optical properties. Alternatively, the database of simulated reflectance curves stored in memory 117 could be sized 16 x 4 x 5850 where sixteen points are stored per curve for four different wavelengths that can be generated and emitted by each source structure and where there is a total of 5850 curves covering the ranges of optical properties. The 5850 curves originate, for example, from a matrix of 39 values of scattering coefficients μs' and 150 values of absorption coefficients μa. In other implementations, more or fewer simulated reflectance curves are stored in memory. For example, the number of simulated reflectance curves stored in memory may vary from approximately 5000 curves, to approximately 250,000 curves, to approximately 400,000 curves, or more.

[0097] The values of the reduced dispersion coefficient μs' could vary from 5: 5: 24 per centimeter. The μa values could vary from 0.01: 0.01: 1.5 per centimeter. It will be understood that the ranges described above are exemplary ranges and the number of source-detector pairs, the number of wavelengths generated and emitted by each source structure, and the number of simulated reflectance curves may be smaller or larger.

[0098] Figure 9 shows a database 900 of simulated reflectance curves 315 that is stored in the memory of the oximeter probe in an implementation. The database is for a homogeneous tissue model. Each row in the database represents a simulated reflectance curve generated from a MonteCarlo simulation for simulated light emitted into simulated tissue from two simulated source structures (e.g., source structures 120a-120b) and detected by eight structures simulated detector structures (e.g., detector structures 125a-125h) subsequent to simulated tissue reflectance. The Monte-Carlo simulations used to generate the simulated reflectance curves for the databases are for a homogeneous tissue model. The simulated tissue for the homogeneous tissue model has homogeneous optical properties from the tissue surface through the epidermis, dermis, and subcutaneous tissue. That is, the optical properties of the epidermis, dermis and subcutaneous tissue are the same for the Monte-Carlo simulations. In the database, each of the simulated reflectance curves is associated with a value for absorption (μa) and a value for reduced scattering (μs'). Each of the simulated reflectance curves in the database may be associated with values for other chromophores.

[0099] The database of simulated reflectance curves may include real values (e.g., floating point values) for simulated reflectances or may include indexed values (e.g., binary values) for the real values for the simulated reflectances. As shown in Figure 9, the database includes indexed values (e.g., binary values) for the actual values for the simulated reflectances. The database may include binary words of a variety of lengths dependent on, for example, the precision of the entries. Binary words can be 2 bits long, 4 bits long, 8 bits long, 16 bits long, 32 bits long, or other lengths.

[00100] In one implementation, one or more mathematical transforms are applied to the simulated reflectance curves before entering the values for the curves into the database. Mathematical transforms can improve the fit of reflectance data generated by detector structures to simulated reflectance curves. For example, a log function can be applied to the simulated reflectance curves to improve the fit of the measured data generated by the detector structures to the simulated reflectance curves.

[00101] When an oximetry measurement is made, the reflectance data for each wavelength of emitted light is detected by the detector structures and adjusted to the simulated reflectance curves from database 900 individually. For the reflectance data for each wavelength of emitted light fitted to the simulated reflectance curves, the oximeter probe determines the absorption μa, the reduced scatter μs', or both of these values. For example, a first set of reflectance data for a first wavelength of light is fitted to simulated reflectance curves to determine one or more absorption μa and reduced scattering μs' (e.g., a first set of tissue parameters ). The fitting of the reflectance data to the simulated reflectance curves is further described below.

[00102] Subsequently, a second set of reflectance data for a second wavelength of light is fitted to the simulated reflectance curves in database 900 to determine one or more absorption μa, and reduced scattering μs' (e.g. , a second set of tissue parameters) for the second wavelength. Thereafter, a third set of reflectance data for a third wavelength of light is fitted to the simulated reflectance curves in database 900 to determine one or more absorption μa, and reduced scattering μs' (e.g., a third tissue parameter set). Subsequently, a fourth set of reflectance data for a fourth wavelength of light is fitted to the simulated reflectance curves in database 900 to determine one or more absorption μa, and reduced scattering μs' (e.g., a quarter tissue parameter set) for the fourth wavelength.

[00103] The four sets of tissue parameters can then be used by the oximeter probe together to determine various values for the tissue, such as oxygenated hemoglobin concentration, deoxygenated hemoglobin concentration, melanin content, or other parameters.

[00104] Figure 10 shows a database 1000 of simulated reflectance curves that is stored in the memory of the oximeter probe in an implementation. The database is for a layered tissue model (e.g. layered skin). The Monte-Carlo simulations that generated the simulated reflectance curves use the layered fabric model for the simulations. Layered fabric may include two or more layers. In one implementation, the layered fabric includes two layers of fabric. The two fabric layers have different optical properties, such as different absorption μa, reduced scattering μs', or both of these properties.

[00105] In one implementation, a first simulated tissue layer is for the epidermis and a second simulated tissue layer is for the dermis. The thickness of the epidermis used in Monte-Carlo simulations can vary from approximately 40 microns to approximately 140 microns. For example, the thickness for the epidermis may be 40 microns, 50 microns, 60 microns, 70 microns, 80 microns, 90 microns, 100 microns, 110 microns, 120 microns, 130 microns, 140 microns, or other thicknesses. The thickness of the dermis used in Monte-Carlo simulations can vary from less than 1 millimeter to an effectively infinite thickness, such as 12 millimeters or greater.

[00106] One or more optical properties of the epidermis can be varied when simulated reflectance curves are generated for the dermis. For example, melanin content can be varied for the epidermis when simulation reflectance curves are generated for the dermis. Alternatively, μa can be varied for the epidermis when simulation reflectance curves are generated for the dermis.

[00107] In one implementation, database 1000 includes simulated reflectance curves for light that is reflected by the combination of epidermis and dermis.

[00108] The reflectance data for each wavelength of light emitted by the source structures and detected by the detector structures for real tissue measured by the oximeter probe are fitted to the simulated reflectance curves one at a time by the processor. Based on the fit to one or more of the simulated reflectance curves in the database, the oximeter probe determines one or both of the absorption μa, and the reduced scattering μs' for the real tissue for one or both layers. From the absorption values (μa) determined for a layer, the oximeter probe determines the concentrations of oxygenated and deoxygenated hemoglobin for the tissue.

[00109] Figures 11A-11B show a database 1110 of simulated reflectance curves stored in the memory of the oximeter probe in one implementation. The database is for a layered fabric model. Each row of the database includes simulated reflectance curves for each of four wavelengths of light emitted from simulated source structures and detected by simulated detector structures. Each row of four simulated reflectance curves includes 16 values for each simulated reflectance curve. More specifically, each row includes 16 values for the 16 source-to-detector distances for source structures 120a and 120b and detector structures 125a-125h. In total, each row includes 64 values for the four simulated reflectance curves for four wavelengths of light emitted from the two simulated source structures and detected by the eight simulated detector structures.

[00110] The layered tissue model for database 1110 may include more or less simulated reflectance curves per line if more or fewer wavelengths are emitted from the source structures. Database 1110 may include more or less than 16 values for each of the simulated reflectance curves if, for example, one or more than two source structures are included in the probe tip, more or fewer detector structures are included. included in the probe tip, or both.

[00111] Each of the four simulated reflectance curves for each row of database 1110 is associated with four tissue parameters, including melanin content, blood volume, dispersion, and oxygen saturation (the fraction of oxygenated hemoglobin relative to total hemoglobin for tissue). More or fewer tissue parameters may be included in the database 1110.

[00112] When a set of detector values that are generated by detector structures 125a-125h for the tissue to be measured by the oximeter probe are adjusted by the processor for one or more of the lines, the oximeter probe thereby determines, in any combination, one or more of the tissue parameters such as melanin content, blood volume, dispersion, and oxygen saturation. In one implementation, the oximeter probe is adapted to determine oxygen saturation for tissue and display a value for oxygen saturation on the display.

