[go: up one dir, main page]

Gan et al., 2023 - Google Patents

Self-supervised deep equilibrium models with theoretical guarantees and applications to MRI reconstruction

Gan et al., 2023

Document ID
2414913363037253073
Author
Gan W
Ying C
Boroojeni P
Wang T
Eldeniz C
Hu Y
Liu J
Chen Y
An H
Kamilov U
Publication year
Publication venue
IEEE Transactions on Computational Imaging

External Links

Snippet

Deep equilibrium models (DEQ) have emerged as a powerful alternative to deep unfolding (DU) for image reconstruction. DEQ models—implicit neural networks with effectively infinite number of layers—were shown to achieve state-of-the-art image reconstruction without the …
Continue reading at ieeexplore.ieee.org (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
    • G01R33/5601Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution involving use of a contrast agent for contrast manipulation, e.g. a paramagnetic, super-paramagnetic, ferromagnetic or hyperpolarised contrast agent
    • 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
    • G01R33/54Signal 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
    • G01R33/4806Functional imaging of brain activation
    • 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
    • G01R33/483NMR imaging systems with selection of signals or spectra from particular regions of the volume, e.g. in vivo spectroscopy
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations

Similar Documents

Publication Publication Date Title
Bustin et al. Five‐minute whole‐heart coronary MRA with sub‐millimeter isotropic resolution, 100% respiratory scan efficiency, and 3D‐PROST reconstruction
Chen et al. Variable-density single-shot fast spin-echo MRI with deep learning reconstruction by using variational networks
Lee et al. Deep residual learning for accelerated MRI using magnitude and phase networks
Bilgic et al. Fast quantitative susceptibility mapping with L1‐regularization and automatic parameter selection
US9542763B2 (en) Systems and methods for fast reconstruction for quantitative susceptibility mapping using magnetic resonance imaging
Zhang et al. MRI Gibbs‐ringing artifact reduction by means of machine learning using convolutional neural networks
Gan et al. Self-supervised deep equilibrium models with theoretical guarantees and applications to MRI reconstruction
Sandino et al. Deep convolutional neural networks for accelerated dynamic magnetic resonance imaging
Hammernik et al. $\Sigma $-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction
Sudarshan et al. Joint PET-MRI image reconstruction using a patch-based joint-dictionary prior
Liu et al. High-performance rapid MR parameter mapping using model-based deep adversarial learning
Chen et al. Data‐driven self‐calibration and reconstruction for non‐cartesian wave‐encoded single‐shot fast spin echo using deep learning
Singh et al. Joint frequency and image space learning for MRI reconstruction and analysis
Cha et al. Unpaired training of deep learning tMRA for flexible spatio-temporal resolution
Kleineisel et al. Real‐time cardiac MRI using an undersampled spiral k‐space trajectory and a reconstruction based on a variational network
Zaid Alkilani et al. FD‐Net: An unsupervised deep forward‐distortion model for susceptibility artifact correction in EPI
Liu et al. Accelerating the 3D T1ρ mapping of cartilage using a signal-compensated robust tensor principal component analysis model
Xu et al. CoRRECT: A deep unfolding framework for motion-corrected quantitative R2* mapping
Klug et al. Analyzing the sample complexity of self-supervised image reconstruction methods
Tripathi et al. Denoising of motion artifacted MRI scans using conditional generative adversarial network
WO2025103397A1 (en) Echo time-dependent magnetic resonance diffusion imaging signal generation method and apparatus, computing device and medium
Gan et al. SS-JIRCS: Self-supervised joint image reconstruction and coil sensitivity calibration in parallel MRI without ground truth
Usman et al. Motion corrected multishot MRI reconstruction using generative networks with sensitivity encoding
Gan et al. Self-supervised deep equilibrium models for inverse problems with theoretical guarantees
Huang et al. Accelerating cardiac diffusion tensor imaging combining local low-rank and 3D TV constraint