Magnetic Resonance Imaging

Also known as MRI

Magnetic resonance imaging research studies how to acquire, reconstruct, interpret, and quantify images of the body, from structural brain scans to cardiac motion and tissue microstructure. Approaches include physics-based and compressed-sensing reconstruction, deep unrolled networks, diffusion models and implicit representations, alongside methods for segmentation, diagnosis, image synthesis, and scanner harmonization. Work increasingly combines these tools with acquisition physics, multimodal or longitudinal data, and uncertainty estimates to accelerate scans and support clinical measurements. A central challenge is ensuring that reconstructed or synthesized detail is genuinely supported by the measurements and that models remain reliable across scanners, field strengths, protocols, and patient populations.

Latest papers

258 papers26 papers in the last four weeks, level with the four weeks before. 0.2% of all new papers.

SFL-Net: Source-Factorized Latent Representation Learning for Multi-Contrast MRI to Tau-PET Synthesis
Feb 26, 2026Agamdeep S. Chopra, Caitlin Neher, Tianyi Ren +3Disentangled Representation Learning Multimodal Disentangled Representation Learning
Department of Mechanical Engineering, University of Washington, Seattle, WA, USA · Department of Radiology, University of Washington, Seattle, WA, USA
Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries
Feb 25, 2026Joshua Schulz, David Schote, Christoph Kolbitsch +2Accelerated MRI Reconstruction Sparse Recovery
Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany · School of Mathematical Sciences, Queen Mary University of London, UK
IHF-Harmony: Multi-Modality Magnetic Resonance Images Harmonization using Invertible Hierarchy Flow Model
Feb 25, 2026Pengli Zhu, Yitao Zhu, Haowen Pang +1Medical Image Harmonization Magnetic Resonance Imaging
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong · School of Integrated Circuits and Electronics, Beijing Institute of Technology, China · Mental Health Research Center, The Hong Kong Polytechnic University, Hong Kong +1
M-Gaussian: An Magnetic Gaussian Framework for Efficient Multi-Stack MRI Reconstruction
Feb 24, 2026Kangyuan Zheng, Xuan Cai, Jiangqi Wang +6Magnetic Resonance Imaging 3D Gaussian Splatting
School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China. · the School of Information Science and Engineering, Shandong Normal University, Shandong, China. · School of Computing and Data Science, The University of Hong Kong, Hong Kong, China.
OrthoDiffusion: A Generalizable Multi-Task Diffusion Foundation Model for Musculoskeletal MRI Interpretation
Feb 24, 2026Tian Lan, Lei Xu, Zimu Yuan +8Diffusion Models for Medical Image Segmentation Medical Imaging Foundation Models
Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China. · Department of Sports Medicine, Peking University Third Hospital, Institute of Sports Medicine of Peking University, Beijing, China. · Beijing Key Laboratory of Research and Translation for Drugs and Medical Devices in Precision Diagnosis and Treatment of Sports Injuries, Beijing, China. +4
The Sim-to-Real Gap in MRS Quantification: A Systematic Deep Learning Validation for GABA
Feb 23, 2026Zien Ma, S. M. Shermer, Oktay Karakuş +1Magnetic Resonance Spectroscopy Sim-to-Real Transfer
School of Computer Science and Informatics, Cardiff University, Cardiff, CF24 4AG, United Kingdom · Faculty of Science and Engineering, Swansea University, Swansea, SA2 8PP, United Kingdom
Towards Personalized Multi-Modal MRI Synthesis across Heterogeneous Datasets
Feb 23, 2026Yue Zhang, Zhizheng Zhuo, Siyao Xu +6Medical Image Synthesis Missing-Modality Learning
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, China · Center for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE), Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, China · National Key Laboratory of Intelligent Collaborative Computing, University of Electronic Science and Technology of China, Chengdu, 610097, China +6
MRI Contrast Enhancement Kinetics World Model
Feb 22, 2026Jindi Kong, Yuting He, Cong Xia +2Magnetic Resonance Imaging Medical Image Synthesis
Case Western Reserve University, Cleveland, OH, USA · Jiangsu Cancer Hospital, Nanjing, Jiangsu, China · Southeast University, Nanjing, Jiangsu, China
Distributional Deep Learning for Super-Resolution of 4D Flow MRI under Domain Shift
Feb 16, 2026Xiaoyi Wen, Fei JiangMedical Image Super-Resolution Magnetic Resonance Imaging
Department of Epidemiology and Biostatistics, University of California, San Francisco
Multiparameter Uncertainty Mapping in Quantitative Molecular MRI using a Physics-Structured Variational Autoencoder (PS-VAE)
Feb 3, 2026Alex Finkelstein, Ron Moneta, Or Zohar +4Magnetic Resonance Imaging Uncertainty Quantification
School of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, Israel · Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel · School of Biochemistry, Neurobiology, and Biophysics, Tel-Aviv University, Tel-Aviv, Israel +1
MASC: Metal-Aware Sampling and Correction via Reinforcement Learning for Accelerated MRI
Jan 30, 2026Zhengyi Lu, Ming Lu, Chongyu Qu +11Magnetic Resonance Imaging Accelerated MRI Reconstruction
Vanderbilt University, Nashville, TN, USA 37215 · Vanderbilt University Medical Center, Nashville, TN, USA 37232 · Oak Ridge National Laboratory, Oak Ridge, TN, USA 37831
Accelerated MR Elastography Using Learned Neural Network Representation
Jan 17, 2026Xi PengRepresentation Learning Accelerated MRI Reconstruction
Departments of Radiology, Biomedical Engineering, Electronic and Computer Engineering, and Mechanical Engineering, University of Iowa, Iowa City, IA 52246 · Department of Radiology, University of Iowa, Iowa City, IA, 52246
SRUG: A Fusion-Driven Generator Network for Medical Image Translation
Jan 8, 2026Xihe Qiu, Yang Dai, Xiaoyu Tan +4Medical Image Synthesis Image-to-Image Translation
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China · Department of Thoracic Surgery, Zhongshan Hospital of Fudan University, Shanghai 200032, China · Department of Medical Oncology, Cancer Center, and Fudan Zhangjiang Institute, Zhongshan Hospital of Fudan University, Shanghai 200032, China +1
Equivariant symmetry-aware head pose estimation for fetal MRI
Dec 4, 2025Ramya Muthukrishnan, Borjan Gagoski, Aryn Lee +4Symmetry-Aware Object Pose Estimation Object Pose Estimation
MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, USA · Boston Children’s Hospital and Harvard Medical School, Boston, USA · Inria, Université Côte d’Azur, Sophia Antipolis, France
End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRI
Sep 15, 2025Yihong Chen, Jiancheng Yang, Deniz Sayin Mercadier +34D Reconstruction 3D Reconstruction
School of Computer and Communication Science, EPFL · ELLIS Institute Finland, Aalto University, Finland · University of North Carolina at Charlotte +1
Reduced NEXI protocol for the quantification of human gray matter microstructure on the Connectome 2.0 scanner
Sep 11, 2025Quentin Uhl, Tommaso Pavan, Julianna Gerold +10Magnetic Resonance Imaging Neuroimaging
Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland · Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, USA
Converting T1-weighted MRI from 3T to 7T quality using deep learning
Jul 18, 2025Malo Gicquel, Ruoyi Zhao, Anika Wuestefeld +12Magnetic Resonance Imaging Medical Image Synthesis
Department of Clinical Sciences Malmö, SciLifeLab, Lund University, Lund, Sweden · Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, France · Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden +8
ArteryX: A Reliable End-to-End Toolbox for Standardized Intracranial Artery Feature Extraction from 3D TOF-MRA
Jul 10, 2025Abrar Faiyaz, Nhat Hoang, Giovanni Schifitto +1Medical Image Analysis Magnetic Resonance Imaging
Department of Neurology, University of Rochester, Rochester, NY 14642, USA · Department of Physics, University of Rochester, Rochester, NY 14627, USA · Department of Imaging Sciences, University of Rochester, Rochester, NY 14642, USA +2