[00113] As briefly described above, database 1110 includes simulated reflectance curves 315 for a layered fabric model. The layers of the simulated tissue may include the epidermis, the dermis, subcutaneous tissue, or any combination of one or more of these layers. The layers may include higher resolution skin morphology, such as the reticular dermis and superficial plexus. Monte-Carlo simulations that generate the simulated reflectance curve can simulate tissue for various chromophores included in tissue layers. For example, Monte-Carlo simulations might utilize a tissue model for the epidermis that has various melanin contents, but might not utilize a tissue model for the epidermis that includes blood. Monte-Carlo simulations can utilize a tissue model for the dermis layer that has various blood volumes and various oxygen saturations. In one implementation, Monte-Carlo simulations do not use a tissue model for the dermis that includes melanin. Similarly, Monte-Carlo simulations can use a tissue model of adipose tissue that has various blood volumes and various oxygen saturations. In one implementation, the Monte-Carlo simulations do not use a tissue model for adipose tissue that has melanin. Tissue models for tissue layers may include concentrations for other tissue chromophores, such as water and fat, where the concentrations of these chromophores are relatively typical physiological values.

[00114] In one implementation, the various chromophore concentrations that Monte-Carlo simulations use to generate the simulated reflectance curves cover a relatively large and relatively accurate range of actual physiological values present in real tissue. The number of values included in the actual physiological value ranges can be varied to balance various parameters of tissue oximeter measurements. For example, the number of values used for the range of chromophore concentrations in the simulated tissue may be relatively high or low, and affect the accuracy of measurements made by the oximeter probe. In one implementation, 355 values are used in Monte-Carlo simulations for the range of melanin content for light absorption in simulated epidermal tissue. In one implementation, 86 values are used in Monte-Carlo simulations for the range of melanin content for light absorption in simulated dermal tissue. For scattering in both simulated epidermal tissue and simulated dermal tissue, 65 values are used in the Monte-Carlo simulations. In other implementations, the number of these values is different.

[00115] Tissue Analysis. Figures 12A-12B show a flowchart of a method for determining the optical properties of tissue (e.g., skin) by the oximeter probe 101 where the oximeter probe uses reflectance data and simulated reflectance curves 315 to determine the optical properties. Optical properties may include the absorption coefficient μa and the reduced scattering coefficient μs' of the tissue. An additional method for converting tissue absorption coefficient μa to tissue oxygen saturation values is described in more detail below. The flowchart represents an exemplary implementation. Steps can be added to, removed from, or combined in the flowchart without deviating from the scope of the implementation.

[00116] At 1200, the oximeter probe 101 emits light (e.g., near-infrared light) from one of the source structures 120, such as the source structure 120a, into the tissue. The oximeter probe is generally in contact with the tissue when light is emitted from the source structure. After the emitted light reflects from the tissue, the detector structures 125 detect a portion of this light, step 1205, and generate reflectance data points for the tissue, step 1210. Steps 1200, 1205, and 1210 may be repeated for multiple lengths. of light wave (e.g., red, near-infrared light, or both) and to one or more other source structures, such as source structure 120b. Reflectance data points for a single wavelength could include sixteen reflectance data points if, for example, tissue oximeter probe 115 has sixteen source-to-detector distances. Reflectance data points are sometimes referred to as a vector N of reflectance data points.

[00117] At 1215, the reflectance data points (e.g., raw reflectance data points) are corrected for gain of the source-detector pairs. During calibration of the source-detector pairs, gain corrections are generated for the source-detector pairs and are stored in memory 117. The generation of gain corrections is described in more detail below.

[00118] At 1220, the processor 116 adjusts (e.g., through a sum squared error calculation) the reflectance data to the simulated reflectance curves 315 to determine the specific reflectance data curve that best fits ( that is, it has the smallest fit error) to the reflectance data points. The database stored in memory and fitted to the reflectance data may be database 900, database 1000, or database 1100. In a particular implementation, a relatively small set of simulated reflectance curves that are a "raw" grid from the database of simulated reflectance curves is selected and used for fitting step 1220. For example, for database 900 data 39 values of scattering coefficient μs' and 150 values of scattering coefficient absorption μa, a raw grid of simulated reflectance curves could be determined by the processor 116 by taking every 5th scattering coefficient value μs' and every 8th absorption coefficient μa for a total of 40 reflectance curves simulated on the raw grid. It will be understood that the specific values above are for an exemplary implementation and that raw grids of other sizes could be used by processor 116. The result of fitting the reflectance data points to the raw grid is a coordinate on the raw grid (μa,μs ')coarse of the best-fit simulated reflectance curve. For the 1000 database, the raw grid will cover absorption in each layer and reduced scattering. Each of the following steps for the method for database 1000 will be adjusted for each layer's μa and μs'. For the 1100 database, the raw grid will cover melanin content, oxygen saturation, blood volume, and dispersion. Each of the following steps for the method for the 1100 database will be adjusted for melanin content, oxygen saturation, blood volume, and dispersion instead of μa and μs'.

[00119] At 1225, the specific simulated reflectance curve of the raw grid that has the smallest fit error is used by processor 116 to define a "fine" grid of simulated reflectance curves where the simulated reflectance curves on the fine grid are adjacent to around the simulated reflectance curve of the raw grid that has the smallest fit error.

[00120] That is, the fine grid is a defined size, with the lowest error simulated reflectance curve of the raw grid defining the center of the fine grid. The fine grid may have the same number of simulated reflectance curves as the raw grid, or it may have more or fewer simulated reflectance curves. The fine grid has a fineness so as to provide a sufficient number of points to determine a peak surface grid of absorption coefficient values μa and scattering coefficient values μs' close, at step 1230, on the fine grid. Specifically, a threshold may be adjusted by processor 116 using the lowest error value of the raw grid plus a specified offset. The positions of the scattering coefficient μs' and the absorption coefficient μa on the fine grating that have errors below the threshold can all be identified for use in determining the peak surface lattice to further determine the scattering coefficient μs' and the μa absorption for the reflectance data. Specifically, an error adjustment is made to the peak to determine the absorption coefficient μa and scattering coefficient μs' values at the peak. A weighted average (e.g., a centroid calculation) of the absorption coefficient μa and scattering coefficient μs' values at the peak can be used by the oximeter probe for determining the absorption coefficient μa and scattering coefficient values. μs' for the reflectance data points for the fabric, step 1240.

[00121] The weights for the absorption coefficient μa and scattering coefficient μs' values for the weighted average can be determined by the processor 116 as the limit minus the fine grid error. As the points on the fine grid are selected with errors below the threshold, this provides positive weights. Weighted averaging calculation (e.g., centroid calculation) makes the predicted scattering coefficient μs' and absorption coefficient μa (i.e., (μa,μs')fine) for the reflectance data points for the tissue . Other methods may be used by the oximeter probe, such as tuning with one or more of a variety of nonlinear least squares to determine the true minimum error peak for the absorption coefficient μa.

[00122] In one implementation, processor 116 calculates the log of reflectance data points and simulated reflectance curves, and divides each log by the square root of the source-to-detector distances (e.g., in centimeters). These log values divided by the square root of the source-to-detector distances can be used by processor 116 for the reflectance data points and reflectance curves simulated in the steps described above (e.g., steps 1215, 1220, 1225, and 1230 ) to fine-tune the fit of the reflectance data points to the simulated reflectance curves.

[00123] According to another implementation, the offset is set essentially to zero, which effectively provides an offset of the difference between the coarse grid minimum and the fine grid minimum. The method described above with respect to Figures 12A-12B is based on minimum fit error for the coarse grid, so that the true minimum error on the fine grid is typically lower. Ideally, the threshold is determined from the lowest error on the fine grid, which would typically require additional computation by the processor.

[00124] The following is a further detailed description for finding the specific simulated reflectance curve that best fits the reflectance data points on the fine grid in an implementation. Figure 12B shows a flowchart of a method for finding the specific simulated reflectance curve that best fits the fine grid reflectance data points in an implementation. The flowchart represents an exemplary implementation. Steps can be added to, removed from, or combined in the flowchart without deviating from the scope of the implementation.

