JP5954767B2 - Ultrasonic diagnostic apparatus and control program for ultrasonic diagnostic apparatus - Google Patents

Description

  Embodiments described herein relate generally to an ultrasonic diagnostic apparatus and a control program for the ultrasonic diagnostic apparatus.

  As one of imaging methods in an ultrasonic diagnostic apparatus, a CFM (Color Flow Mapping) technique that displays information such as blood flow velocity in a two-dimensional manner in color is known. Blood flow information can be acquired as an ultrasonic color image (ultrasonic color Doppler image) that is a two-dimensional ultrasonic Doppler image by collecting ultrasonic Doppler signals. For this reason, the imaging mode for collecting blood flow information is referred to as a color Doppler mode.

  On the other hand, an imaging mode for collecting ultrasonic morphological images of organs and organs is referred to as a B mode. Normally, blood flow information acquired in the color Doppler mode is displayed superimposed on a B-mode image that is an ultrasonic form image.

  In the color Doppler mode, when displaying the blood flow velocity, a singular point may occur as a different color or blackout from the surroundings. The appearance of such singular points is due to noise. Therefore, a median filter for removing noise has been devised.

JP-A-2005-204725

  In the color Doppler mode, it is desired to remove singular points by further reducing noise. In particular, noise may not be sufficiently removed even when a conventionally proposed median filter is used. For example, even when noise removal processing is performed using a conventional median filter when generating an image showing the blood flow velocity, singular points such as black spots, bright spots with different colors from the surroundings, dark spots, different sign points, etc. May not be sufficiently removed. For this reason, improvement of the image quality of an ultrasonic color image is desired.

  Accordingly, an object of the present invention is to provide an ultrasonic diagnostic apparatus and an ultrasonic diagnostic apparatus control program capable of reducing noise and displaying an ultrasonic color image with better image quality.

An ultrasonic diagnostic apparatus according to an embodiment of the present invention includes a scanning unit, a speed calculation unit, a correction unit, and an image generation unit. The scanning means transmits / receives ultrasonic waves to / from the subject and collects a plurality of blood flow Doppler signals as complex signals. The velocity calculation means obtains the velocity of the blood flow based on the blood flow Doppler signal. The correction means is a vector calculation for calculating the velocity of the blood flow at a plurality of spatially different points in units of a plurality of vectors on a complex plane and the velocity of each blood flow from the average velocity of the blood flow. The blood flow velocity is corrected along with the correction of the difference velocity, which is the difference . The image generation means generates blood flow image data based on the corrected blood flow velocity.
The ultrasonic diagnostic apparatus according to the embodiment of the present invention includes a scanning unit, a speed calculation unit, a correction unit, and an image generation unit. The scanning means transmits / receives ultrasonic waves to / from the subject and collects a plurality of blood flow Doppler signals as complex signals. The velocity calculation means obtains the velocity of the blood flow based on the blood flow Doppler signal. The correcting means corrects the blood flow velocity together with a vector calculation for calculating the velocity of the blood flow in units of a plurality of vectors on the complex plane, and a return correction of the velocity. The image generation means generates blood flow image data based on the corrected blood flow velocity. The correction means represents each blood flow velocity at a plurality of sample points included in a predetermined range as a plurality of vectors on a complex plane, and the blood flow velocity corresponding to a vector obtained by averaging the plurality of vectors A mean speed calculation means for obtaining the mean speed, and a folding correction for making each differential speed between the mean speed and each speed of the blood flow at the plurality of sample points be within the range of the folding speed. And the blood flow velocity at the sample point of interest is corrected using the blood flow velocity at the sample point corresponding to the differential velocity extracted from each differential velocity after the folding correction.
In addition, the control program for the ultrasonic diagnostic apparatus according to the embodiment of the present invention causes the ultrasonic diagnostic apparatus to function as the scanning unit, the speed calculating unit, the correcting unit, and the image generating unit.

1 is a configuration diagram of an ultrasonic diagnostic apparatus according to an embodiment of the present invention. The detailed block diagram of the blood-flow information calculator shown in FIG. The figure which shows the complex plane showing the velocity V of the blood flow used as calculation object in the speed / dispersion / power calculator shown in FIG. The figure which shows the example which allocated the blood flow velocity V calculated in the velocity / dispersion / power calculator shown in FIG. 2 to the color bar. The figure which shows the example of the vector velocity of the blood flow used as the process target in the vector velocity averager shown in FIG. The figure which shows the example of a setting of the sample point used as the calculation object of average value vector Vmean_vector in the vector velocity averager shown in FIG. The flowchart which shows the flow at the time of collecting and displaying the blood-flow image of a subject on the B-mode image by the ultrasonic diagnostic apparatus shown in FIG. The figure which shows the 1st example of the velocity distribution of the blood flow in each sample point adjacent to the center sample point shown in FIG. The figure which shows the 2nd example of the velocity distribution of the blood flow in each sample point adjacent to the center sample point shown in FIG. The figure which shows the 3rd example of the velocity distribution of the blood flow in each sample point adjacent to the center sample point shown in FIG. The figure which shows the 4th example of the velocity distribution of the blood flow in each sample point adjacent to the center sample point shown in FIG.

  An ultrasonic diagnostic apparatus and a control program for the ultrasonic diagnostic apparatus according to an embodiment of the present invention will be described with reference to the accompanying drawings.

  FIG. 1 is a configuration diagram of an ultrasonic diagnostic apparatus according to an embodiment of the present invention.

  The ultrasonic diagnostic apparatus 1 includes a transmission system 2, an ultrasonic probe 3, a reception system 4, a B-mode processing system 5, a quadrature detector 6, a CFM processing system 7, a scan converter 8, and a display system 9. The CFM processing system 7 includes an MTI (moving target indication) filter 10, an autocorrelator 11, and a blood flow information calculator 12.

  Each component of the ultrasonic diagnostic apparatus 1 can be configured using a circuit. However, components that process digital information can be constructed by causing a computer to read a control program of the ultrasonic diagnostic apparatus 1. For this purpose, the control program of the ultrasonic diagnostic apparatus 1 can be recorded on an information recording medium and distributed as a program product.

  The ultrasonic probe 3 includes a plurality of ultrasonic transducers. Each ultrasonic transducer has a function of converting an electrical signal applied from the transmission system 2 with a predetermined transmission delay time into an ultrasonic transmission signal and transmitting it to the inside of the subject, and an ultrasonic signal in the subject. A function of receiving an ultrasonic reflection echo signal generated by the reflection of the signal and converting it into an electric signal. A reception signal output from each ultrasonic transducer is input to the reception system 4.