Feb 26, 2026J/K move · Enter open · S save

SFL-Net: Source-Factorized Latent Representation Learning for Multi-Contrast MRI to Tau-PET Synthesis

Agamdeep S. Chopra, Caitlin Neher, Tianyi Ren, Juampablo E. Heras Rivera+2

Department of Mechanical Engineering, University of Washington, Seattle, WA, USA · Department of Radiology, University of Washington, Seattle, WA, USA

Disentangled Representation Learning Multimodal Disentangled Representation Learning Magnetic Resonance Imaging Medical Image Synthesis Explainable Medical Image Analysis

Open paper AskPDF arXiv

Tau positron emission tomography supports Alzheimer's disease staging but is difficult to scale because of tracer, scanner, and radiation constraints. Synthesis from structural MRI is therefore attractive, but it is a particularly difficult setting. T1-weighted and FLAIR MRI provide anatomy and disease correlated morphology, but they do not directly measure Tau-PET relevant signal. We introduce SFL-Net, a multi-input synthesis framework that predicts Tau-PET from T1-weighted and FLAIR MRI. SFL-Net factorizes the latent representation into shared, T1-specific, FLAIR-specific, and complementary pathways and preserves anatomical detail through latent structural conditioning rather than direct encoder-decoder connections. We evaluated SFL-Net and baseline models using 605 training and 83 validation subjects from ADNI-3 and OASIS-3 datasets. Evaluation included raw image fidelity, standardized uptake value ratio agreement, high uptake overlap, regional Bland-Altman bias, braak derived stage agreement, non-inferiority sensitivity analysis, and latent component Shapley attribution. SFL-Net performed competitively on both clinically relevant and reconstruction metrics, while also delivering explicit source level auditability that conventional UNet derived models lack.