[00125] Subsequent to determining the specific simulated reflectance curve (μa,μs')coarse of the raw grid that best fits the reflectance data points in step 1225, the processor 116 computes an error surface in a region around ( μa,μs')coarse on the total simulated reflectance curve database (i.e., database of 16 x 5850 (μa,μs')) of simulated reflectance curves, step 1250. The error surface is denoted as : err(μa,μs'). Subsequently, processor 116 finds the minimum error value at err(μa,μs'), which is referred to as errmin, step 1255. Processor 116 then generates a peak surface network of err(μa,μs') that is denoted by pksurf (μa, μs) = k+err^ - err (μa, μ's) if the peak surface is greater than zero, or pksurf (μa, μs) = k+err^ - err (μa, μs ) = 0 if the peak surface is less than or equal to zero, step 1260. In the expression k is chosen from a peak at the minimum point of err( μ , μ' ) with a width above zero of approximately ten elements. The center of mass (i.e., centroid calculation) of the peak in pksurf (μ,μ') uses the point heights as weights, step 1265. The position of the center of mass is the interpolated result for the absorption coefficient μa and scattering coefficient μs' for the reflectance data points for the fabric.

[00126] The method described above with respect to Figures 12A and 12B for determining the absorption coefficient μa and the scattering coefficient μs' for the given points of reflectance for the tissue can be repeated for each of the wavelengths (e.g. , 3 or 4 wavelengths) generated by each of the source structures 120.

[00127] Determination of Oxygen Saturation. According to a first implementation, the processor 116 determines the oxygen saturation for the tissue that is probed by the oximeter probe 101 using the μa absorption coefficients (e.g., 3 or 4 μa absorption coefficients) that are determined (as above described) for the 3 or 4 wavelengths of light that are generated by each source structure 120. According to a first implementation, a look-up table of oxygen saturation values is generated to find the best fit of the absorption coefficients μa to oxygen saturation. The lookup table can be generated by assuming a range of likely values for total hemoglobin, melanin and oxygen saturation and calculating μa for each of these scenarios. Then, the absorption coefficient μa points are converted to a unit vector by dividing by a unit vector norm to reduce the systematic error and only depend on the relative shape of the curve. Then, the unit vector is compared with the lookup table to find the best fit, which gives the oxygen saturation.

[00128] According to a second implementation, the processor 116 determines the oxygen saturation for the tissue by calculating the net analyte signal (NAS) of deoxygenated hemoglobin and oxygenated hemoglobin. The NAS is identified as the portion of the spectrum that is orthogonal to the other spectral components in the system. For example, the NAS of deoxygenated hemoglobin in a system that also contains oxygenated hemoglobin and deoxygenated hemoglobin is the portion of the spectrum that is orthogonal to the oxygenated hemoglobin spectrum and the melanin spectrum. The concentrations of deoxygenated and oxygenated hemoglobin can be calculated by the vector that multiplies the respective NAS by the absorption coefficients previously determined at each wavelength. Oxygen saturation is then readily calculated as the concentration of oxygenated hemoglobin divided by the sum of oxygenated hemoglobin divided by the sum of oxygenated hemoglobin and deoxygenated hemoglobin. Anal. Chem. 58: 1167-1172 (1986) by Lorber is incorporated by reference and provides a framework for further detailed understanding of the second implementation for determining oxygen saturation for tissue.

[00129] In an implementation of the oximeter probe 101, reflectance data is generated by detector structures 125 at 30 Hertz, and oxygen saturation values are calculated at approximately 3 Hertz. A moving average of determined oxygen saturation values (e.g., at least three oxygen saturation values) may be displayed on display 115, which could have an update rate of 1 Hertz.

[00130] Optical Properties. As briefly described above, each simulated reflectance curve 315 that is stored in memory 117 represents unique tissue optical properties. More specifically, the unique shapes of the simulated reflectance curves, for a given wavelength, represent unique values of the optical properties of the tissue, namely the scattering coefficient (μs), the absorption coefficient (μa), the anisotropy of the tissue (g), and tissue refractive index from which tissue properties can be determined.

[00131] The reflectance detected by detector structures 125 for relatively small source-to-detector distances is primarily dependent on the reduced scattering coefficient, μs'. The reduced scattering coefficient is an "integrated" property that incorporates the scattering coefficient μs and the anisotropy g of the tissue where μs'= μs (1 - g), and is used to describe the diffusion of photons in a random walk of many steps of size 1/μs' where each step involves isotropic scattering. Such a description is equivalent to a description of photon motion that uses many small steps 1/μs, each of which involves only a partial deflection angle if there are many scattering events before an absorption event, i.e., μa << μs '.

[00132] In contrast, the reflectance that is detected by the detector structures 125 for relatively large source-to-detector distances is primarily dependent on the effective absorption coefficient μeff, which is defined as + μ'), which is a function of both μa and μs'.

[00133] Thus, measuring reflectance at relatively small source-to-detector distances (e.g., S1-D4 and S2-D8 of Figure 2) and relatively large source-to-detector distances (e.g., S1-D8 and S2-D4 from Figure 2), both μa and μs' can be determined independently of each other. The optical properties of the tissue can, in turn, provide sufficient information to calculate the concentrations of oxygenated hemoglobin and deoxygenated hemoglobin and, therefore, the oxygen saturation of the tissue.

[00134] Iterative Tuning for Data Collection Optimization. Figure 13 shows a flowchart of another method for determining the optical properties of tissue by oximeter probe 101. The flowchart represents an exemplary implementation. Steps can be added to, removed from, or combined in the flowchart without deviating from the scope of the implementation.

[00135] At 1300, the oximeter probe 101 emits light (e.g., near-infrared light) with one of the source structures, such as the source structure 120a, into the tissue. After the emitted light reflects from the tissue, detector structures 125 detect the light, step 1305, and generate reflectance data for the tissue, step 1310. Steps 1300, 1305, and 1310 may be repeated for multiple wavelengths of light. and to one or more other source structures, such as source structure 120b. At 1315, the oximeter probe 101 fits the reflectance data to the simulated reflectance curves 315 and determines the simulated reflectance curve to which the reflectance data has the best fit. The database stored in memory and adjusted for reflectance data may be database 900, database 1000, or database 1100. Subsequently, the oximeter probe 101 determines the optical properties (e.g., μa, and μs' for the 900 database or 1000 database, or melanin content, oxygen saturation, blood volume, and scattering for the 1100 database) for the tissue based on the optical properties of the simulated reflectance curve that best fits the reflectance data, step 1320.

[00136] At 1325 the oximeter probe 101 determines the mean free path of light within the tissue from the optical properties (e.g., mfp = i/(μa+μj)) determined in step 1320. Specifically, the mean free path can be determined from the optical properties obtained from a cumulative reflectance curve that includes the reflectance data for all source-detector pairs (e.g., pair 1: source structure 120a and detector structure 125a; pair 2: source structure 120a and pair 125b detector structure: 120a source structure and 125c detector structure; pair 4: 120a source structure and 125d detector structure; 120a and 125f detector structure; pair 7: 120a source structure and 125g detector structure; pair 8: 120a source structure and 125h detector structure, pair 9: 120b source structure and 125a detector structure, pair 10: structure structure 120b and detector structure 125b... and others).

[00137] At 1330, the oximeter probe 101 determines whether the calculated mean free path for a given tissue region is longer than twice the shortest detector-to-detector distance (e.g., S1-D4 and S2-D8 of Figure 2). If the mean free path is longer than twice the shortest source-to-detector distance, then the collected reflectance data is refitted to the simulated reflectance curves (i.e., reanalyzed) without using the reflectance data collected from the detector structures for the source-to-detector pairs that have the shortest source-to-detector distance. For example, steps 1315-1330 are repeated without using the reflectance data from the detector structure 125e with the source structure 120a acting as the source for the detector structure 125d, and without using the reflectance data from the detector structure 125d. detector 125h with the source structure 120b acting as the source for the detector structure 125h. The process of calculating the mean free path and discarding the reflectance data for one or more source-detector pairs may be repeated until no source-detector pair contributing reflection data to the fit has a source-to-detector distance greater than shorter than half the calculated mean free path. Subsequently, oxygen saturation is determined from the best fit reflectance curve and reported by oximeter probe 101, as in display 115, step 1335.