  The ultrasonic probe 3 includes a two-dimensional (2D) scanning probe and a three-dimensional (3D) scanning probe. There are 2D scanning probes such as a sector type, a convex type, and a linear type. Further, as a probe for 3D scanning, a mechanical four-dimensional (4D) probe and a 2D array probe are known. The mechanical 4D probe is a probe for performing 3D scanning by swinging a one-dimensional (1D) array in a direction intersecting the array.

  Here, a case where a 2D ultrasound image is generated by performing 2D scanning will be described, but the same applies to a case where a 2D ultrasound image or a 3D ultrasound image is generated by performing 3D scanning.

  The transmission system 2 is a circuit that drives the ultrasonic probe 3. The transmission system 2 has a function of applying an electrical signal as a transmission signal to each ultrasonic transducer provided in the ultrasonic probe 3. Further, when the transmission system 2 gives a predetermined transmission delay time to the transmission signal for each ultrasonic transducer, transmission directivity is generated, and transmission beamforming can be performed.

  The reception system 4 receives reception signals output from the respective ultrasonic transducers provided in the ultrasonic probe 3 and performs signal processing including amplification processing, A / D (Analog to Digital) conversion processing, and phasing addition processing. , And a function of generating a reception signal from a scanning position having reception directivity. That is, signal processing including reception beamforming is executed in the reception system 4.

  The B-mode processing system 5 acquires the ultrasonic reception signals collected for the B-mode image data, which is ultrasonic morphological image data, from the reception system 4 and executes known signal processing to obtain the B-mode image data. It has a function of generating ultrasonic B-mode data.

  The quadrature detector 6 has a function of acquiring the ultrasonic reception signal collected for the ultrasonic color image data from the reception system 4 and acquiring the ultrasonic Doppler signal. The ultrasonic reception signals for ultrasonic color image data are a plurality of ultrasonic reception signals collected from scanning lines in the same direction in the subject. Then, the quadrature detector 6 performs quadrature detection on a plurality of ultrasonic reception signals to generate an ultrasonic Doppler signal composed of time-series data of complex signals at each point (scanning point, sample position) in space. It has a function.

  The CFM processing system 7 has a function of generating ultrasonic color data including blood flow information such as blood flow velocity, dispersion, and power by executing signal processing on the ultrasonic Doppler signal.

  The MTI filter 10 of the CFM processing system 7 is a filter for obtaining a blood flow Doppler signal by removing clutter components other than the signal due to the blood flow component from the ultrasonic Doppler signal. When N time-series ultrasonic Doppler signals are collected from the same scanning point, the blood flow Doppler signal is obtained by removing M clutter component signals from the M complex numbers corresponding to the scanning point. It becomes time series data. However, M ≦ N.

The autocorrelator 11 has a function of obtaining a complex autocorrelation of a blood flow Doppler signal composed of complex time series data. M complex blood flow Doppler signals z _i (i = 1,..., M) at each scanning point in the scanning region can be expressed using an imaginary unit j as shown in Equation (1).
z _i = x _i + jy _i (1)

Further, the complex autocorrelation C (1) can be calculated from the blood flow Doppler signal z _i represented by the expression (1) according to the expression (2).
However, in formula (2), 1 in C (1) represents the subscript _{1 of} z _{i + 1} ^* .

The blood flow information calculator 12 has a function of calculating ultrasonic color data such as blood flow velocity, dispersion, and power at each scanning point in the scanning region based on the complex autocorrelation of the blood flow Doppler signal. The calculated ultrasonic color data is output to the scan converter 8 for generation of ultrasonic color image data.
FIG. 2 is a detailed configuration diagram of the blood flow information calculator 12 shown in FIG.

  The blood flow information calculator 12 includes a velocity / dispersion / power calculator 20, a blanking processor 21, a vector velocity averager 22, an average velocity calculator 23, a differential velocity calculator 24, and a replacement filter 25.

  The velocity / dispersion / power calculator 20 calculates blood flow velocity, variance, and power at each sample position based on the complex autocorrelation of the blood flow Doppler signal, and blood flow velocity data, blood flow power data, and blood flow variance. It has a function to generate data.

  FIG. 3 is a diagram showing a complex plane representing the blood flow velocity V to be calculated in the velocity / dispersion / power calculator 20 shown in FIG.

In FIG. 3, the vertical axis indicates the imaginary axis, and the horizontal axis indicates the real axis. As shown in FIG. 3, the complex autocorrelation C (1) of the blood flow Doppler signal can be expressed as a vector on the complex plane. The blood flow velocity V is proportional to the frequency of the blood flow Doppler signal z _i and is a vector angle indicating the complex autocorrelation C (1). Therefore, the blood flow velocity V can be calculated from the complex autocorrelation C (1) by the equation (3) when the coefficient is omitted.
V = atan [Im {C (1)} / Re {C (1)}] (-π ≦ V <π) (3)
In Equation (3), Im () is a function that outputs an imaginary part, and Re () is a function that outputs a real part. In addition, −π and π shown in Expression (3) are folding speeds corresponding to the folding frequency of the blood flow Doppler frequency.

  FIG. 4 is a diagram showing an example in which the blood flow velocity V calculated by the velocity / dispersion / power calculator 20 shown in FIG. 2 is assigned to the color bar.

  In FIG. 4A, the vertical axis indicates the imaginary axis, and the horizontal axis indicates the real axis. FIG. 4A shows five blood flow velocities V0. V1, V2, V3, and V4 under different conditions. FIG. 4B shows an example of color bars assigned when displaying the blood flow velocities V0.V1, V2, V3, V4 shown in FIG. 4A as ultrasonic color images.

  As shown in FIG. 4 (A), the range that the blood flow velocity V can take is from 0 to ± π. In other words, ± π is the folding speed. Therefore, as shown in FIG. 4B, the upper limit of the color bar can be + π, the lower limit can be −π, and the center can be zero.

  Further, in the velocity range between 0 and + π, the blood flow velocity V is a positive value, and this velocity range represents a velocity range in which the blood flow approaches the ultrasonic probe 3. On the other hand, in the velocity range between 0 and −π, the blood flow velocity V has a negative value, and this velocity range represents a velocity range in which the blood flow moves away from the ultrasonic probe 3.

  The velocity V0 corresponds to the case where the vector is directed to the positive electrode side on the real axis. That is, the speed V0 corresponds to zero speed. Therefore, the color corresponding to the center of the color bar is assigned to the speed V0.

