ReviewBMC medical imaging2021
A review on deep learning MRI reconstruction without fully sampled k-space.
Review in BMC medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
What it found
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Who cites it
40 citing papers in PubMed.
- Article
- Memory-efficient image reconstruction using diffusion models for accelerated 3D non-Cartesian UTE imaging.Magma (New York, N.Y.) · 2026Article
- Limited-view signal recovery with frequency-aware denoising diffusion and geometry-integrated masked autoencoders for X-ray acoustic computed tomography.Photoacoustics · 2026Article
- DEEP-DISORDER: Motion Correction in 3D MRI via Segment Reconstruction and Registration.NMR in biomedicine · 2026Article
- Clinical evaluation of deep learning accelerated lumbar T2-weighted and fat-suppressed MRI sequences.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- FOCUS-DWI improves prostate cancer detection through deep learning reconstruction with IQMR technology.Abdominal radiology (New York) · 2026Article
- Self-supervised learning for MRI reconstruction: a review and new perspective.Magma (New York, N.Y.) · 2025Review
- Standardized spinal MRI protocol: diagnosing spontaneous intracranial hypotension and correlating with brain MRI.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- Medical machine learning operations: a framework to facilitate clinical AI development and deployment in radiology.European radiology · 2025Review
- Accelerated Abdominal MRI: A Review of Current Methods and Applications.Journal of magnetic resonance imaging : JMRI · 2025Review
- Multidisciplinary, Clinical Assessment of Accelerated Deep-Learning MRI Protocols at 1.5 T and 3 T After Intracranial Tumor Surgery and Their Influence on Residual Tumor Perception.Diagnostics (Basel, Switzerland) · 2025Article
- Compressed Sensing Reconstruction with Zero-Shot Self-Supervised Learning for High-Resolution MRI of Human Embryos.Tomography (Ann Arbor, Mich.) · 2025Article
- Accelerated brain magnetic resonance imaging with deep learning reconstruction: a comparative study on image quality in pediatric neuroimaging.Pediatric radiology · 2025Article
- MR Spatiospectral Reconstruction Integrating Subspace Modeling and Self-Supervised Spatiotemporal Denoising.IEEE transactions on medical imaging · 2025Article
- CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping.Journal of mathematical imaging and vision · 2025Article
- Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration.ArXiv · 2025Article
- MRI recovery with self-calibrated denoisers without fully-sampled data.Magma (New York, N.Y.) · 2025Article
- Computationally Efficient Implicit Training Strategy for Unrolled Networks (IMUNNE): A Preliminary Analysis Using Accelerated Real-Time Cardiac Cine MRI.IEEE transactions on bio-medical engineering · 2025Article
- Clean Self-Supervised MRI Reconstruction from Noisy, Sub-Sampled Training Data with Robust SSDU.Bioengineering (Basel, Switzerland) · 2024Article
- Multidisciplinary quantitative and qualitative assessment of IDH-mutant gliomas with full diagnostic deep learning image reconstruction.European journal of radiology open · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMagnetic resonance imaging (MRI) is an effective auxiliary diagnostic method in clinical medicine, but it has always suffered from the problem of long acquisition time. Compressed sensing and parallel imaging are two common techniques to accelerate MRI reconstruction. Recently, deep learning provides a new direction for MRI, while most of them require a large number of data pairs for training. However, there are many scenarios where fully sampled k-space data cannot be obtained, which will seriously hinder the application of supervised learning. Therefore, deep learning without fully sampled data is indispensable. MAIN TEXT: In this review, we first introduce the forward model of MRI as a classic inverse problem, and briefly discuss the connection of traditional iterative methods to deep learning. Next, we will explain how to train reconstruction network without fully sampled data from the perspective of obtaining prior information.
conclusionAlthough the reviewed methods are used for MRI reconstruction, they can also be extended to other areas where ground-truth is not available. Furthermore, we may anticipate that the combination of traditional methods and deep learning will produce better reconstruction results.
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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.