[00138] Light that is emitted from one of the source structures into the tissue and that travels less than half the mean free path is non-diffusely reflected. The re-emission distance for this light is strongly dependent on the tissue phase function and local tissue composition. Therefore, using reflectance data for this light tends to result in a less accurate determination of optical properties and tissue properties compared to reflectance data for light that has been subjected to multiple scattering events.

[00139] Data Weighting detector structures. Detector structures 125 that are positioned at increasing distances from source structures 120 receive decreasing amounts of tissue reflectance. Therefore, reflectance data generated by detector structures 125 that have relatively short source-to-detector distances (e.g., S1-D4 and S2-D8 of Figure 2) tend to intrinsically exhibit a higher signal compared to reflectance data generated by detector structures that have relatively long source-to-detector distances (e.g., S1-D8 and S2-D4 of Figure 2). Fitting algorithms may therefore preferably fit the simulated reflectance curves to reflectance data that are generated by detector structures 125 that have relatively short source-to-detector distances (e.g., source-to-detector distances less than or equal to average distance between source structures and detector structures) more closely than reflectance data that is generated by detector structures that have relatively long source-to-detector distances (e.g., source-to-detector distances greater than the average distance). For a relatively accurate determination of the optical properties of reflectance data, this distance-proportional bias may be undesirable and can be corrected by weighting the reflectance data as immediately described below.

[00140] Figure 14 shows a flowchart of a method for weighting reflectance data generated by selected detector structures 125. The flowchart represents an exemplary implementation. Steps can be added to, removed from, or combined in the flowchart without deviating from the scope of the implementation.

[00141] At 1400, the oximeter probe 101 emits light from one of the source structures, such as the source structure 120a, into the tissue. After the emitted light reflects from the tissue, detector structures 125 detect the light, step 1405, and generate reflectance data for the tissue, step 1410. Steps 1400, 1405, and 1410 may be repeated for multiple wavelengths of light. and to one or more other source structures, such as source structure 120b. At 1415, the gold probe 101 fits a first portion of the reflectance data to the simulated reflectance curves 315. The database stored in memory and fitted to the reflectance data may be database 900, database 100 , or database 1100. The first portion of the reflectance data is generated by a first portion of detector structures that are less than a threshold distance from the source structure. The threshold distance may be the average distance (e.g., approximate half-range distance) between the source structures and the detector structures. At 1420, the reflectance data for a second portion of the reflectance data is fitted to the simulated reflectance curves. The second portion of reflectance data is generated by the first portion of the detector structures and another detector structure that is at the next largest source-to-detector source distance from the source compared to the threshold distance. For example, if the first portion of detector structures includes the detector structures 125c, 125d, 125e, and 125f, then the detector structure that is at the next largest source-to-detector distance is the detector structure 125g (see Table 1 ).

[00142] At 1425, the fit generated in step 1415 is compared to the fit generated in step 1420 to determine whether the fit generated in step 1420 is better than the fit generated in 1415. As will be understood by those skilled in the art, a "Closeness" of a data fit to a curve is quantifiable based on a variety of parameters, and the closeness of fits is directly comparable to determine the data that has a closest fit (closest fit) to a curve. As will be further understood, a closer fit is sometimes also referred to as a better fit or a tighter fit. If the fit generated in step 1420 is better than the fit generated in step 1415, then steps 1420 and 1425 are repeated with reflectance data that is generated by detector structures that include an additional detector structure (according to example being considered, the detector structure 125c) which is positioned at an increased source-to-detector distance from the source. Alternatively, if the fit generated in step 1420 is not better than the fit generated in step 1415, then reflectance data for detector structures 125 that are positioned at source-to-detector distances that are greater than the threshold distance will not be are used in the adjustment. Subsequently, the oximeter probe 101 uses the setting generated in 1415 or step 1420 (if better than the setting determined in step 1415) to determine the optical properties and oxygen saturation of the tissue, step 1430. Thereafter, the oxygen saturation is reported by oximeter probe 101, as in display 115, step 1435.

[00143] According to an alternative implementation, if the fit generated in step 1420 is not better than the fit generated in step 1415, then the reflectance data is weighted by a weighting factor for detector structures that have distances of source to detector that are greater than the threshold distance so that this weighted reflectance data has a diminished influence on the fit. Reflectance data that is not used in a fit can be considered to have a zero weight and can be associated with the reflectance of the tissue below the tissue layer of interest. The reflectance of the tissue beneath the tissue layer of interest is said to exhibit a characteristic kink in the reflectance curve that indicates this specific reflectance.

[00144] It is noted that curve fitting algorithms that fit reflectance data to simulated reflectance curves can take into account the amount of uncertainty of the reflectance data as well as the absolute location of the reflectance data. The uncertainty in the reflectance data corresponds to the amount of noise from generating the reflectance data by one of the detector structures, and the amount of noise can scale as the square root of the magnitude of the reflectance data.

[00145] According to a further implementation, the oximeter probe 101 iteratively weights the reflectance data based on the amount of noise associated with measurements of the reflectance data. Specifically, reflectance data generated by detector structures that have relatively large source-to-detector distances generally have a lower signal-to-noise ratio compared to reflectance data generated by detector structure that have relatively short source-to-detector distances. . Weighting reflectance data generated by detector structures that have relatively large source-to-detector distances allows these data to contribute to the fit equally or approximately equally to other reflectance data.

[00146] The methods described for matching reflectance data to a number of simulated MonteCarlo reflectance curves provide a relatively quick and accurate determination of the optical properties of the real tissue probed by the oximeter probe. Speed in determining tissue optical properties is an important consideration in the design of intraoperative probes compared to postoperative probes. Furthermore, the MonteCarlo methods described allow robust calibration methods which, in turn, allow the generation of absolute optical properties compared to relative optical properties. Reporting absolute optical properties, as opposed to relative optical properties, is relatively important for intraoperative oximeter probes compared to postoperative oximeter probes.

[00147] Figure 15 shows a flowchart of a method for determining relative tissue parameters for a tissue measured by the oximeter probe where contributions of melanin in the tissue are removed from the relative tissue parameters. The flowchart represents an exemplary implementation. Steps can be added to, removed from, or combined in the flowchart without deviating from the scope of the implementation.

[00148] The method includes making oximeter measurements over different tissue locations (e.g., first and second target tissues) of the patient's body, and using the oximeter measurements to determine a relative tissue parameter for one of the target tissues (e.g. example, the second target tissue). Different target locations may be tissues that have the same or similar concentrations of melanin, such as contralateral tissues. For example, during breast reconstruction surgery (e.g., where a tissue flap is being used in reconstruction), the first target tissue may be healthy breast tissue and the second target tissue may be breast tissue. which oximeter reading is desired (e.g., the breast being reconstructed). The first breast tissue may be from the same breast or a different breast or other tissue, such as other breast tissue. The two fabrics must have the same or similar melanin content. The oximeter measurements for the first and second target tissues are then used to generate a relative tissue parameter (e.g., relative oxygen saturation value) that is the difference between a first tissue parameter (e.g., first oxygen saturation ) of the first target tissue (e.g., healthy breast tissue) and a second tissue parameter (e.g., second oxygen saturation) of the second target tissue (e.g., tissue flap being used for breast tissue reconstruction). breast being reconstructed) where contributions from light absorption by melanin are removed from the measurement for relative oxygen saturation.

[00149] As further described below, the method exploits the approximately constant slope of the curve of melanin absorption coefficients in tissue for light having wavelengths of approximately 700 nanometers to approximately 900 nanometers (e.g., infrared wavelength ). The method uses a proposed derivative of absorption coefficients to remove melanin contributions (e.g., from light absorption by melanin) from oximetry measurements and determinations (e.g., final results, intermediate results, or both). See the slope for the melanin absorption coefficients in Figure 8. The method also exploits differences in the slope of the curves for the melanin and oxygenated blood hemoglobin absorption coefficients and the differences in the slope of the curves for the melanin absorption coefficients and deoxygenated blood hemoglobin. See the curves for the absorption coefficients of oxygenated and deoxygenated hemoglobin in Figure 8. Also, as further explained below, the method exploits changes in the slopes of curves for the absorption coefficients for the first and second target tissues where these tissues may have different concentrations of oxygenated and deoxygenated hemoglobin.