  The velocity V1 corresponds to a state in which the imaginary part of the vector is inclined with a negative value and is turned to the negative side on the real axis. On the other hand, the velocity V4 corresponds to a state in which the imaginary part of the vector is inclined with a positive value and is turned to the negative side on the real axis. That is, the speed V1 is the folding speed −π, and the speed V4 is the folding speed + π. Therefore, the color corresponding to the lower limit of the color bar is assigned to the speed V1. On the other hand, the color corresponding to the upper limit of the color bar is assigned to the speed V4.

  The speed V3 corresponds to a state in which the speed range is between 0 and + π. On the other hand, the velocity V2 corresponds to a state in which the velocity range is between 0 and −π. Therefore, a color corresponding to a value between the center and the upper limit of the color bar is assigned to the speed V3. On the other hand, a color corresponding to a value between the center and the lower limit of the color bar is assigned to the speed V2.

  By assigning the blood flow velocity V to the color bar in this manner, it is possible to generate data for displaying a blood flow velocity image that displays the blood flow velocity V in color.

The blood flow power C (0) can be calculated from the blood flow Doppler signal z _{i according} to the equation (4).

Further, if the coefficient is omitted, the blood flow variance σ ² can be calculated from the complex autocorrelation C (1) of the blood flow Doppler signal z _i and the blood flow power C (0) by Equation (5).
σ ² = 1- | C (1) | / C (0) (0 ≤ σ ² ≤ 1) (5)

Therefore, the blood flow power image display data and the blood flow dispersion image display data for color display can be generated for the blood flow power C (0) and the variance σ ² , respectively.

  The blanking processor 21 has a function of performing a blanking process for reducing noise in blood flow data such as blood flow velocity data, blood flow power data, and blood flow dispersion data. The blanking process includes a process of blanking a sample point exhibiting a speed below a certain threshold as a clutter, a process of blanking a sample point exhibiting a power below a certain threshold as noise, and the like.

  However, there is noise that cannot be removed even by blanking processing. That is, due to the influence of speckle or the like, a singular point having a value different from the signal value in the surrounding blood flow region remains in the blood flow data. When the remaining singular point is displayed, it is displayed as a black spot or a point having a luminance different from that of the surrounding area.

  The vector velocity averager 22, the average velocity calculator 23, the differential velocity calculator 24, and the replacement filter 25 of the blood flow information calculator 12 remove blood flow velocity data by removing specific noise remaining by blanking processing. Has the function of improving quality. Accordingly, the blood flow velocity data of each sample point that is spatially associated with a two-dimensional position is the target of noise removal processing.

  The vector velocity averager 22 has a function of obtaining an average value of a plurality of vectors indicating the blood flow velocity V at a plurality of sample points within a predetermined range on a complex plane as a vector by an operation using the plurality of vectors. .

  FIG. 5 is a diagram showing an example of the vector velocity of blood flow to be processed in the vector velocity averager 22 shown in FIG.

5A and 5B, the vertical axis represents the imaginary axis, and the horizontal axis represents the real axis. FIG. 5A is a diagram showing the complex autocorrelation C (1) of the blood flow Doppler signal z _i at five sample points as five vectors on the complex plane.

  The velocity of blood flow is determined by the vectors V1vector, V2vector, V3vector, .., whose elements are the real part Re {C (1)} and the imaginary part Im {C (1)} of the complex autocorrelation C (1) at each sample point. ., V5vector. Hereinafter, a vector indicating the speed on the complex plane is referred to as a vector speed, and the length of the vector speed is referred to as a vector speed magnitude.

  In addition, instead of Re {C (1)} and Im {C (1)}, vector velocities V1'vector, V2 'having cosine cos (V) and sine sin (V) of blood flow velocity V as elements. You can also express the velocity of blood flow using vector, V3'vector, ..., V5'vector. FIG. 5B shows vector velocities V1'vector, V2'vector, V3'vector, ..., V5'vector whose elements are cosine cos (V) and sine sin (V) of blood flow velocity V. An example illustrating the velocity of blood flow is shown.

  As shown in FIG. 5 (A), vector velocities V1vector, V2vector, V3vector,... With the real part Re {C (1)} and the imaginary part Im {C (1)} of the complex autocorrelation C (1) as elements. .., V5vector changes in size according to signal strength. On the other hand, vector velocities V1′vector, V2′vector, V3′vector,... Having cosine cos (V) and sine sin (V) of blood flow velocity V shown in FIG. , V5'vector is constant regardless of signal strength. In the following explanation, the vector velocities V1vector, V2vector, V3vector, ..., V5vector whose elements are the real part Re {C (1)} and the imaginary part Im {C (1)} of the complex autocorrelation C (1) An example in which the velocity of blood flow is expressed will be described.

  In this case, the vector velocity averager 22 obtains the average value vector Vmean_vector by taking the average of the vector velocities V1, V2, V3,... At a plurality of sample points.

  FIG. 6 is a diagram illustrating a setting example of sample points that are the calculation target of the average value vector Vmean_vector in the vector velocity averager 22 illustrated in FIG. 2.

  As shown in FIG. 6, nine sample points adjacent to each other on a 3 × 3 matrix can be set as the calculation target of the average value vector Vmean_vector. In this case, the average value vector Vmean_vector is calculated by calculating the average of each element of the vector velocities V1vector, V2 vector, V3vector,..., V9vector at nine sample points.

  In the example shown in FIG. 6, the vector velocity V5vector at the center sample point of the matrix and the vector velocities V1vector, V2vector, V3vector, V4vector, V6vector, V7vector, V8vector, and 8 sample points adjacent to the center sample point of the matrix, respectively. An average value vector Vmean_vector is calculated from V9vector.

  Then, it is preferable that the blood flow velocity V5 as the scalar quantity at the center sample point is the noise removal processing target. For this purpose, the vector velocity V1vector, V2 vector, V3vector,..., V9vector of the blood flow at the center sample point and the sample points adjacent to the center sample point are used.

  However, the blood flow velocities V1, V2, V3, V4, V6, V7, V8, and V9 at any sample point that is not the center may be subject to noise removal processing. In addition, a plurality of sample points on a matrix other than the 3 × 3 matrix may be a target for calculating the average value.

  Furthermore, not only the sample points included in the plurality of sample points that are the calculation target of the average value vector Vmean_vector, but also the sample points not included in the plurality of sample points can be targeted for noise removal. When sample points that are not included in the multiple sample points that are subject to calculation of the average value vector Vmean_vector are to be subject to noise removal, sample points in the vicinity of the multiple sample points that are subject to calculation of the average value vector Vmean_vector It is realistic to set the noise removal target from the viewpoint of maintaining the continuity of the blood flow velocity data.

