ArticleIEEE journal of selected topics in signal processing2020
J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction.
Article in IEEE journal of selected topics in signal processing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Deep-Learning-Based Image Reconstruction to Improve End-Diastolic and Systolic Cardiac T1 Mapping.Magnetic resonance in medicine · 2026Article
- Jointly Learned 3D Non-Cartesian Sampling With Wave Encoding and Reconstruction for Neurovascular Phase Contrast MRI.Magnetic resonance in medicine · 2026Article
- Well-designed k-space coverage is important for good MRI denoising.Magma (New York, N.Y.) · 2026Article
- Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO).IEEE transactions on computational imaging · 2026Article
- Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration.ArXiv · 2025Article
- A critical assessment of artificial intelligence in magnetic resonance imaging of cancer.Npj imaging · 2025Review
- AutoSamp: Autoencoding k-Space Sampling via Variational Information Maximization for 3D MRI.IEEE transactions on medical imaging · 2025Article
- A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies.Biomedical engineering letters · 2024Review
- 2D CAIPI accelerated 3D multi-slab diffusion weighted EPI combined with qModeL reconstruction for fast high resolution microstructure imaging.Magnetic resonance imaging · 2024Article
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- HyperSLICE: HyperBand optimized spiral for low-latency interactive cardiac examination.Magnetic resonance in medicine · 2024Article
- Learning Task-Specific Strategies for Accelerated MRI.IEEE transactions on computational imaging · 2024Article
- Complexities of deep learning-based undersampled MR image reconstruction.European radiology experimental · 2023Review
- Optimizing variable flip angles in magnetization-prepared gradient-echo sequences for efficient 3D-T1ρ mapping.Magnetic resonance in medicine · 2023Article
- Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- AI in MRI: Computational Frameworks for a Faster, Optimized, and Automated Imaging Workflow.Bioengineering (Basel, Switzerland) · 2023Article
- Efficient Approximation of Jacobian Matrices Involving a Non-Uniform Fast Fourier Transform (NUFFT).IEEE transactions on computational imaging · 2023Article
- Learning to Sense for Coded Diffraction Imaging.Sensors (Basel, Switzerland) · 2022Article
- FReSCO: Flow Reconstruction and Segmentation for low-latency Cardiac Output monitoring using deep artifact suppression and segmentation.Magnetic resonance in medicine · 2022Article
- Joint Optimization of k-t Sampling Pattern and Reconstruction of DCE MRI for Pharmacokinetic Parameter Estimation.IEEE transactions on medical imaging · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Modern MRI schemes, which rely on compressed sensing or deep learning algorithms to recover MRI data from undersampled multichannel Fourier measurements, are widely used to reduce the scan time. The image quality of these approaches is heavily dependent on the sampling pattern. We introduce a continuous strategy to optimize the sampling pattern and the network parameters jointly. We use a multichannel forward model, consisting of a non-uniform Fourier transform with continuously defined sampling locations, to realize the data consistency block within a model-based deep learning image reconstruction scheme. This approach facilitates the joint and continuous optimization of the sampling pattern and the CNN parameters to improve image quality. We observe that the joint optimization of the sampling patterns and the reconstruction module significantly improves the performance of most deep learning reconstruction algorithms. The source code is available at https://github.com/hkaggarwal/J-MoDL.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.