[00150] In one implementation of the method, a user contacts the probe tip of the oximeter probe to the first target tissue at a first location (e.g., a location different from the second target tissue) in preparation for using the probe to make an oximeter measurement. See 1500 in Figure 15. Thereafter, the oximeter probe emits light (e.g., 2, 3, 4, or more IR wavelengths) to one or more of the source structures (e.g., two source structures) on the probe face into the first target tissue. Detector structures on the face of the probe detect light subsequent to reflection from or transmission through the first target tissue and generate first reflectance data based on the detected light. The first reflectance data includes a first melanin absorption component of reflectance data for melanin content (e.g., first melanin content for the first target tissue) of the first target tissue. See 1505 in Figure 15.

[00151] The oximeter probe then determines a number of first oximeter parameters for the first target tissue using the first reflectance data for each wavelength of light transmitted from the source structures into the tissue. See 1510 in Figure 15. The first oximeter parameters can be determined by the oximeter probe by fitting the reflectance data to the simulated reflectance curves as described above. The oximeter probe stores these first oximeter parameters in the probe's memory. The first oximeter parameters may be values for the absorption coefficients for each of the wavelengths of light transmitted to the first target tissue. The first oximeter parameters for the first target tissue (e.g., healthy tissue) are baseline parameters. The first oximeter parameters (e.g., intermediate values such as angular measurement, absorption coefficients, oxygen saturation values, other values) may be unavailable for display after the first measurement is made and before a second measurement is made ( for example, described below at 1515, 1520, and 1525 of Figure 15).

[00152] Figures 16A and 16B show exemplary plots of absorption coefficients for the first target tissue and the second target tissue illuminated by a number of wavelengths of light, such as 760 nanometers, 810 nanometers, 845 nanometers, and 895 nanometers. Other wavelengths may be utilized by the oximeter probe including longer or shorter wavelengths of light.

[00153] At 1515, the user moves the probe face of the oximeter probe to the second target tissue (e.g., breast tissue undergoing reconstructive surgery). Subsequently, the oximeter probe emits light (e.g., 2, 3, 4, or more IR wavelengths) from the one or more source structures on the face of the probe into the second target tissue. Detector structures on the face of the probe detect light subsequent to reflectance from or transmission through the second target tissue and generate second reflectance data based on the detected light. The second reflectance data includes a second component of melanin absorption reflectance data for the melanin content (e.g., second melanin content for the first target tissue) of the second target tissue. See 1520 in Figure 15.

[00154] The oximeter probe then determines a number of seconds of oximeter parameters for the second target tissue using the second reflectance data for each wavelength of light transmitted from the source structures into the tissue. See 1525 in Figure 15. The second oximeter parameters can be determined by the oximeter probe by fitting the second reflectance data to the simulated reflectance curves as described above. The oximeter probe can store these second oximeter parameters in the probe's memory. The second oximeter parameters may be values for the absorption coefficients for the wavelengths of light transmitted to the second target tissue.

[00155] At 1530, the oximeter probe determines a first angular deviation θi (see Figure 16A) from the first curve (e.g., lines forming the curves) to first absorption coefficients for line 1605 (e.g., the projection 1605a of line 1605 which is shown as a dashed line in Figure 16A) between 760 nanometers and 810 nanometers and 1410 between 810 nanometers and 845 nanometers.

[00156] The oximeter probe determines a second angular deviation ΦI from the first curve (e.g., lines forming the curves) to the second absorption coefficients for line 1610 (e.g., projection 1610a of line 1610 which is shown as a dashed line in Figure 16A) between 810 nanometers and 845 nanometers and the 1615 line between 845 nanometers and 890 nanometers.

[00157] The oximeter probe determines a third angular deviation ©2 (see Figure 16B) from the second curve (e.g., lines forming the curves) to the second absorption coefficients for line 1620 (e.g., projection 1620a of line 1620 which is shown as a dashed line in Figure 16B) between 760 nanometers and 810 nanometers and line 1625 between 810 nanometers and 845 nanometers.

[00158] The oximeter probe determines a fourth angular deviation Φ2 of the second curve (e.g., lines forming the curves) for the second absorption coefficients for line 1625 (e.g., projection 1625a of line 1625 which is shown as a dashed line in Figure 16B) between 810 nanometers and 845 nanometers and line 1630 between 845 nanometers and 890 nanometers.

[00159] The first and second angular deviations ©1 and Φ1 shown in Figure 16A are calculated by the oximeter probe by taking the second derivative of the first curve for the absorption coefficients with respect to wavelength for the first target tissue (e.g. , healthy breast tissue). The third and fourth angular deviations ©2 and Φ2 shown in Figure 16B are calculated by the oximeter probe by taking the second derivative of the first curve for the absorption coefficients with respect to wavelength for the second target tissue (e.g., tissue of reconstructed breast).

[00160] Figure 17A shows an exemplary curve (e.g., first spectrum) of absorption coefficients for the first target tissue (e.g., healthy breast tissue). The exemplary curve has a negative slope along the entire length of the curve. Figure 17B shows an exemplary curve of the first derivative of the absorption coefficients with respect to wavelength for the first target tissue. The graph in Figure 17B is for wavelengths between 750 and 850. The negative values of the exemplary curve of Figure 17B coincide with the negative slope shown in Figure 17A, and the exemplary curve has a positive slope along the entire length of the curve. Figure 17C shows an exemplary curve of the second derivative of the absorption coefficients with respect to wavelength for the first target location. The graph in Figure 17C is for wavelengths between 800 and 850 nanometers (e.g., specifically for 810 nanometers and 845 nanometers). The positive values of the exemplary curve shown in Figure 17C coincide with the positive slope of the curve in Figure 17B.

[00161] Figure 17D shows an exemplary first curve (e.g., first spectrum) 1701 and a second exemplary curve (e.g., second spectrum) 1703 of the absorption coefficients for the first target tissue (e.g., healthy breast tissue ) and the second target tissue (e.g. reconstructed breast tissue). The relatively small shift of the curves indicates the relatively small change in absorption coefficients between the first target tissue and a second target tissue. The exemplary curves each have a negative slope along the entire length of the curve.

[00162] Figure 17E shows a first exemplary graph 1711 (e.g., three upper points) of the first derivative of the absorption coefficients with respect to wavelength for the first target tissue and shows a second graph 1713 (e.g., three lower points) of the first derivative of the absorption coefficients with respect to wavelength for the second target tissue. The graph in Figure 17E is for wavelengths between 750 and 850. The negative values of the exemplary graphs in Figure 17E coincide with the negative slopes shown in Figure 17D, and the exemplary curves have positive slopes along the entire lengths of the curves.

[00163] Figure 17F shows a first exemplary plot 1721 (e.g., for lower points) of the second derivative of the absorption coefficients with respect to wavelength for the first target location and shows a second exemplary plot 1723 (e.g., for the upper points) of the second derivative of the absorption coefficients with respect to wavelength for the second target location. The graphs in Figure 17D are for wavelengths between 800 and 850 nanometers (e.g., specifically for 810 nanometers and 845 nanometers). The positive values of the exemplary graphs shown in Figure 17F coincide with the positive slope of the curve in Figure 17E.

[00164] Figure 18 shows a vector (θi, Φi) in "angle" space for the values of the second derivatives θi and Φi plotted against each other. In angle space, the vertical and horizontal geometric axes are for θi and Φi values of the second derivatives for two wavelengths of light. In the specific example, the vertical and horizontal geometric axes are for values for the second derivative for 8i0 nanometers and 845 nanometers. Other wavelength values of the second derivatives can be chosen if the tissue is illuminated by other wavelengths of light. That is, the i80ia endpoint of the i80i vector in angle space represents two values for the second derivative for the first tissue (e.g., healthy breast tissue) plotted against each other.