  For example, in FIG. 6, the blood flow velocity V5 at the center sample point is the target of noise removal processing, and the blood flow vector velocity V1vector, V2vector, V3vector, V4vector, V6vector, V7vector, V8vector, V9vector at the adjacent sample points This corresponds to the case where the average value vector Vmean_vector is calculated. In addition, the vector velocities V1vector, V2vector, V3vector, ..., V9vector of blood flow at each sample point in the 3x3 matrix are used as the calculation target of the mean value vector Vmean_vector, and at the sample points outside the 3x3 matrix This is also the case when the blood flow velocity is the target of noise removal processing.

  On the other hand, as shown in FIG. 6, if the blood flow velocity V5 at the center sample point is the target of noise removal processing, the blood flow vector velocity V1vector, V2vector, V3vector, V4vector, V6vector, V7vector, Isotropic noise removal can be performed using V8vector and V9vector. Therefore, hereinafter, a case where the blood flow velocity V5 at the central sample point shown in FIG.

  The average velocity calculator 23 has a function of obtaining the arc tangent of the ratio of the imaginary part to the real part of the average value vector Vmean_vector as the average blood flow velocity Vmean at a plurality of sample points. This calculation is the same as Expression (3), and the average blood flow velocity Vmean is obtained as a scalar quantity.

  The difference velocity calculator 24 calculates the difference velocity V1- between the velocity V1, V2, V3,..., V9 as the scalar quantity at each sample point for which the average value vector Vmean_vector is calculated and the blood flow average velocity Vmean. A function to calculate Vmean, V2-Vmean, V3-Vmean, ..., V9-Vmean, and a loopback correction of differential speed V1-Vmean, V2-Vmean, V3-Vmean, ..., V9-Vmean With functions.

The loopback correction is a correction that makes the differential speeds V1-Vmean, V2-Vmean, V3-Vmean,..., V9-Vmean fall within the range of the loopback speed. Therefore, the aliasing correction can be performed by the algorithm shown in Equation (6).
Vi-Vmean = Vi-Vmean + 2π: Vi-Vmean <-π
= Vi-Vmean: -π ≤ Vi-Vmean <π
= Vi-Vmean-2π: Vi-Vmean ≧ π
i = 1, 2, 3, ..., 9
(6)

  That is, when the differential velocity Vi-Vmean is smaller than -π, the differential velocity Vi-Vmean + 2π obtained by adding 2π is set as a new differential velocity, and when the differential velocity Vi-Vmean is + π or more, The difference velocity Vi-Vmean-2π obtained by subtracting 2π is set as a new difference velocity. By such folding correction, a differential velocity Vi-Vmean satisfying −π ≦ Vi−Vmean <π can be obtained. The differential speed Vi-Vmean corrected by the equation (6) is hereinafter referred to as Vi #.

  When the loopback correction process is completed, the differential speed Vi # after the loopback correction and the speed Vi before the differential process are associated with each other and given from the differential speed calculator 24 to the replacement filter 25.

  The replacement filter 25 has a function to extract a plausible velocity Vp that is not a singular point and is consistent with the surrounding sample points from the velocity Vi at each sample point, and a sample to be subjected to noise removal processing on the extracted velocity Vp. It has the function of replacing the velocity at the position.

  Therefore, as shown in FIG. 6, if the speed V5 at the center is the speed to be subjected to the noise removal process, the replacement filter 25 is a signal filter that replaces the extracted likely speed Vp with the speed V5 at the center. Further, by selecting a sample point to be replaced with the velocity Vp, it is possible to perform velocity noise removal processing at an arbitrary sample point.

  Probable speed Vp is extracted by arranging differential speeds Vi # after loopback correction in order of magnitude and regarding speed Vi before differential processing corresponding to differential speed Vi # in a predetermined order as probable speed Vp. be able to.

  The predetermined order for extracting the probable differential velocity Vi # can be arbitrarily determined. However, it is most preferable to set the order corresponding to the median value to the predetermined order from the viewpoint of avoiding that the singular value is extracted as the likely speed Vp. Further, it is considered that a singular value can be prevented from being sufficiently extracted even if the order around the median is set as the predetermined order.

  Therefore, it is desirable to extract the median value or the differential velocity Vi # near the median from each differential velocity Vi # after the loopback correction as a probable differential velocity Vi #. For example, as shown in FIG. 6, when the probable speed Vp is obtained based on the differential speed Vi # at nine sample points, the fourth, fifth, or sixth is a desirable order as the predetermined order.

  With such an order setting, the blood flow velocity at the sample point that is the target of the noise removal process is replaced with the blood flow velocity Vp at the sample point corresponding to the probable differential velocity Vi #. As a result, even if the velocity at the sample point that is the target of the noise removal process is a singular value, it can be corrected to a certain velocity Vp that is consistent with the velocity Vi at the adjacent sample point.

  If the original speed before replacement at the sample point that is the target of noise removal, such as the central sample point, is not a singular value and is consistent with the speed Vi at the adjacent sample point, the replacement process is not performed. It is desirable to adopt it as blood flow velocity data as it is.

  Therefore, if the pre-replacement speed at the sample point to be denoised is not a singular value and is an appropriate value that is consistent with the speed Vi at the adjacent sample point, the replacement is not performed. The filter 25 can be configured.

It can be determined by threshold processing whether or not the speed before replacement at the sample point to be denoised is an appropriate value. Here, a specific example will be described in which the sample point to be subjected to noise removal is the center sample point. That is, an appropriate threshold Vt is set, and if the difference | V5 # -Vp # | between the differential velocity V5 # at the center sample point and the likely differential velocity Vp # is smaller than the threshold Vt, the velocity V5 before replacement is It can be determined that the value is appropriate. In this case, the replacement process of the speed V5 can be expressed as in Expression (7).
V5 = V5: | V5 # -Vp # | <Vt
Vp: | V5 # -Vp # | ≧ Vt
(7)
The threshold value Vt can be determined to an appropriate value empirically or by simulation or the like.

  A sample point corresponding to a probable differential velocity only when the blood flow velocity at the target sample point is determined to be not an appropriate value by the velocity value substitution process including the decision shown in Equation (7) Can be replaced with blood flow velocity Vp. As a result, it is possible to suppress blurring of the image that may occur due to the replacement process.

  As another specific example, it is possible to determine whether or not the speed V5 before replacement is an appropriate value by threshold processing for the differential speed | V5 # | at the center sample point.

  As a result, the blood flow velocity at the target sample point is replaced with a probable blood flow velocity at another sample point only when it is determined that it is not an appropriate value. That is, when the blood flow velocity at the target sample point is a singular value, it is corrected to a consistent value.

  Then, the blood flow velocity at all the sample points can be corrected by sequentially changing the sample points to be subjected to noise removal together with a predetermined range including a plurality of sample points as shown in FIG. That is, the singular value of the velocity value can be corrected to a consistent velocity value for all sample points.

