ArticleMagnetic resonance in medicine2019
Dynamic MRI using model-based deep learning and SToRM priors: MoDL-SToRM.
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Spatiotemporal Maps for Dynamic MRI Reconstruction.IEEE transactions on computational imaging · 2026Article
- Multifrequency Time-Dependent Deep Image Prior for Real-Time Free-Breathing Cardiac Imaging.NMR in biomedicine · 2025Article
- Review of the Current State of Artificial Intelligence in Pediatric Cardiovascular Magnetic Resonance Imaging.Children (Basel, Switzerland) · 2025Review
- A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies.Biomedical engineering letters · 2024Review
- Alternating low-rank tensor reconstruction for improved multiparametric mapping with cardiovascular MR Multitasking.Magnetic resonance in medicine · 2024Article
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- 3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR).Physics in medicine and biology · 2024Article
- High-resolution spiral real-time cardiac cine imaging with deep learning-based rapid image reconstruction and quantification.NMR in biomedicine · 2024Article
- Model-based deep learning framework for accelerated optical projection tomography.Scientific reports · 2023Article
- Model-based Deep Learning Reconstruction Using a Folded Image Training Strategy for Abdominal 3D T1-weighted Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Article
- Joint spectral quantification of MR spectroscopic imaging using linear tangent space alignment-based manifold learning.Magnetic resonance in medicine · 2023Article
- Memory efficient model based deep learning reconstructions for high spatial resolution 3D non-cartesian acquisitions.Physics in medicine and biology · 2023Article
- Deep Learning-Based Reconstruction for Cardiac MRI: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Manifold Learning via Linear Tangent Space Alignment (LTSA) for Accelerated Dynamic MRI With Sparse Sampling.IEEE transactions on medical imaging · 2023Article
- Accelerating 3D MTC-BOOST in patients with congenital heart disease using a joint multi-scale variational neural network reconstruction.Magnetic resonance imaging · 2022Article
- Dynamic Imaging Using Deep Bi-Linear Unsupervised Representation (DEBLUR).IEEE transactions on medical imaging · 2022Article
- Gradient-echo-train-based sub-millisecond periodic event encoded dynamic imaging with random (k, t)-space undersampling: k-t get-SPEEDI.Magnetic resonance in medicine · 2022Article
- A review and experimental evaluation of deep learning methods for MRI reconstruction.The journal of machine learning for biomedical imaging · 2022Article
- Cardiac MR: From Theory to Practice.Frontiers in cardiovascular medicine · 2022Review
- Deep learning for radial SMS myocardial perfusion reconstruction using the 3D residual booster U-net.Magnetic resonance imaging · 2021Article
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3 authors.
Funding
Abstract
purposeTo introduce a novel framework to combine deep-learned priors along with complementary image regularization penalties to reconstruct free breathing & ungated cardiac MRI data from highly undersampled multi-channel measurements.
methodsImage recovery is formulated as an optimization problem, where the cost function is the sum of data consistency term, convolutional neural network (CNN) denoising prior, and SmooThness regularization on manifolds (SToRM) prior that exploits the manifold structure of images in the dataset. An iterative algorithm, which alternates between denoizing of the image data using CNN and SToRM, and conjugate gradients (CG) step that minimizes the data consistency cost is introduced. Unrolling the iterative algorithm yields a deep network, which is trained using exemplar data.
resultsThe experimental results demonstrate that the proposed framework can offer fast recovery of free breathing and ungated cardiac MRI data from less than 8.2s of acquisition time per slice. The reconstructions are comparable in image quality to SToRM reconstructions from 42s of acquisition time, offering a fivefold reduction in scan time.
conclusionsThe results show the benefit in combining deep learned CNN priors with complementary image regularization penalties. Specifically, this work demonstrates the benefit in combining the CNN prior that exploits local and population generalizable redundancies together with SToRM, which capitalizes on patient-specific information including cardiac and respiratory patterns. The synergistic combination is facilitated by the proposed framework.
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