[00165] Figure i9 shows the first vector i90i (θi, Φi) and a second vector i903 (θ2, Φ2) in the "angle" space. That is, θi, and Φi are plotted relative to each other and θ2 and Φ2 are plotted relative to each other. The difference between the two vectors is the delta angles Δθ = θi - θ2 and ΔΦ = Φi - Φ2 and represents the change in curvature of the curves (also sometimes referred to as spectra) for absorption coefficients for the first and second target tissues for the wavelengths 810 and 845. The delta angles Δθ and ΔΦ can be determined by the processor by projecting vector 1903 onto vector 1901. See Figure 15 at 1535 and 1540.

[00166] Figure 20 shows one of the delta angles Δθ and ΔΦ in vector space. The curvature changes of the absorption coefficients are attributable to relative changes in oxygen saturation between the first and second target tissue sites. Because the curvatures for the melanin absorption coefficients are fixed or approximately fixed for the first and second target tissues (e.g., melanin concentrations are the same or similar for the first and second target tissues, single patient with contralateral measurements), the curvature changes of the absorption coefficients Δθ and ΔΦ are not attributable to melanin at tissue sites. That is, any contribution to the second derivatives of melanin goes to zero.

[00167] The relative change in oxygen saturation between the first and second target tissues is calculated from the delta angles Δθ and ΔΦ and a value (e.g. percentage difference) for the relative change in oxygen saturation is displayed on the probe display oximeter. See Figure 15 at 1545 and 1550. The oximeter probe processor performs this calculation. Specifically, the angle changes Δθ and ΔΦ have an arbitrary scaling that is corrected so that the scaling is for blood. The correction can be based on a scaling factor, a correction vector, or both. The scaling factor, correction vector, or both may be stored in non-volatile memory and remain resident in memory when the oximeter probe is detached from a power source (e.g., batteries are removed from the probe). These values can be generated when the oximeter is first manufactured and tested for use. Values are retrieved from memory and loaded into the processor for use. The correction vector can be a vector in angle space used by the processor to correct vectors in angle space or correct angle changes Δθ and ΔΦ in angle space.

[00168] The correction vector is determined using a tissue phantom. The tissue phantom may be a liquid tissue phantom, one or more rigid tissue phantoms, or a combination of liquid and rigid tissue phantoms. The oximeter probe takes oxygen saturation measurements on the tissue phantom during a period of time when the tissue phantom has an initial blood oxygenation saturation of 100 percent (e.g., fully oxygenated) and decreases to 0 percent (e.g., example, fully deoxygenated).

[00169] The reflectance data (e.g., for 2, 3, 4, or more wavelengths of light, such as IR) that are generated by the oximeter probe for the tissue phantom are fitted to the simulated reflectance curves to determine one or more simulated reflectance curves that best fit the reflectance data. The absorption coefficients associated with the one or more simulated reflectance curves are determined. The first and second derivatives of the curves (e.g. spectrum) for the absorption coefficient are determined.

[00170] Figure 21A shows a graph 2100 for the absorption coefficients (e.g., spectrum) for the fully oxygenated measurements and a graph 2105 for the absorption coefficients for the fully deoxygenated measurements. Figure 21B shows a first graph 2110 over the graph for the first derivatives of the fully oxygenated spectrum with respect to wavelength and a second graph 2115 over the graph for the first derivative with respect to wavelength of the fully deoxygenated spectrum. Figure 21C shows a first graph 2120 over a graph for the second derivative with respect to the wavelength of the fully oxygenated spectrum and a second graph 1225 over the graph for the second derivative with respect to the wavelength of the fully deoxygenated spectrum.

[00171] Subsequently, the angular deviations (e.g., θi over Φi) for the curves for the fully oxygenated measurements are determined for the same wavelengths (e.g., angular deviation θ1 between the line from 760 nanometers to 810 nanometers and the 810 nanometer line to 845 nanometers, and the angular deviation Φ1 between the 810 nanometer line to 845 nanometers and the 845 nanometer to 890 nanometer line) as the first and second target tissue measurements described above.

[00172] The angular deviations (e.g., θ2 over Φ2) for the curves for the fully deoxygenated measurement are determined for the same wavelengths (e.g., angular deviation θ2 between the 760 nanometer to 810 nanometer line and the from 810 nanometers to 845 nanometers, and the angular deviation Φ2 between the 810 nanometers to 845 nanometers line and the 845 nanometers to 890 nanometers line) as the first and second target tissue measurements described above.

[00173] Figure 22 shows the vector (Δθ, ΔΦ) in angle space where Δθ and ΔΦ are plotted relative to each other. Delta angles can be used to scale (or calibrate) tissue measurements for the first and second target tissue (e.g., contralateral breast tissue measurements).

[00174] These angular changes Δθ = θ1 - θ2 and ΔΦ = Φ1 - Φ2 are determined by the processor. The delta angle represents the change in curvature of the absorption spectra between the fully deoxygenated measurement and the fully oxygenated measurement. The delta angles Δθ and ΔΦ indicate what a 100 percent change in oxygenation to the tissue is expected to look like and provide a reference that other smaller changes in the delta angles Δθ and ΔΦ (e.g., for contralateral breast tissue) can be corrected to scale. Δθ and ΔΦ arbitrarily scaled (e.g. for contralateral breast tissue).

[00175] The calculated vector (Δθ, ΔΦ) for the tissue phantom is multiplied by a correction factor to correct the difference in blood volume volume in the phantom and the blood volume in the patient's tissue. The correction factor may be 10 or another factor to account for a difference between 10 percent blood volume in the specific phantom used and 1 percent blood volume (or another percentage of blood volume 1.25 percent, 1.2 percent, 1.15 percent, 1.1 percent, 1.05 percent, 0.95 percent, 0.9 percent, 0.85 percent, 0.8 percent, or other values) for the patient's tissue. Alternatively, the correction factor can be applied to the measurements for the patient's tissue compared to the measurements for the phantom.

[00176] Figure 23 shows the baseline corrected vector 2401 and the corrected calculated vector 2403 for the phantom corrected by the scaling factor for the difference in blood volume between the blood volume for the phantom and the patient's tissue. The delta angles Δθ and ΔΦ corrected for blood volume difference indicate what a 100 percent change in oxygenation to the tissue is expected to look like and provides a reference to other smaller changes in delta angles Δθ and ΔΦ for the patient's tissue (e.g. , for contralateral breast tissue) can be corrected to scale Δθ and arbitrary scaled ΔΦ for the patient's tissue.

[00177] In one implementation, the vector for the patient tissue is scaled by the vector for the phantom by projecting the vector for the patient tissue over the vector for the phantom vector. Figure 24 shows vector 2501 for the patient tissue projected over vector 2503 for the phantom. The projection result is identified with the reference number 2505.

[00178] In one implementation, the vector for the patient tissue is scaled by the vector for the phantom (1550 of Figure 15) by dividing the normalized vector for the patient tissue vector normalized for the phantom (e.g., determining a percentage difference ) and multiplying by 100 percent and -1. [0003]

[00179] Factor -1 represents a measurement for a decrease in oxygen saturation of the patient's tissue measured by the oximeter probe. In the example of Figure 24, the relative increase in deoxygenation (e.g., decrease in oxygenation) between the patient's contralateral target tissue is approximately 18%.

[00180] In one implementation, nonlinear transforms are used by the oximeter probe to scale the vector (Δθ, ΔΦ) for the patient's tissue by the vector (Δθ, ΔΦ) for the phantom.

[00181] In one implementation, the oximeter probe transmits light from at least one of the light sources (e.g., source structures) of the oximeter probe into a first tissue (first breast tissue) at a first location a be measured.

[00182] The first tissue comprises a first melanin component, such as a first melanin content. The first component of melanin includes eumelanin, pheomelanin, or both eumelanin, pheomelanin. A number of the detector structures receive light subsequent to transmission through reflectance of the first tissue.

[00183] The received light comprises a first melanin absorption component due to the first melanin component. That is, the received light includes information for the melanin in the first tissue as the melanin absorbs a portion of the light transmitted into the first tissue.

[00184] The oximeter probe subsequently determines a melanin compensation component (e.g., an angle correction (such as θi, ©2, Φi, Φ2, Δθ, ΔΦ, or any combinations thereof), a determined absorption coefficient from fitting reflectance data to simulated reflectance curves, any preliminary calculation result, any, intermediate, any final, or any combination thereof) for a melanin absorption component due to a melanin component of the tissue.