  In particular, the average blood flow velocity Vmean is calculated from the components of the average value vector Vmean_vector obtained by calculation between vectors. For this reason, it is not affected by turning back. In addition, even if a singular value exists, it is hardly affected by the singular value. Therefore, the average blood flow velocity Vmean is a value at the approximate center of the distribution in consideration of the continuity at the turning points of the blood flow velocity V1, V2, V3,. As a result, singular points such as black spots and bright spots can be effectively removed, and the data quality of blood flow velocity data can be improved.

  However, in a special case such as when turbulent flow or jet is generated in the blood flow, the blood flow velocity at the target sample point may be replaced with zero velocity corresponding to the blackout of the image. Therefore, a function for detecting a predetermined state such as a state in which turbulent flow or jet is generated in the replacement filter 25 and, when a predetermined state is detected, the velocity of the blood flow at the target sample point is a singular value. It is possible to provide a function for executing error processing so as not to be replaced by the.

  When the blood flow velocity at all the sample points in the target range is corrected by the replacement filter 25, the ultrasonic blood flow velocity data is output from the blood flow information calculator 12 to the scan converter 8 as the corrected blood flow velocity data. . The ultrasonic blood flow dispersion data and the ultrasonic blood flow power data obtained by blanking the blood flow dispersion data and the blood flow power data at each sample point are also converted from the blood flow information calculator 12 to the scan converter 8. Is output.

  The scan converter 8 converts each ultrasonic data output from the B-mode processing system 5 and the CFM processing system 7 from scan format data to television format data, and combines the combined ultrasonic image data. Is output to the display system 9. That is, the scan converter 8 performs coordinate conversion processing of the ultrasound color data and ultrasound B-mode data to generate ultrasound color image data and B-mode image data, and synthesizes them, thereby rendering the form of the subject. It has a function of generating ultrasound image data in which ultrasound color image data such as blood flow velocity image data, velocity dispersion image data, or power image data is superimposed on B-mode image data.

  The display system 9 includes a monitor, and has a function of performing RGB (Red, Green, Blue) conversion of the ultrasonic image data generated by the scan converter 8 and displaying the converted data on the monitor. For the ultrasonic color image data, for example, color image display data can be generated by performing RGB conversion using a color bar as shown in FIG. 4B. On the other hand, B-mode image data is normally RGB-converted in gray scale. As a result, it is possible to superimpose and display a blood flow image in color on a B-mode image that is displayed with luminance using a gray scale.

  In the above-described example, the case where the noise removal process is performed on the blood flow velocity data before the scan conversion by the scan converter 8 has been described. However, the noise removal is performed on the blood flow velocity image data in the television format after the scan conversion. Processing may be performed. In this case, the vector velocity averager 22 obtains the blood flow velocity image data in the television format from the scan converter 8 and calculates the average vector Vmean_vector from the vector velocities V1vector, V2 vector, V3vector, ..., V9vector at each sample point. Is configured to calculate

  The ultrasonic diagnostic apparatus 1 having such a configuration includes a scanning unit that transmits and receives ultrasonic waves to a subject and collects a plurality of blood flow Doppler signals as complex signals, and the blood flow velocity based on the blood flow Doppler signals. Velocity calculation means to be obtained, vector calculation for calculating the velocity of blood flow in units of a plurality of vectors on the complex plane, and correction means for correcting the blood flow velocity with correction of velocity folding, and the corrected blood flow velocity A function as image generation means for generating blood flow image data based on the image data is provided.

  In the example described above, the transmission system 2, the ultrasonic probe 3, the reception system 4, the quadrature detector 6, and the MTI filter 10 of the CFM processing system 7 function as a scanning unit of the ultrasonic diagnostic apparatus 1. . The speed / dispersion / power calculator 20 in the autocorrelator 11 and the blood flow information calculator 12 functions as a speed calculator, while the vector speed averager 22, the average speed calculator 23, the differential speed calculator 24, and the replacement The filter 25 cooperates to function as correction means. Further, the scan converter 8 functions as an image generation unit.

  Further, the correction means represents the blood flow velocities at a plurality of sample points included in a predetermined range as a plurality of vectors on the complex plane, and the blood flow corresponding to the vector obtained by averaging the plurality of vectors. Mean speed calculating means for obtaining the speed as the average speed can be provided. In the above-described example, the vector speed averager 22 and the average speed calculator 23 cooperate to function as average speed calculation means.

  Then, the correction means performs folding correction so that each differential velocity between the average velocity and each velocity of blood flow at a plurality of sample points is within the range of the folding velocity, and each differential velocity after the folding correction is performed. The blood flow velocity at the sample point of interest can be corrected using the blood flow velocity at the sample point corresponding to the likely differential velocity extracted from.

  If the ultrasonic diagnostic apparatus is provided with functions as a scanning unit, a speed calculating unit, a correcting unit, and an image generating unit, the ultrasonic diagnostic apparatus 1 can be configured with other components. In addition, by causing the computer to read the control program of the ultrasonic diagnostic apparatus 1, the ultrasonic diagnostic apparatus 1 can function as a scanning unit, a speed calculating unit, a correcting unit, and an image generating unit.

  Next, the operation and action of the ultrasonic diagnostic apparatus 1 will be described.

  FIG. 7 is a flowchart showing a flow when the blood flow image of the subject is collected and displayed on the B-mode image by the ultrasonic diagnostic apparatus 1 shown in FIG.

  First, in step S1, ultrasonic signals are collected by transmitting and receiving ultrasonic waves to and from the subject. That is, a scan is performed in which an ultrasonic signal for a B-mode image and an ultrasonic signal for a color Doppler blood flow image are collected in parallel.

  More specifically, the transmission system 2 applies a transmission signal to each ultrasonic transducer provided in the ultrasonic probe 3. For this reason, an ultrasonic transmission signal is transmitted from each ultrasonic transducer of the ultrasonic probe 3 in the scanning direction inside the subject. Then, an ultrasonic reflected echo signal generated by the reflection of the ultrasonic signal in the subject is received by each ultrasonic transducer. The ultrasonic reflected echo signal received by each ultrasonic transducer is output to the reception system 4 as a reception signal of an electrical signal. Then, in the reception system 4, a reception signal from the scanning direction having reception directivity is generated by reception beam forming.

  Subsequently, ultrasonic signals from each scanning direction are sequentially collected as reception signals in the same flow. In addition, an ultrasonic signal for a color Doppler blood flow image is collected a plurality of times from the same scanning direction in order to obtain an ultrasonic Doppler signal. For this reason, the ultrasonic reception signal for color Doppler blood flow images is reception data composed of a plurality of signals.

