Accelerated MRI Reconstruction

MRI: Magnetic Resonance Imaging

The field studies how to reconstruct MRI images from fewer k-space measurements, reducing scan time while retaining structures and signals needed for diagnosis. Methods range from compressed sensing and parallel-coil models to unrolled networks, implicit neural representations, and diffusion or flow-based priors, often enforcing consistency with acquired measurements. Researchers also learn acquisition patterns and develop techniques for dynamic, multi-contrast, low-field, and motion-affected MRI. A central challenge is balancing speed and image quality with generalization across scanners and patients, while detecting hallucinations and estimating when a reconstruction is unreliable.

Latest papers

85 papers10 papers in the last four weeks, up 11% on the four weeks before. 0.1% of all new papers.

Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging
Apr 14, 2026Xinyu Peng, Ziyang Zheng, Wenrui Dai +5Magnetic Resonance Imaging Compressed Sensing
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. · Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. · Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China. +1
MosaicMRI: A Diverse Dataset and Benchmark for Raw Musculoskeletal MRI
Apr 13, 2026Paula Arguello, Berk Tinaz, Mohammad Shahab Sepehri +2Magnetic Resonance Imaging Medical Image Benchmarks
Department of Computer Science, University of Southern California · USC Center on AI Foundations for the Sciences · Department of Electrical and Computer Engineering, University of Southern California +1
PSIRNet: Deep Learning-based Free-breathing Rapid Acquisition Late Enhancement Imaging
Apr 9, 2026Arda Atalik, Hui Xue, Rhodri H. Davies +4Image Reconstruction Cardiac MRI
Health Futures, Microsoft Research, Redmond, WA, USA · Center for Data Science, New York University, New York, NY, USA · Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA +3
Vision-Language Model-Guided Deep Unrolling Enables Personalized, Fast MRI
Apr 8, 2026Fangmao Ju, Yuzhu He, Zhiwen Xue +2Magnetic Resonance Imaging Accelerated MRI Reconstruction
School of Mathematics and Statistics, Xi’an Jiaotong University, No.28, Xianning West Road, Xi’an, 710049, Shaanxi, China. · Research Center for Intelligent Medical Equipment and Devices (IMED), Xi’an Jiaotong University, No.28, Xianning West Road, Xi’an, 710049, Shaanxi, China. · Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, No.28, Xianning West Road, Xi’an, 710049, Shaanxi, China.
Training deep learning based dynamic MR image reconstruction using synthetic fractals
Mar 31, 2026Anirudh Raman, Olivier Jaubert, Mark Wrobel +8Image Reconstruction Magnetic Resonance Imaging
UCL Centre for Translational Cardiovascular Imaging, University College London, 30 Guilford St, London, WC1N 1EH, UK · Department of Cardiology, Royal Free London NHS Foundation Trust, London, NW3 2QG, UK · Institute of Cardiovascular Science, University College London, London, WC1E 6HX, UK
Weakly Convex Ridge Regularization for 3D Non-Cartesian MRI Reconstruction
Mar 28, 2026German Shâma Wache, Chaithya G R, Asma Tanabene +1Accelerated MRI Reconstruction Magnetic Resonance Imaging
Faculty of Mathematics, Chemnitz University of Technology, Germany · NeuroSpin, Frédéric Joliot Institute for Life Sciences, CEA Paris-Saclay, France · MIND, Inria Saclay Centre, France +1
Anatomical Token Uncertainty for Transformer-Guided Active MRI Acquisition
Mar 23, 2026Lev Ayzenberg, Shady Abu-Hussein, Raja Giryes +1Magnetic Resonance Imaging Accelerated MRI Reconstruction
Tel Aviv University, Faculty of Engineering, Tel Aviv, Israel · University of Cambridge, Department of Engineering, Cambridge, UK
UEPS: Robust and Efficient MRI Reconstruction
Mar 19, 2026Xiang Zhou, Hong Shang, Zijian Zhan +3Accelerated MRI Reconstruction OOD Generalization
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China · School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, China · University of Chinese Academy of Sciences, China +2
Unrolled Reconstruction with Integrated Super-Resolution for Accelerated 3D LGE MRI
Mar 18, 2026Md Hasibul Husain Hisham, Shireen Elhabian, Ganesh Adluru +5Accelerated MRI Reconstruction Cardiac MRI
Kahlert School of Computing, University of Utah, Salt Lake City, UT, USA · Radiology & Imaging Sciences, University of Utah, Salt Lake City, UT, USA · Cardiology, University of Utah, Salt Lake City, UT, USA
Low-Field Magnetic Resonance Image Enhancement using Undersampled k-Space
Mar 14, 2026Daniel Tweneboah Anyimadu, Mohammed Abdalla, Mohammed M. Abdelsamea +1Magnetic Resonance Imaging Medical Image Super-Resolution
Department of Computer Science, University of Exeter, United Kingdom · Neurology Department, Royal Devon and Exeter Hospital, Exeter, United Kingdom · UCL Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom
Low-Field Magnetic Resonance Image Quality Enhancement using Undersampled k-Space and Out-of-Distribution Generalisation
Mar 14, 2026Daniel Tweneboah Anyimadu, Mohammed M. Abdelsamea, Ahmed Karam EldalyMagnetic Resonance Imaging Accelerated MRI Reconstruction
Department of Computer Science, University of Exeter, Exeter, United Kingdom · UCL Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom
Open World MRI Reconstruction with Bias-Calibrated Adaptation
Mar 13, 2026Jiyao Liu, Shangqi Gao, Lihao Liu +5Image Reconstruction Domain Adaptation
Fudan University, Shanghai, China · University of Cambridge, Cambridge, United Kingdom · Shanghai Artificial Intelligence Laboratory, Shanghai, China
Accelerating Stroke MRI with Diffusion Probabilistic Models through Large-Scale Pre-training and Target-Specific Fine-Tuning
Mar 13, 2026Yamin Arefeen, Sidharth Kumar, Steven Warach +2Accelerated MRI Reconstruction Magnetic Resonance Imaging
Chandra Family Department of Electrical and Computer Engineering The University of Texas at Austin Austin, TX, USA · Department of Imaging Physics MD Anderson Cancer Center Houston, TX, USA · Dell Medical School Department of Neurology The University of Texas at Austin Austin, TX, USA +3
Bayesian Uncertainty-Aware MRI Reconstruction
Mar 13, 2026Ahmed Karam Eldaly, Matteo Figini, Daniel C. AlexanderTV Regularization Accelerated MRI Reconstruction
Department of Computer Science, University of Exeter, UK · UCL Hawkes Institute, Department of Computer Science, University College London, UK
MRI2Qmap: multi-parametric quantitative mapping with MRI-driven denoising priors
Mar 11, 2026Mohammad Golbabaee, Matteo Cencini, Carolin Pirkl +3Magnetic Resonance Imaging Accelerated MRI Reconstruction
University of Bristol · IRCCS Fondazione Stella Maris · GE Healthcare +2
Transferable Optimization Network for Cross-Domain Image Reconstruction
Mar 8, 2026Yunmei Chen, Chi Ding, Xiaojing YeImage Reconstruction Cross-Domain Transfer Learning
Department of Mathematics, University of Florida, Gainesville, FL 32611, USA. · Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303, USA.
Gabor Primitives for Accelerated Cardiac Cine MRI Reconstruction
Mar 5, 2026Wenqi Huang, Veronika Spieker, Nil Stolt-Ansó +6Accelerated MRI Reconstruction Cardiac MRI
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany · School of Computation and Information Technology, Technical University of Munich, Munich, Germany · Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Neuherberg, Germany +5
MPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstruction
Mar 4, 2026Seunghoi Kim, Chen Jin, Henry F. J. Tregidgo +2Cross-Modal Alignment Multimodal Pretraining
University College London, UK · Centre for AI, DS&AI, AstraZeneca, UK
Revisiting Global Token Mixing in Task-Dependent MRI Restoration: Insights from Minimal Gated CNN Baselines
Mar 2, 2026Xiangjian Hou, Chao Qin, Chang Ni +3Accelerated MRI Reconstruction Convolutional Neural Networks
Dept. of Radiology & Imaging Sciences, University of Utah, Salt Lake City, UT, USA · Dept. of Electrical & Computer Engineering, University of Utah, Salt Lake City, UT, USA · Dept. of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE +2
Direct low-field MRI super-resolution using undersampled k-space
Feb 28, 2026Daniel Tweneboah Anyimadu, Mohammed M. Abdelsamea, Ahmed Karam EldalyMagnetic Resonance Imaging Medical Image Super-Resolution
Department of Computer Science, University of Exeter, Exeter, United Kingdom · UCL Hawkes Institute, University College London, London, United Kingdom
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
CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction
Feb 20, 2026Qingyong Zhu, Yumin Tan, Xiang Gu +1Accelerated MRI Reconstruction Generative Modeling
Medical AI Research Centre, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China · School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, Guangdong, China · School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, China +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
Pixelwise Uncertainty Quantification of Accelerated MRI Reconstruction
Jan 19, 2026Ilias I. Giannakopoulos, Lokesh B Gautham Muthukumar, Yvonne W. Lui +1Uncertainty Quantification Accelerated MRI Reconstruction
Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, 10016, New York, NY, United States of America · Courant Institute of Mathematical Sciences, NYU, 10012, New York, NY, United States of America · Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Grossman School of Medicine, 10016, New York, NY, United States of America
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

