ArticleIEEE signal processing magazine2023
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging.
Article in IEEE signal processing magazine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
49 citing papers in PubMed, 98 citations in OpenAlex.
- Accelerating Stroke MRI With Diffusion Probabilistic Models Through Large-Scale Pre-Training and Target-Specific Fine-Tuning.Magnetic resonance in medicine · 2026Article
- Variable flip angle T1 mapping for quantitative liver imaging at 0.55 T.Magma (New York, N.Y.) · 2026Article
- Memory-efficient image reconstruction using diffusion models for accelerated 3D non-Cartesian UTE imaging.Magma (New York, N.Y.) · 2026Article
- Deep-Learning-Based Image Reconstruction to Improve End-Diastolic and Systolic Cardiac T1 Mapping.Magnetic resonance in medicine · 2026Article
- Hybrid learning: a combination of self-supervised and supervised learning for joint MRI reconstruction and denoising in low-field MRI.Physics in medicine and biology · 2026Article
- Domain-Conditioned and Temporal-Guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction.NMR in biomedicine · 2026Article
- Physics-based simulation of ultrasound propagation from MRI volumes for cross-modal breast imaging.Journal of biological engineering · 2026Article
- Optimized reconstruction of undersampled Dixon sequences using new memory-efficient unrolled deep neural networks: HalfVarNet and HalfDIRCN.Magnetic resonance in medicine · 2026Article
- A Physics-Informed Neural Network forResearch square · 2026Article
- PINN-FFD: physics- and frequency-informed one-stage detector for brain tumor detection in MRI.Frontiers in oncology · 2026Article
- Liver fat quantification at 0.55 T enabled by locally low-rank enforced deep learning reconstruction.Magnetic resonance in medicine · 2026Article
- Multi-contrast low-field MRI acceleration with k-space progressive learning and image-space hybrid attention fusion.Medical image analysis · 2026Article
- Deep Learning HASTE for Upper Abdominal MRI: Improved Image Quality, Speed, and Energy Efficiency in a Prospective Study.NMR in biomedicine · 2026Article
- CONSISTENCY MODELS FOR FAST MRI USING REGULARIZATION BY DENOISING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- REVISITING MRI RECONSTRUCTION USING A COMBINATION OF COMPLEX AND MAGNITUDE MEASUREMENTS WITH LEARNED PRIORS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- Foundation Models Meet Medical Image Interpretation.Research (Washington, D.C.) · 2026Review
- Accelerated free-breathing abdominal T2 mapping with deep learning reconstruction of radial turbo spin-echo data.Magnetic resonance in medicine · 2025Article
- Image space formalism of convolutional neural networks for k-space interpolation.Magnetic resonance in medicine · 2025Article
- An unsupervised method for MRI recovery: deep image prior with structured sparsity.Magma (New York, N.Y.) · 2025Article
- Real-time cardiac cine MRI: A comparison of a diffusion probabilistic model with alternative state-of-the-art image reconstruction techniques for undersampled spiral acquisitions.Magnetic resonance in medicine · 2025Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
Abstract
Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits. This article provides an overview of the recent developments in incorporating physics information into learning-based MRI reconstruction. We consider inverse problems with both linear and non-linear forward models for computational MRI, and review the classical approaches for solving these. We then focus on physics-driven deep learning approaches, covering physics-driven loss functions, plug-and-play methods, generative models, and unrolled networks. We highlight domain-specific challenges such as real- and complex-valued building blocks of neural networks, and translational applications in MRI with linear and non-linear forward models. Finally, we discuss common issues and open challenges, and draw connections to the importance of physics-driven learning when combined with other downstream tasks in the medical imaging pipeline.
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What OpenQuestion holds
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.