  The ultrasonic reception signal for the B mode image is output to the B mode processing system 5. Then, the B-mode processing system 5 generates B-mode image ultrasonic B-mode data. On the other hand, the ultrasonic reception signal for the color Doppler image is output to the quadrature detector 6.

  Next, in step S2, the quadrature detector 6 performs quadrature detection on a plurality of ultrasonic reception signals for color Doppler images and sequentially generates time-series ultrasonic Doppler complex signals at each sample position in space. To do. The generated ultrasonic Doppler signal is output to the CFM processing system 7. Then, in the MTI filter 10, the clutter component is cut from the ultrasonic Doppler signal. Thereby, a blood flow Doppler signal is generated.

  Next, in step S3, the velocity, variance, and power of the blood flow at each sample position are calculated based on the blood flow Doppler signal. For this purpose, the autocorrelator 11 obtains a complex autocorrelation of a blood flow Doppler signal composed of complex time series data according to the equation (2). Subsequently, the velocity / dispersion / power calculator 20 of the blood flow information calculator 12 calculates each sample according to equations (3), (4), and (5) based on the complex autocorrelation of the blood flow Doppler signal. The blood flow velocity, power, and dispersion at the position are calculated, and blood flow velocity data, blood flow power data, and blood flow dispersion data are generated.

  Next, in step S4, the blanking processor 21 performs a blanking process for reducing noise in blood flow velocity data, blood flow power data, and blood flow dispersion data. However, noise remains even after the blanking process. In particular, the blood flow velocity is different from the dispersion and power, and therefore, a process different from the dispersion and power is required.

  Therefore, a noise correction process in the blood flow velocity data remaining by the blanking process is executed. Here, as shown in FIG. 6, the case where the noise removal processing of the blood flow velocity V5 at the central sample point is performed based on the blood flow velocity V1, V2, V3,..., V9 at the nine sample points. Explained as an example.

  Specifically, in step S5, the vector velocity averager 22 displays a plurality of vector velocities V1vector, V2 vector, V3vector indicating the blood flow velocity V1, V2, V3,..., V9 at each sample point on the complex plane. , ..., find the average value of V9vector as the average value vector Vmean_vector.

  Next, in step S6, the average velocity calculator 23 obtains the arc tangent of the average value vector Vmean_vector as the average velocity Vmean of blood flow at a plurality of sample points.

  Next, in step S7, the differential velocity calculator 24 calculates the differential velocity V1-Vmean, V2-Vmean, V3 between the velocity V1, V2, V3,..., V9 and the average blood flow velocity Vmean at each sample point. -Vmean, ..., V9-Vmean is calculated.

  Next, in step S8, the differential velocity calculator 24 performs folding correction of differential velocities V1-Vmean, V2-Vmean, V3-Vmean,..., V9-Vmean at each sample point. The aliasing correction can be performed by an algorithm as shown in Equation (6). As a result, the differential speeds V1 #, V2 #, V3 #,..., V9 # after the loopback correction are within the range of the loopback speed ± π. That is, −π ≦ Vi # <π.

  Next, in step S9, the replacement filter 25 extracts a probable velocity Vp that is consistent with the surrounding sample points from the velocities V1, V2, V3,. Therefore, in the replacement filter 25, the median values of the differential velocities V1 #, V2 #, V3 #,..., V9 # after the loopback correction or values near the median are extracted as probable differential velocities. The blood flow velocity corresponding to the probable differential velocity is regarded as the probable velocity Vp.

  Next, in step S10, the replacement filter 25 determines whether or not the blood flow velocity V5 at the central sample point to be corrected for noise removal is an appropriate value. The process for determining whether or not the blood flow velocity V5 is an appropriate value can be a threshold process such as that shown in Expression (7), for example. That is, the blood flow velocity is determined by threshold processing that determines whether or not the difference | V5 # -Vp # | between the blood flow differential velocity V5 # and the probable differential velocity Vp # at the central sample point is smaller than the threshold Vt. Whether V5 is a singular value can be determined.

  If it is determined that the value of the blood flow velocity V5 at the central sample point is not appropriate, the blood flow velocity V5 is replaced with a probable velocity Vp by the replacement filter 25 in step S11. As a result, even if the blood flow velocity V5 at the central sample point is a singular value, it can be corrected to a velocity Vp that is consistent with each blood flow velocity Vi at the surrounding sample points.

  On the other hand, when it is determined that the value of the blood flow velocity V5 at the central sample point is appropriate, the replacement of the blood flow velocity V5 by the replacement filter 25 is not performed. As a result, it is possible to suppress the occurrence of blurring in the image accompanying the replacement of blood flow velocity data.

  Next, in step S12, the blood flow information calculator 12 determines whether or not the blood flow velocity at all sample points of the blood flow velocity data has been subjected to noise correction. If the blood flow velocity at all the sample points is not subject to noise correction, the noise correction processing from step S5 to step S11 is executed again by changing the sample point to be subject to noise correction. That is, the noise correction process is repeatedly performed while sequentially moving the central sample point and each adjacent sample point shown in FIG.

  If it is determined in step S12 that the blood flow velocity at all the sample points has been subjected to noise correction, the necessary blood flow velocity data, blood flow power data, and blood flow dispersion data after the noise correction processing are required. The blood flow data is output from the blood flow information calculator 12 to the scan converter 8 as ultrasonic color data for a color Doppler image.

  Next, in step S13, the scan converter 8 performs scan conversion of the ultrasonic color data for color Doppler images and the ultrasonic B-mode data for B-mode images. As a result, ultrasonic color image data and B-mode image data in the television format are generated. Further, the scan converter 8 combines the ultrasonic color image data and the B-mode image data. Thereby, ultrasonic image data in which ultrasonic color image data is superimposed on B-mode image data is generated.

  Next, in step S <b> 14, the ultrasonic image data generated by the scan converter 8 is output to the display system 9. Then, the display system 9 performs RGB conversion of the ultrasonic image data. For RGB conversion of the ultrasonic color image data in the ultrasonic image data, for example, a color bar as shown in FIG. 4B can be used. On the other hand, gray scale can be used for RGB conversion of B-mode image data in ultrasonic image data.

  As a result, the data for displaying the blood flow color image for displaying the blood flow dynamics such as the blood flow velocity, dispersion, and power in color and the data for displaying the B mode image for displaying the form in luminance are superimposed. Data for image display is generated. The display system 9 displays data for displaying color images on the monitor.

  As a result, an ultrasonic diagnostic image in which a color Doppler image is superimposed and displayed on the B-mode image is displayed on the monitor of the display system 9. At this time, the blood flow velocity image displayed on the display system 9 is an image with good image quality in which the singular value is corrected by the noise correction processing. For this reason, the user can perform an appropriate diagnosis.

