[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Lin et al., 2021 - Google Patents

Artificial intelligence for MR image reconstruction: an overview for clinicians

Lin et al., 2021

View HTML
Document ID
8157537277579667671
Author
Lin D
Johnson P
Knoll F
Lui Y
Publication year
Publication venue
Journal of Magnetic Resonance Imaging

External Links

Snippet

Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep‐learning models for data acquisition, classification problems, segmentation, image synthesis, and image reconstruction. With an eye towards …
Continue reading at www.ncbi.nlm.nih.gov (HTML) (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00Arrangements or instruments for measuring magnetic variables
    • G01R33/20Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48NMR imaging systems
    • G01R33/54Signal processing systems, e.g. using pulse sequences, Generation or control of pulse sequences ; Operator Console
    • G01R33/56Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/563Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution of moving material, e.g. flow contrast angiography
    • G01R33/56308Characterization of motion or flow; Dynamic imaging
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00Arrangements or instruments for measuring magnetic variables
    • G01R33/20Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48NMR imaging systems
    • G01R33/54Signal processing systems, e.g. using pulse sequences, Generation or control of pulse sequences ; Operator Console
    • G01R33/56Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/561Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution by reduction of the scanning time, i.e. fast acquiring systems, e.g. using echo-planar pulse sequences
    • G01R33/5615Echo train techniques involving acquiring plural, differently encoded, echo signals after one RF excitation, e.g. using gradient refocusing in echo planar imaging [EPI], RF refocusing in rapid acquisition with relaxation enhancement [RARE] or using both RF and gradient refocusing in gradient and spin echo imaging [GRASE]
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00Arrangements or instruments for measuring magnetic variables
    • G01R33/20Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48NMR imaging systems
    • G01R33/54Signal processing systems, e.g. using pulse sequences, Generation or control of pulse sequences ; Operator Console
    • G01R33/56Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/563Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution of moving material, e.g. flow contrast angiography
    • G01R33/56341Diffusion imaging
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00Arrangements or instruments for measuring magnetic variables
    • G01R33/20Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48NMR imaging systems
    • G01R33/54Signal processing systems, e.g. using pulse sequences, Generation or control of pulse sequences ; Operator Console
    • G01R33/56Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/565Correction of image distortions, e.g. due to magnetic field inhomogeneities
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00Arrangements or instruments for measuring magnetic variables
    • G01R33/20Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48NMR imaging systems
    • G01R33/4806Functional imaging of brain activation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10072Tomographic images
    • G06T2207/10084Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
    • G06T3/40Scaling the whole image or part thereof
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/002D [Two Dimensional] image generation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications

Similar Documents

Publication Publication Date Title
Lin et al. Artificial intelligence for MR image reconstruction: an overview for clinicians
Küstner et al. CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions
Cole et al. Analysis of deep complex‐valued convolutional neural networks for MRI reconstruction and phase‐focused applications
Sandino et al. Accelerating cardiac cine MRI using a deep learning‐based ESPIRiT reconstruction
Chaudhari et al. Rapid knee MRI acquisition and analysis techniques for imaging osteoarthritis
Feng et al. GRASP‐Pro: imProving GRASP DCE‐MRI through self‐calibrating subspace‐modeling and contrast phase automation
Cruz et al. Accelerated motion corrected three‐dimensional abdominal MRI using total variation regularized SENSE reconstruction
Do et al. Reconstruction of multicontrast MR images through deep learning
Cole et al. Unsupervised MRI reconstruction with generative adversarial networks
Van Reeth et al. Super‐resolution in magnetic resonance imaging: a review
Otazo et al. Low‐rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components
Schloegl et al. Infimal convolution of total generalized variation functionals for dynamic MRI
Lv et al. Which GAN? A comparative study of generative adversarial network-based fast MRI reconstruction
Jia et al. A new sparse representation framework for reconstruction of an isotropic high spatial resolution MR volume from orthogonal anisotropic resolution scans
Zhou et al. Dual-domain self-supervised learning for accelerated non-Cartesian MRI reconstruction
Gao et al. PCLR: phase‐constrained low‐rank model for compressive diffusion‐weighted MRI
Chang et al. Deep learning-based rigid motion correction for magnetic resonance imaging: a survey
Meng et al. Accelerating T2 mapping of the brain by integrating deep learning priors with low‐rank and sparse modeling
Rich et al. A Bayesian approach for 4D flow imaging of aortic valve in a single breath‐hold
Defazio et al. MRI banding removal via adversarial training
Liu et al. Robust water–fat separation based on deep learning model exploring multi‐echo nature of mGRE
Xu et al. STRESS: Super-resolution for dynamic fetal MRI using self-supervised learning
Li et al. High‐fidelity fast volumetric brain MRI using synergistic wave‐controlled aliasing in parallel imaging and a hybrid denoising generative adversarial network (HDnGAN)
Ghodrati et al. Temporally aware volumetric generative adversarial network‐based MR image reconstruction with simultaneous respiratory motion compensation: Initial feasibility in 3D dynamic cine cardiac MRI
Desai et al. Vortex: Physics-driven data augmentations for consistency training for robust accelerated mri reconstruction