[00185] The melanin absorption component includes the first melanin component. The melanin component includes the first melanin component. The oximeter probe uses the melanin compensation component to obtain a melanin-corrected oxygen saturation value for the first tissue. The melanin-corrected oxygen saturation value takes into account the absorption component of melanin.

[00186] In one implementation, a method includes contacting a probe tip of an oximeter probe on a first target tissue of a patient, wherein the first target tissue is a healthy tissue; use the oximeter probe, making a first oximetry measurement on the first target tissue; determine, by a processor of the oximeter probe a first plurality of absorption coefficients that are dependent on a plurality of wavelengths of light emitted from the oximeter probe into the first target tissue when the measurement on the first target tissue is executed; contacting the probe tip on a second target tissue of the patient, wherein the second target tissue is a tissue for which an oximetry saturation value is to be determined; using the oximeter probe, make a second oximetry measurement on the target tissue; determining, by the processor of the oximeter probe, a second plurality of absorption coefficients that are dependent on the first plurality of wavelengths of light emitted from the oximeter probe into the second target tissue when the measurement on the second target tissue is performed ; calculating, by the processor, a first angular deviation and a second angular deviation of a curve for the first plurality of absorption coefficients for the first target tissue; calculating, by the processor, a third angular deviation and a fourth angular deviation of a curve for the second plurality of absorption coefficients for the second target tissue; calculating, by the processor, a first angular difference between the first and second angular deviations and a second angular difference between the third and fourth angular deviations; calculating, by the processor, a relative change in oxygen saturation between the first and second target tissues based on the first and second angular differences; and displaying, by a display of the oximeter probe, a value for relative oxygen saturation.

[00187] The method may include transmitting a first light from a source structure of the oximeter probe into the first target tissue; detecting a first reflected light that is reflected from the first target tissue by a plurality of detector structures of the oximeter probe; generating by the detector structures first reflectance data for the first reflected light detected by the detector structures; fitting the reflectance data to a plurality of simulated reflectance curves; determining one or more best fits of the simulated reflectance curves from the fit of the first reflectance data to the plurality of simulated reflectance curves, wherein each of the simulated reflectance curves is associated with a value for an absorption coefficient; and determining the first plurality of absorption coefficients for the one or more best fits of the simulated reflectance curves to the first reflectance data.

[00188] The method may include transmitting a second light from the source structure of the oximeter probe into the second target tissue; detecting the second reflected light that is reflected from the second target tissue by the plurality of detector structures of the oximeter probe; generating by the detector structures second reflectance data for the second reflected light detected by the detector structures; fitting the second reflectance data to the plurality of simulated reflectance curves; determining one or more best fits of the simulated reflectance curves from fitting the second reflectance data to the plurality of simulated reflectance curves; and determining the second plurality of absorption coefficients for the one or more best fits of the simulated reflectance curves to the second reflectance data.

[00189] The method may include scaling, by the processor, the first and second angular differences with a scaling vector, where the scaling vector represents a 100 percent difference in oxygenation of a tissue phantom. Scaling includes projecting a first vector comprising data points for the first and second angular differences in angle space onto the scaling vector in angle space. Scaling alternatively includes dividing a normalization of the first vector, which comprises data points for the first and second angular differences in angle space, by a normalization of the scaling vector.

[00190] The method may include calculating, by the processor, a percentage difference of a quotient of the normalization of the first vector divided by the normalization of the scaling vector; and quotient by negative one to include a decrease in oxygenation between the first target tissue and the second target tissue. The value shown on the display is the product of the quotient multiplied by a negative.

[00191] In one implementation, the system implements the method where the system includes an oximeter probe that includes a portable housing; a processor housed within the portable housing; a memory, housed within the portable housing, electronically coupled to the processor and which stores a first code for controlling the processor; a display, accessible from outside the portable housing, electronically coupled to the processor; and a battery, housed within the portable housing, coupled to and supplying power to the processor, the memory, and the display, wherein the code includes an instruction executable by the processor to perform the steps for the method including making a first oximetry measurement on a first target tissue from a patient; determining a first plurality of absorption coefficients that are dependent on a plurality of wavelengths of light emitted from the oximeter probe into the first target tissue when the measurement over the first target tissue is performed; make a second oximetry measurement on a second target tissue of the patient; determining a second plurality of absorption coefficients that are dependent on the first plurality of wavelengths of light emitted from the oximeter probe into the second target tissue when the measurement over the second target tissue is performed; calculating a first angular deviation and a second angular deviation of a curve for the first plurality of absorption coefficients for the first target tissue; calculating a third angular deviation and a fourth angular deviation of a curve for the second plurality of absorption coefficients for the second target tissue; calculating a first angular difference between the first and second angular deviations and a second angular difference between the third and fourth angular deviations; calculating a relative change in oxygen saturation between the first and second target tissues based on the first and second angular differences; and display the value for relative oxygen saturation.

[00192] In one implementation a method includes contacting a probe tip on a first target tissue of a patient, where the first target tissue is a healthy tissue; using the oximeter probe, make a first oximetry measurement on the first target tissue; determining, by a processor of the oximeter probe, a first absorption coefficient based on the first oximetry measurement for the first target tissue; contacting the probe tip to a second target tissue of the patient, wherein the second target tissue is the tissue for which an oximetry saturation value is to be determined; using the oximeter probe, make a second oximetry measurement on the second target tissue; determining, by the oximeter probe processor, a second absorption coefficient that is based on the second oximetry measurement for the second target tissue; generating, by the processor, a third absorption coefficient by adjusting the second absorption coefficient using the first absorption coefficient; determining a value for oxygen saturation for the second target tissue from the third absorption coefficient; and display the value for oxygen saturation for the second target tissue. The method may include fitting the first reflectance data for the first oximetry measurement to a plurality of simulated reflectance curves to determine by the processor the first absorption coefficient based on the first oximetry measurement for the first target tissue, wherein the curves reflectance simulations include modeling for melanin in the simulated tissue; and determining, by the processor, the first absorption coefficient of one or more best fits of the simulated reflectance curves.

[00193] This description of the invention has been presented for the purposes of illustration and description. This is not intended to be exhaustive or to limit the invention to the precise form described, but many modifications and variations are possible in light of the above teaching. The implementations were chosen and described in order to better explain the principles of the invention and its practical applications. This description will enable others skilled in the art to better utilize and practice the invention in various implementations and with various modifications as are suitable for a specific use. The scope of the invention is defined by the following claims.

Claims (20)