  In particular, velocity data used for generating a blood flow velocity image is corrected by a noise correction process using a vector velocity. Therefore, the singular value can be removed by adapting to various blood flow velocity distributions. Hereinafter, the noise correction process according to the tendency of blood flow velocity distribution will be described.

  FIG. 8 is a diagram showing a first example of the blood flow velocity distribution at each sample point adjacent to the central sample point shown in FIG.

  In FIG. 8, the vertical axis indicates the imaginary axis, and the horizontal axis indicates the real axis. FIG. 8 shows an example in which the vector velocities Vivector of blood flow at eight sample points adjacent to the central sample point are distributed in the second quadrant of the complex plane.

  That is, the blood flow velocities V 1, V 2, V 3, V 4, V 6, V 7, V 8, V 9 at the eight sample points are the speeds approaching the ultrasonic probe 3. When the blood flow approaches the ultrasonic probe 3 at a general flow rate, the blood flow velocity distribution is as shown in FIG.

  When the vector velocity V5vector of the blood flow at the central sample point is included in the region 1 where the vector velocity Vivector of the blood flow at other surrounding sample points is distributed, the mean value vector Vmean_vector ends near the center of the region 1 It becomes a vector.

  Also, if the probable blood flow velocity Vp is the blood flow velocity corresponding to the median of the differential velocity Vi #, the probable blood flow velocity Vp corresponds to the vector velocity whose end point is close to the mean value vector Vmean_vector. It becomes speed. Accordingly, in the example shown in FIG. 8, the blood flow velocity V5 at the central sample point is not replaced or replaced with the velocity V1 or velocity V9 in the vicinity of the mean value vector Vmean_vector. Accordingly, the blood flow velocity V5 at the center sample point is a consistent value.

  On the other hand, when the vector velocity V5vector of the blood flow at the center sample point is not included in the region 1, the velocity V5 of the blood flow at the center sample point exhibits a singular value. However, the average value vector Vmean_vector is in the vicinity of each vector speed indicating the speed V1 and the speed V9. Therefore, the speed extracted as the likely speed Vp is the speed V1 or the speed V9. Therefore, the velocity V5 that is a singular value is corrected to a consistent value by replacing the velocity V1 or the velocity V9.

  FIG. 9 is a diagram showing a second example of the blood flow velocity distribution at each sample point adjacent to the central sample point shown in FIG.

  In FIG. 9, the vertical axis indicates the imaginary axis, and the horizontal axis indicates the real axis. FIG. 9 shows an example in which the vector velocities Vivector of blood flow at eight sample points adjacent to the central sample point are distributed across the second and third quadrants of the complex plane.

  That is, the blood flow velocities V1, V2, V3, and V4 at the four sample points are moving away from the ultrasonic probe 3, and the blood flow velocities V6, V7, V8, and V9 at the remaining four sample points are The speed approaches the ultrasonic probe 3. When the blood flow velocity is fast and folding occurs, the blood flow velocity distribution is as shown in FIG.

  When the vector velocity V5vector of the blood flow at the central sample point is included in the region 1 where the vector velocity Vivector of the blood flow at other surrounding sample points is distributed, the mean value vector Vmean_vector ends near the center of the region 1 It becomes a vector. Accordingly, as in the example shown in FIG. 8, the blood flow velocity V5 at the central sample point is a consistent value.

  Further, even when the vector velocity V5vector of the blood flow at the central sample point is not included in the region 1, the velocity V5 which is a singular value is replaced with the velocity V1 or the velocity V9 as in the example shown in FIG. Thus, the value is corrected to a consistent value.

  FIG. 10 is a diagram showing a third example of the blood flow velocity distribution at each sample point adjacent to the central sample point shown in FIG.

  In FIG. 10, the vertical axis indicates the imaginary axis, and the horizontal axis indicates the real axis. FIG. 10 shows an example in which the vector velocities Vivector of blood flow at eight sample points adjacent to the center sample point are distributed over the first quadrant and the fourth quadrant of the complex plane.

  That is, the blood flow speeds V1, V2, V3, and V4 at the four sample points are close to the ultrasonic probe 3, and the blood flow speeds V6, V7, V8, and V9 at the remaining four sample points are The speed is away from the ultrasonic probe 3. In the case where both the blood flow approaching the ultrasonic probe 3 and the blood flow moving away from the ultrasonic probe 3 exist, the velocity distribution of the blood flow is as shown in FIG. The velocity distribution as shown in FIG. 10 is observed at the boundary between the two blood flows when, for example, the blood flow flowing in the heart chamber and the blood flow ejected are adjacent to each other.

  Even in the case of the velocity distribution as shown in FIG. 10, the blood flow velocity V5 at the central sample point is consistent with the noise correction processing using the vector velocity, as in the examples shown in FIGS. It is a good value.

  FIG. 11 is a diagram showing a fourth example of the blood flow velocity distribution at each sample point adjacent to the central sample point shown in FIG.

  In FIG. 11, the vertical axis indicates the imaginary axis, and the horizontal axis indicates the real axis. FIG. 11 shows an example in which the vector velocities Vivector of blood flow at eight sample points adjacent to the central sample point are distributed over all quadrants from the first quadrant to the fourth quadrant of the complex plane. For example, when turbulent flow or valve regurgitant jet occurs in the heart chamber and the blood flow is mosaic, the blood flow velocity distribution is as shown in FIG.

  In this case, the average velocity Vmean of the blood flow can take various values. As a result, the speed V5 after the noise correction process can take various values. Since the periphery of the central sample point is a mosaic, there is usually no problem even if the speed V5 after the noise correction processing has various values.

  However, if there is a zero that causes blackout in the velocities V1, V2, V3, ..., V9 at each sample point, depending on the average speed Vmean value, the blackout as the speed V5 after noise correction processing There is a risk of adopting a speed corresponding to. Therefore, it is desirable to avoid errors in which blackout remains as a result of noise correction processing.

  When a turbulent flow or jet is generated in the blood flow, the vector velocity Vivector faces in various directions. Therefore, the average value vector Vmean_vector is small. Therefore, it is possible to detect a special case such as a case where turbulent flow or jet is generated by threshold processing for the magnitude | Vmean_vector | of the average value vector Vmean_vector.

