Terpstra et al., 2021 - Google Patents

Real‐time 3D motion estimation from undersampled MRI using multi‐resolution neural networks

Terpstra et al., 2021

View PDF
Document ID
3751206026011011313
Author
Terpstra M
Maspero M
Bruijnen T
Verhoeff J
Lagendijk J
van den Berg C
Publication year
Publication venue
Medical physics

External Links

Snippet

Purpose: To enable real‐time adaptive magnetic resonance imaging–guided radiotherapy (MRIgRT) by obtaining time‐resolved three‐dimensional (3D) deformation vector fields (DVFs) with high spatiotemporal resolution and low latency (ms). Theory and Methods …
Continue reading at aapm.onlinelibrary.wiley.com (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
    • G01R33/563—Image 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
    • 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
    • G01R33/561—Image 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
    • 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
    • G01R33/5601—Image 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
    • 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
    • G01R33/565—Correction of image distortions, e.g. due to magnetic field inhomogeneities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • 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
    • 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
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Akçakaya et al. Scan‐specific robust artificial‐neural‐networks for k‐space interpolation (RAKI) reconstruction: database‐free deep learning for fast imaging
Terpstra et al. Real‐time 3D motion estimation from undersampled MRI using multi‐resolution neural networks
Lin et al. Artificial intelligence for MR image reconstruction: an overview for clinicians
Knoll et al. Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues
Lingala et al. Deformation corrected compressed sensing (DC-CS): a novel framework for accelerated dynamic MRI
El‐Rewaidy et al. Multi‐domain convolutional neural network (MD‐CNN) for radial reconstruction of dynamic cardiac MRI
Zhou et al. Simple motion correction strategy reduces respiratory-induced motion artifacts for kt accelerated and compressed-sensing cardiovascular magnetic resonance perfusion imaging
US20140219531A1 (en) Systems and methods for accelerated dynamic magnetic resonance imaging
Jun et al. Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip angle method
Chang et al. Deep learning-based rigid motion correction for magnetic resonance imaging: a survey
Küstner et al. LAPNet: Non-rigid registration derived in k-space for magnetic resonance imaging
Hossbach et al. Deep learning‐based motion quantification from k‐space for fast model‐based magnetic resonance imaging motion correction
Liu et al. High-performance rapid MR parameter mapping using model-based deep adversarial learning
Huttinga et al. Nonrigid 3D motion estimation at high temporal resolution from prospectively undersampled k‐space data using low‐rank MR‐MOTUS
Friedrich et al. Stability of conventional and machine learning‐based tumor auto‐segmentation techniques using undersampled dynamic radial bSSFP acquisitions on a 0.35 T hybrid MR‐linac system
Meng et al. Accelerating T2 mapping of the brain by integrating deep learning priors with low‐rank and sparse modeling
Wang et al. Correction of out-of-FOV motion artifacts using convolutional neural network
Rich et al. A Bayesian approach for 4D flow imaging of aortic valve in a single breath‐hold
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
Terpstra et al. Accelerated respiratory‐resolved 4D‐MRI with separable spatio‐temporal neural networks
Shao et al. Real-time MRI motion estimation through an unsupervised k-space-driven deformable registration network (KS-RegNet)
Hamilton et al. A low-rank deep image prior reconstruction for free-breathing ungated spiral functional CMR at 0.55 T and 1.5 T
Jafari et al. GRASPNET: fast spatiotemporal deep learning reconstruction of golden‐angle radial data for free‐breathing dynamic contrast‐enhanced magnetic resonance imaging
Bao et al. Retrospective motion correction for preclinical/clinical magnetic resonance imaging based on a conditional generative adversarial network with entropy loss
Waddington et al. Real‐time radial reconstruction with domain transform manifold learning for MRI‐guided radiotherapy