Nothing Special   »   [go: up one dir, main page]

Sudarshan et al., 2021 - Google Patents

Towards lower-dose PET using physics-based uncertainty-aware multimodal learning with robustness to out-of-distribution data

Sudarshan et al., 2021

View PDF
Document ID
9241157494418287619
Author
Sudarshan V
Upadhyay U
Egan G
Chen Z
Awate S
Publication year
Publication venue
Medical Image Analysis

External Links

Snippet

Radiation exposure in positron emission tomography (PET) imaging limits its usage in the studies of radiation-sensitive populations, eg, pregnant women, children, and adults that require longitudinal imaging. Reducing the PET radiotracer dose or acquisition time reduces …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences, Generation or control of pulse sequences ; Operator Console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2211/00—Image generation
    • G06T2211/40—Computed tomography
    • G06T2211/424—Iterative
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10104—Positron emission tomography [PET]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/003—Reconstruction from projections, e.g. tomography
    • G06T11/005—Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/003—Reconstruction from projections, e.g. tomography
    • G06T11/006—Inverse problem, transformation from projection-space into object-space, e.g. transform methods, back-projection, algebraic methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing

Similar Documents

Publication Publication Date Title
Sudarshan et al. Towards lower-dose PET using physics-based uncertainty-aware multimodal learning with robustness to out-of-distribution data
Sanaat et al. Projection space implementation of deep learning–guided low-dose brain PET imaging improves performance over implementation in image space
Zhu et al. Image reconstruction by domain-transform manifold learning
Zhou et al. Handbook of medical image computing and computer assisted intervention
Arabi et al. Deep learning‐guided joint attenuation and scatter correction in multitracer neuroimaging studies
Zhao et al. Study of low-dose PET image recovery using supervised learning with CycleGAN
Hagiwara et al. Improving the quality of synthetic FLAIR images with deep learning using a conditional generative adversarial network for pixel-by-pixel image translation
Keereman et al. MRI-based attenuation correction for PET/MRI using ultrashort echo time sequences
Spuhler et al. Full‐count PET recovery from low‐count image using a dilated convolutional neural network
Gómez et al. Rapid three-dimensional multiparametric MRI with quantitative transient-state imaging
Liu et al. Accuracy of the morphology enabled dipole inversion (MEDI) algorithm for quantitative susceptibility mapping in MRI
Catana et al. Toward implementing an MRI-based PET attenuation-correction method for neurologic studies on the MR-PET brain prototype
Cheng et al. Comprehensive multi-dimensional MRI for the simultaneous assessment of cardiopulmonary anatomy and physiology
Eun et al. Deep-learning-based image quality enhancement of compressed sensing magnetic resonance imaging of vessel wall: comparison of self-supervised and unsupervised approaches
Liu et al. Deep learning with noise‐to‐noise training for denoising in SPECT myocardial perfusion imaging
Konkle et al. A convex formulation for magnetic particle imaging x-space reconstruction
Sanaat et al. DeepTOFSino: A deep learning model for synthesizing full-dose time-of-flight bin sinograms from their corresponding low-dose sinograms
Schwarz et al. Contributions of imprecision in PET‐MRI rigid registration to imprecision in amyloid PET SUVR measurements
Liang et al. Comparison of different compressed sensing algorithms for low SNR 19F MRI applications—imaging of transplanted pancreatic islets and cells labeled with perfluorocarbons
Zhang et al. Spatial adaptive and transformer fusion network (STFNet) for low‐count PET blind denoising with MRI
Fuin et al. PET/MRI in the presence of metal implants: completion of the attenuation map from PET emission data
Li et al. Adaptive angular sampling for SPECT imaging
Hashimoto et al. Deep learning-based attenuation correction for brain PET with various radiotracers
Xi et al. Simultaneous CT-MRI reconstruction for constrained imaging geometries using structural coupling and compressive sensing
Xu et al. Ultra-low-dose 18F-FDG brain PET/MR denoising using deep learning and multi-contrast information