1. Method, characterized by the fact that it comprises: transmitting a first light from a light source (120a) of an oximeter probe into a first tissue at a first location to be measured, wherein the first tissue comprises a first component of melanin, and the first melanin component comprises at least one of eumelanin or pheomelanin; receiving first light at a detector (125d) of the oximeter probe that is reflected by the first tissue in response to the first transmitted light, wherein the first received light comprises a first melanin absorption component due to the first melanin component; transmit the second light from the oximeter probe light source (120a) into a second tissue at a second location to be measured, wherein the second location is different from the first location, and the second tissue comprises a second melanin component , and the second melanin component comprises at least one of eumelanin or pheomelanin; receiving the second light at the detector (125d) of the oximeter probe which is reflected by the second tissue in response to the transmitted light, the second received light comprising a second melanin absorption component due to the second melanin component; determining a melanin compensation component for a melanin absorption component due to a melanin component of the tissue, wherein the melanin absorption component comprises the first and second melanin components; and using the melanin compensation component, obtaining a melanin-corrected oxygen saturation value for the first tissue, wherein the melanin-corrected oxygen saturation value takes into account the melanin absorption component. 2. Method according to claim 1, characterized by the fact that it comprises: determining a first plurality of absorption coefficients that are dependent on a plurality of wavelengths of the first light emitted from the oximeter probe into the first tissue when the measurement of the first tissue is performed; determining a second plurality of absorption coefficients that are dependent on the plurality of wavelengths of the second light emitted from the oximeter probe into the second tissue when the measurement of the second tissue is performed; generating a third absorption coefficient by adjusting at least one of the first absorption coefficient using at least one of the absorption coefficients of the second plurality of absorption coefficients; and generating the melanin-corrected oxygen saturation value for the first tissue using the third absorption coefficient. 3. Method according to claim 2, characterized by the fact that it comprises: adjusting the first reflectance data for the first light received in the oximeter probe detector for the first tissue to a plurality of simulated reflectance curves to determine the at least one of the absorption coefficients of the first plurality of absorption coefficients, wherein the simulated reflectance curves include modeling for melanin in the simulated tissue; and determining, by the processor, the at least one of the absorption coefficients of the first plurality of absorption coefficients of one or more of the most suitable of the simulated reflectance curves. 4. Method according to claim 3, characterized by the fact that it comprises: adjusting the second reflectance data for the second light received in the oximeter probe detector for the second tissue to the plurality of simulated reflectance curves to determine the at least one of the absorption coefficients of the second plurality of absorption coefficients; and determining, by the processor, the at least one of the absorption coefficients of the second plurality of absorption coefficients of one or more of the most suitable of the simulated reflectance curves. 5. The method of claim 1, wherein the first location and the second location are contralateral locations on a body. 6. The method of claim 1, comprising: calculating a first angular deviation and a second angular deviation of a curve for the first plurality of absorption coefficients for the first fabric; calculating a third angular deviation and a fourth angular deviation of a curve for the second plurality of absorption coefficients for the second tissue; calculating a first angular difference between the first and second angular deviations and a second angular difference between the third and fourth angular deviations; and calculating a relative change in oxygen saturation between the first and second tissues based on the first and second angular differences. 7. Method according to claim 6, characterized by the fact that it comprises calculating a relative difference in oxygen saturation between the first and second tissues based on the first and second angular differences. 8. Method, characterized by the fact that it comprises: transmitting a first light from a light source (120a) of an oximeter probe into a first tissue at a first location to be measured, wherein the first tissue comprises a first component of melanin, and the first melanin component comprises at least one of eumelanin or pheomelanin; receiving first light at a detector (125a) of the oximeter probe that is reflected by the first tissue in response to the first transmitted light, wherein the first received light comprises a first melanin absorption component due to the first melanin component; determining a melanin compensation component for a melanin absorption component due to a melanin component of the tissue, wherein the melanin absorption component comprises the first and second melanin components, wherein determining the melanin compensation component for the melanin absorption component due to the melanin component comprises: transmitting the second light from the oximeter probe light source (120a) into a second tissue at a second location to be measured, the second location being different of the first location, and the second tissue comprises a second melanin component, and the second melanin component comprises at least one of eumelanin or pheomelanin; receiving the second light at the detector (125a) of the oximeter probe that is reflected by the first tissue in response to the transmitted light, wherein the second received light comprises a second melanin absorption component due to the second melanin component; determining a first plurality of absorption coefficients that are dependent on a plurality of wavelengths of the first light emitted from the oximeter probe into the first tissue when the measurement on the first tissue is performed; and determining a second plurality of absorption coefficients that are dependent on the first plurality of wavelengths of the second light emitted from the oximeter probe into the second tissue when the measurement of the second tissue is performed; and using the melanin compensation component, obtaining a melanin-corrected oxygen saturation value for the first tissue, wherein the melanin-corrected oxygen saturation value takes into account the melanin absorption component. 9. Method according to claim 8, characterized by the fact that determining the melanin compensation component for the melanin absorption component due to the melanin component comprises: calculating a first angular deviation and a second angular deviation of a curve for the first plurality of absorption coefficients for the first fabric; calculating a third angular deviation and a fourth angular deviation of a curve for the second plurality of absorption coefficients for the second tissue; calculating a first angular difference between the first and second angular deviations and a second angular difference between the third and fourth angular deviations; and calculating a relative change in oxygen saturation between the first and second tissues based on the first and second angular differences. 10. The method of claim 8, comprising: adjusting the absorption coefficients of the second plurality of absorption coefficients for each wavelength of the first light using the reflectance data for the first tissue, wherein the melanin compensation component comprises adjusted absorption coefficients; and determining an oxygen saturation value for the second tissue using the adjusted absorption coefficients. 11. Method according to claim 9, characterized by the fact that it comprises displaying, on a display of the oximeter probe, the melanin-corrected oxygen saturation value, wherein the melanin-corrected oxygen saturation value is a value for the relative change in oxygen saturation between the first and second tissues. 12. The method of claim 11, wherein determining the melanin compensation component for the melanin absorption component due to the melanin component comprises scaling the first and second angular differences with a scaling vector, where the scaling vector represents a 100 percent difference in oxygenation of a tissue phantom. 13. Method according to claim 8, characterized by the fact that determining the melanin compensation component for the melanin absorption component due to the melanin component comprises: generating a third absorption coefficient by adjusting at least one of the coefficients of the first plurality of absorption coefficients using at least one of the absorption coefficients of the second plurality of absorption coefficients; and generating the melanin-corrected oxygen saturation value for the first tissue using the third absorption coefficient. 14. The method of claim 13, wherein determining the melanin compensation component for the melanin absorption component due to the melanin component comprises: adjusting the first reflectance data for the first light received at the detector from the oximeter probe to the first tissue to a plurality of simulated reflectance curves to determine the at least one of the absorption coefficients of the first plurality of absorption coefficients, wherein the simulated reflectance curves include modeling for melanin in simulated tissue; and determining, by a processor, the at least one of the absorption coefficients of the first plurality of absorption coefficients of one or more best-fit simulated reflectance curves. 15. The method of claim 14, wherein determining the melanin compensation component for the melanin absorption component due to the melanin component comprises: adjusting the second reflectance data for the second light received at the detector from the oximeter probe to the second tissue to the plurality of simulated reflectance curves to determine the at least one of the absorption coefficients of the second plurality of absorption coefficients; and determining, by the processor, the at least one of the absorption coefficients of the second plurality of absorption coefficients from one or more simulated reflectance curves that best fit. 16. System, characterized by the fact that it comprises: an oximeter device (101) comprising a probe tip (110) comprising source structures (120a-120b) and detector structures (125a-125h) on an end furthest from the device, a processor (116) and a display (115) closest to the probe tip and coupled to the processor (116), wherein the processor (116) of the oximeter device calculates a melanin-corrected oxygen saturation value, and displays the melanin-corrected oxygen saturation value on the display, and the processor (116) of the oximeter device is specially configured to: use the probe tip to make a first measurement and a second measurement to determine the oxygen saturation value corrected in melanin; receiving first information based on the first measurement of a first tissue at a first location when the probe tip (110) is positioned in the first tissue, wherein the melanin-corrected oxygen saturation value is unavailable for display after the first measurement is taken and before the second measurement is taken; receiving second information based on the second measurement of a second tissue at a second location when the probe tip (110) is positioned in the second tissue, wherein the second location is different from the first location; and using the first information and second information to determine the melanin-corrected oxygen saturation value, wherein the melanin-corrected oxygen saturation value takes into account the melanin components of the first tissue and second tissue, and the melanin components comprise eumelanin and pheomelanin. 17. System according to claim 16, characterized by the fact that the first location is at a first body position, the second location at a second body position, and the first position and second position are contralateral with respect to each other . 18. The system of claim 16, wherein the oximeter device is a portable oximeter comprising a power source and an electronic processor housed within a housing that also houses the source structures and detector structures of the probe tip. 19. System according to claim 16, characterized by the fact that the oximeter device comprises a memory (117), and the memory stores first simulated reflectance curves (315) for a first melanin content value, second curves of simulated reflectance for a second melanin content value, and the first melanin content value is different from the second melanin content value. 20. System according to claim 19, characterized by the fact that the oximeter device is specially configured to: based on the first and second information, determine a melanin content value for the first tissue and the second tissue; and using the determined melanin content value to select one of the first simulated reflectance curves or the second simulated reflectance curves stored in memory by comparing the determined melanin content value with respect to the melanin content value associated with each of the curves of simulated reflectance.