mlsanityData to 14 Oct 2026

Deep Learning for Accelerated and Robust MRI Reconstruction: a Review

arXiv 2404.15692 ↗First posted 24 Apr 2024cs.LG eess.IVcited by 9

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Abstract (arXiv)

Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction. It focuses on DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. These include end-to-end neural networks, pre-trained networks, generative models, and self-supervised methods. The paper also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling subtle bias. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 55 of its 322 references that are in the map. First posted on arXiv 24 Apr 2024.

Builds on

55 of 322 references.

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50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 7; 2026: 1.

9 papers.

  1. 2025Normalization-equivariant Diffusion Models: Learning Posterior Samplers From Noisy And Partial Measurementscited by 3
  2. 2025A Comprehensive Survey on Magnetic Resonance Image Reconstructioncited by 1
  3. 2025Enhancing and Accelerating Brain MRI through Deep Learning Reconstruction Using Prior Subject-Specific Imagingcited by 1
  4. 2025Toward Artificial Palpation: Representation Learning of Touch on Soft Bodiescited by 1
  5. 2024Deep Dive into MRI: Exploring Deep Learning Applications in 0.55T and 7T MRIcited by 0
  6. 2025Evaluating structural uncertainty in accelerated MRI: are voxelwise measures useful surrogates?cited by 0
  7. 2025I2I-PR: Deep Iterative Refinement for Phase Retrieval using Image-to-Image Diffusion Modelscited by 0
  8. 2025Understanding Benefits and Pitfalls of Current Methods for the Segmentation of Undersampled MRI Datacited by 0
  9. 2026Triggering hallucinations in model-based MRI reconstruction via adversarial perturbationscited by 0