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Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging

Xinyu Peng, Ziyang Zheng, Wenrui Dai, Duoduo Xue+4

Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. · Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. · Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China.+1

Magnetic Resonance Imaging Compressed Sensing Accelerated MRI Reconstruction Adaptive Sampling Uncertainty Quantification Mutual Information Maximization

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Task-adapted compressed sensing magnetic resonance imaging (CS-MRI) is emerging to address the specific demands of downstream clinical tasks with significantly fewer k-space measurements than required by Nyquist sampling. However, existing task-adapted CS-MRI methods suffer from the uncertainty problem for medical diagnosis and cannot achieve adaptive sampling in end-to-end optimization with reconstruction or clinical tasks. To address these limitations, we propose the first task-adapted CS-MRI from the information-theoretic perspective to simultaneously achieve probabilistic inference for uncertainty prediction and adapt to arbitrary sampling ratios and versatile clinical applications. Specifically, we formalize the task-adapted CS-MRI optimization problem by maximizing the mutual information between undersampled k-space measurements and clinical tasks to enable probabilistic inference for addressing the uncertainty problem. We leverage amortized optimization and construct tractable variational bounds for mutual information to jointly optimize sampling, reconstruction, and task-inference models, which enables flexible sampling ratio control using a single end-to-end trained model. Furthermore, the proposed framework addresses two kinds of distinct clinical scenarios within a unified approach, i.e., i) joint task and reconstruction, where reconstruction serves as an auxiliary process to enhance task performance; and ii) task implementation with suppressed reconstruction, applicable for privacy protection. Extensive experiments on large-scale MRI datasets demonstrate that the proposed framework achieves highly competitive performance on standard metrics like Dice compared to deterministic counterpart but provides better distribution matching to the ground-truth posterior distribution as measured by the generalized energy distance (GED).