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

Zhou et al., 2022 - Google Patents

Accelerating MR parameter mapping using nonlinear compressive manifold learning and regularized pre-imaging

Zhou et al., 2022

Document ID
17581721824993348200
Author
Zhou Y
Wang H
Liu Y
Liang D
Ying L
Publication year
Publication venue
IEEE Transactions on Biomedical Engineering

External Links

Snippet

In this study, we present a novel method to reconstruct the MR parametric maps from highly undersampled k-space data. Specifically, we utilize a nonlinear model to sparsely represent the unknown MR parameter-weighted images in high-dimensional feature space. Each …
Continue reading at ieeexplore.ieee.org (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
    • 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
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • 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
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/007—Dynamic range modification
    • G06T5/008—Local, e.g. shadow enhancement
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
    • G06T3/40—Scaling the whole image or part thereof
    • G06T3/4053—Super resolution, i.e. output image resolution higher than sensor resolution

Similar Documents

Publication Publication Date Title
Chung et al. Score-based diffusion models for accelerated MRI
Lin et al. Artificial intelligence for MR image reconstruction: an overview for clinicians
Liang et al. Deep magnetic resonance image reconstruction: Inverse problems meet neural networks
Chen et al. Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges
Tezcan et al. MR image reconstruction using deep density priors
Lyu et al. Cine cardiac MRI motion artifact reduction using a recurrent neural network
Lee et al. Deep residual learning for accelerated MRI using magnitude and phase networks
Guo et al. Reconformer: Accelerated mri reconstruction using recurrent transformer
Cole et al. Unsupervised MRI reconstruction with generative adversarial networks
Lingala et al. Deformation corrected compressed sensing (DC-CS): a novel framework for accelerated dynamic MRI
Zou et al. Dynamic imaging using a deep generative SToRM (Gen-SToRM) model
Poddar et al. Manifold recovery using kernel low-rank regularization: Application to dynamic imaging
Lingala et al. Accelerating free breathing myocardial perfusion MRI using multi coil radial k− t SLR
Liu et al. High-performance rapid MR parameter mapping using model-based deep adversarial learning
Singh et al. Joint frequency and image space learning for MRI reconstruction and analysis
Hu et al. Spatiotemporal flexible sparse reconstruction for rapid dynamic contrast-enhanced MRI
Zhou et al. Accelerating MR parameter mapping using nonlinear compressive manifold learning and regularized pre-imaging
Karkalousos et al. Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction
Velasco et al. Artificial intelligence in cardiac magnetic resonance fingerprinting
Fu et al. A multi-scale residual network for accelerated radial MR parameter mapping
Safari et al. MRI motion artifact reduction using a conditional diffusion probabilistic model (MAR‐CDPM)
Djebra et al. Manifold learning via linear tangent space alignment (LTSA) for accelerated dynamic MRI with sparse sampling
Liu et al. Accelerating the 3D T1ρ mapping of cartilage using a signal-compensated robust tensor principal component analysis model
Wang et al. One for multiple: Physics-informed synthetic data boosts generalizable deep learning for fast MRI reconstruction
Zhang et al. Zero-shot self-supervised joint temporal image and sensitivity map reconstruction via linear latent space