Specifically, a threshold value Th for the magnitude | Vmean_vector | of the average value vector Vmean_vector is set, and it can be determined that turbulent flow or jet is generated when Expression (8) is satisfied. The threshold value Th can be estimated in advance by empirical or simulation.
| Vmean_vector | <Th (8)

  If it is determined that Equation (8) is satisfied, error processing that uses a value that does not cause blackout from the speeds V1, V2, V3, ..., V9 at each sample point is replaced as the speed V5. It can be executed in the filter 25. However, error processing that avoids an error in which the velocity V5 at the central sample point is replaced with a singular value can also be performed by other methods.

  For example, when the equation (8) is established, there is an error process that employs a predetermined speed value as the value of the speed V5 at the central sample point. Alternatively, when the equation (8) is satisfied, there is a method of setting the value of the average speed Vmean to the folding speed, the speed value in the vicinity of the folding speed, or the speed V5 at the center sample point. Furthermore, when the equation (8) is established, the velocity V5 may not be replaced unless the velocity V5 at the central sample point is a value corresponding to blackout.

  By performing such error processing by the replacement filter 25, black spots can be removed even when turbulent flow or jet occurs in the blood flow. As a result, it is possible to display a color Doppler image with good image quality.

  That is, the ultrasonic diagnostic apparatus 1 as described above expresses blood flow velocity values at a plurality of sample points included in a kernel defined as a 3 × 3 matrix or the like as a vector on a complex plane, and averages the vectors. The blood flow velocity signal is subjected to noise correction using the differential velocity obtained by subtracting the velocity value corresponding to the value from the velocity value at each sample point. That is, when the sample point to be corrected exhibits a singular value, the singular value is replaced with a probable velocity value extracted by median processing or the like for the differential velocity corresponding to the plurality of sample points.

  Therefore, according to the ultrasonic diagnostic apparatus 1, when a blood flow velocity image is taken, even if a singular point such as a black spot or a bright spot having a color different from the surroundings due to the influence of speckle or the like is removed. Can do. As a result, the blood flow velocity image can be displayed with good image quality with reduced noise.

  In particular, in a conventional noise removal processing method such as a median filter, when generating a blood flow velocity image having a blood flow velocity distribution with a different sign in the vicinity of the folding speed, blackout may not be removed and may remain. .

  On the other hand, according to the ultrasonic diagnostic apparatus 1, since the blood flow velocity is treated as a vector on a complex plane, it is possible to remove singular values regardless of whether or not the velocity distribution causes folding. it can. Therefore, when a singular point exists in a typical flow velocity distribution, when a singular point exists in a situation where the flow velocity is fast and turns, a singular point exists in a situation where the flow velocity is slow and the flow velocity is negative and the sign is positive or negative. The singular point can be removed both when the singular point exists and when the singular point exists in the situation where the turbulent flow or the jet is generated.

  Although specific embodiments have been described above, the described embodiments are merely examples, and do not limit the scope of the invention. The novel methods and apparatus described herein can be implemented in a variety of other ways. Various omissions, substitutions, and changes can be made in the method and apparatus described herein without departing from the spirit of the invention. The appended claims and their equivalents include such various forms and modifications as are encompassed by the scope and spirit of the invention.

DESCRIPTION OF SYMBOLS 1 Ultrasonic diagnostic apparatus 2 Transmission system 3 Ultrasonic probe 4 Reception system 5 B mode processing system 6 Quadrature phase detector 7 CFM processing system 8 Scan converter 9 Display system 10 MTI filter 11 Autocorrelator 12 Blood flow information calculator 20 Speed / Distribution / Power calculator 21 Blanking processor 22 Vector speed averager 23 Average speed calculator 24 Differential speed calculator 25 Replacement filter

Claims (7)

Scanning means for transmitting and receiving ultrasonic waves to a subject and collecting a plurality of blood flow Doppler signals as complex signals;
Speed calculation means for determining the speed of blood flow based on the blood flow Doppler signal;
Vector calculation for calculating the velocity of the blood flow at a plurality of spatially different points in units of a plurality of vectors on a complex plane, and a difference that is a difference in velocity of each blood flow from an average velocity of the blood flow velocity Correction means for correcting the velocity of the blood flow with the return of the velocity correction;
Image generating means for generating blood flow image data based on the corrected blood flow velocity;
An ultrasonic diagnostic apparatus comprising: Scanning means for transmitting and receiving ultrasonic waves to a subject and collecting a plurality of blood flow Doppler signals as complex signals;
Speed calculation means for determining the speed of blood flow based on the blood flow Doppler signal;
Correction means for correcting the blood flow velocity with vector calculation and velocity folding correction for calculating the blood flow velocity in a plurality of vector units on a complex plane;
Image generating means for generating blood flow image data based on the corrected blood flow velocity;
With
The correction means includes
Each velocity of blood flow at a plurality of sample points included in a predetermined range is expressed as a plurality of vectors on a complex plane, and a velocity of blood flow corresponding to a vector obtained by averaging the plurality of vectors is obtained as an average velocity. Having an average speed calculation means,
Folding correction is performed so that each differential velocity between the average velocity and each velocity of the blood flow at the plurality of sample points is within a folding velocity range, and extracted from each differential velocity after the folding correction. ultrasonic diagnostic apparatus which is configured to correct the velocity of blood flow at the sample point of interest using the velocity of blood flow at the sample point corresponding to the difference speed is.   The ultrasonic diagnostic apparatus according to claim 2, wherein the correction unit is configured to replace a blood flow velocity at the sample point as the target with a blood flow velocity at the sample point corresponding to the differential velocity.   When it is determined that the blood flow velocity at the target sample point is not an appropriate value, the correction means changes the blood flow velocity at the target sample point to the blood flow velocity at another sample point. The ultrasonic diagnostic apparatus according to claim 1, wherein the ultrasonic diagnostic apparatus is configured to be replaced.   The ultrasonic diagnostic apparatus according to claim 2, wherein the correction unit is configured to extract a median value or a differential velocity near the median value as a differential velocity from each differential velocity after the aliasing correction.   The correction means is configured to execute error processing that prevents a blood flow velocity at a target sample point from being replaced with a singular value when a predetermined state is detected. The ultrasonic diagnostic apparatus according to any one of the above. Ultrasound diagnostic equipment,
Scanning means for transmitting and receiving ultrasonic waves to a subject and collecting a plurality of blood flow Doppler signals as complex signals;
A speed calculating means for determining a blood flow speed based on the blood flow Doppler signal;
Vector calculation for calculating the velocity of the blood flow at a plurality of spatially different points in units of a plurality of vectors on a complex plane, and a difference that is a difference in velocity of each blood flow from an average velocity of the blood flow velocity Correction means for correcting the velocity of the blood flow with the return of velocity correction, and image generation means for generating blood flow image data based on the corrected velocity of the blood flow,
Control program for an ultrasonic diagnostic apparatus that functions as