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.

Scalable Unrolled Chains for MRI Reconstruction
Oct 24, 2026Emiljo Mëhillaj, Ender KonukogluAccelerated MRI Reconstruction Memory-Efficient Training
Computer Vision Lab, ETH Zurich, Zurich, Switzerland · LOOP Zurich - Medical Research Center, Zurich, Switzerland
End-to-end prostate lesion segmentation and detection from undersampled diffusion-weighted MRI
Oct 24, 2026Lukas T. Rotkopf, Marco Schlimbach, Kevin Kröninger +4Medical Image Analysis Medical Image Segmentation
Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany. · Department of Physics, Technical University Dortmund, Otto-Hahn-Straße 4a, 44227, Dortmund, Germany. · Division of Radiology, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany. +6
AGGR-Mamba: Mamba-based Network with Attention-Guided Gated Residual Refinement for Undersampled MRI Reconstruction
Oct 20, 2026Yue Zhuo, Wenbin Deng, Lingjun Liu +2Accelerated MRI Reconstruction Residual Learning
School of Mathematics and Statistics, Huizhou University, Huizhou, China · School of Computer Science and Engineering, Huizhou University, Huizhou, China
LGGS: Longitudinal MRI Reconstruction via Guided Group Sparsity
Oct 15, 2026Nikola Janjušević, Hersh Chandarana, Yao Wang +1Accelerated MRI Reconstruction Longitudinal Medical Image Analysis
Radiology Department, New York University Grossman School of Medicine, New York, NY 10016 USA · Electrical and Computer Engineering Department, New York University Tandon School of Engineering, Brooklyn, NY 11201 USA
Stochastic Finite-Transition Learning and Exploration for Accelerated MRI Reconstruction
Oct 9, 2026Muhammad Usama Mirza, Esin Öztürk Işık, Tolga ÇukurAccelerated MRI Reconstruction Generative Modeling
Dept. of Electrical-Electronics Engineering and National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey, 06800 · Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey, 34684
Hankel Subspace Self-Supervised Learning for Parallel MRI Reconstruction
Oct 8, 2026Mingyu Hu, Siquan Zhu, Xijun Zhong +1Accelerated MRI Reconstruction Self-Supervised Learning
School of Information Engineering, Nanchang University, Nanchang, China · School of Mathematics and Computer Sciences, Nanchang University, Nanchang, China
Beyond Training from Scratch: Foundation Models for Data-Efficient and Generalizable Cardiac MRI Reconstruction
Oct 6, 2026Anam Hashmi, Mayug Maniparambil, Julia Dietlmeier +2Image Reconstruction Vision Foundation Model Adaptation
Research Ireland Centre for Research Training in Machine Learning, Ireland · Dublin City University, Ireland · University College Dublin, Ireland +1
Learning Subject-Specific Anatomical Representations via Manifold Expansion: Application to Accelerated Multi-Contrast MRI
Oct 2, 2026Ruimin Feng, Wanyu Bian, Albert Jang +2Accelerated MRI Reconstruction Magnetic Resonance Imaging
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States · Harvard Medical School, Boston, Massachusetts, United States · Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
Joint Supervised and Self-Supervised Training with Acquisition-Robust Techniques for Accelerated 4D Flow MRI Reconstruction
Sep 29, 2026Mengyuan Xue, Bochun Mei4D Reconstruction Accelerated MRI Reconstruction
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
SNaP: One-Step Posterior Sampling for Noisy Inverse Problems
Sep 28, 2026Shirin Shoushtari, Edward P. Chandler, Xiao Shi +1Flow Matching MeanFlow
WashU · UW-Madison
OpenMASC: An Open-Source Pipeline for Cross-Trajectory Metal-Aware Sampling and Correction in Accelerated MRI
Sep 26, 2026Zhengyi Lu, Ming Lu, Chongyu Qu +11Magnetic Resonance Imaging Accelerated MRI Reconstruction
Vanderbilt University, Nashville, TN, USA · Vanderbilt University Medical Center, Nashville, TN, USA · Oak Ridge National Laboratory, Oak Ridge, TN, USA
Awaken, Then Scale: Tiny Adaptation for MRI Reconstruction
Sep 25, 2026Mohammed Wattad, Tamir Shor, Alexander M. BronsteinDomain Adaptation Parameter-Efficient Fine-Tuning
Faculty of Computer Science, Technion-Israel Institute of Technology, Haifa, Israel
Beyond Sparsity: Weight Location and Network Context in Pruned MRI Reconstruction
Sep 25, 2026Mohammed Wattad, Tamir Shor, Alexander M. BronsteinNeural Network Pruning Accelerated MRI Reconstruction
Faculty of Computer Science, Technion - Israel Institute of Technology, Haifa, Israel
SGAM: Shared Gaussian Geometry with Implicit Amplitude Modeling for Scan-Specific 3D Multi-Contrast MRI Reconstruction
Sep 21, 2026Jingran Xu, Dong Liang, Hairong Zheng +2Accelerated MRI Reconstruction Magnetic Resonance Imaging
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
CLEAR: Complex Learned Explicit Analytical Regularization for Ultra-Accelerated 4D Flow CMR Reconstruction
Sep 19, 2026German Shâma Wache, Sebastian NeumayerAccelerated MRI Reconstruction 4D Reconstruction
Chemnitz University of Technology, Germany
Anatomically Faithful Artifact Suppression in SENSE Accelerated Brain MRI
Sep 18, 2026Changjing Chai, Bin Huang, Libo Xu +5Accelerated MRI Reconstruction Magnetic Resonance Imaging
Shanghai Key Laboratory of Magnetic Resonance, School of Physics, East China Normal University, Shanghai 200041, China · Institute of Magnetic Resonance and Molecular Imaging in Medicine, East China Normal University, Shanghai 200062, China · Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China +5
One Evaluation, Any Operating Point: Hypernetwork-Amortized MeanFlow for 3D MRI Reconstruction
Sep 15, 2026Ruibo WangAccelerated MRI Reconstruction Magnetic Resonance Imaging
Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2600 AA Delft, Netherlands
One Shared LoRA Weight for MRI Reconstruction across Acceleration Factors
Sep 6, 2026Zhiwei Zhao, Weikang Gong, Zhongnian Li +1Accelerated MRI Reconstruction Low-Rank Adaptation
School of Computer Science and Technology / School of Artificial Intelligence, China University of Mining and Technology, Xuzhou, China
Data-Efficient Networks for Multi-Contrast MRI Reconstruction based on a Generalized Content/Style Prior
Sep 2, 2026Chinmay Rao, Efe Ilıcak, Matthias J. P. van Osch +5Accelerated MRI Reconstruction Magnetic Resonance Imaging
Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands · Philips Innovative Technologies, Hamburg, Germany · Philips Cardiologs, Paris, France +1
Evaluating the Safety of Deep Learning-Based Brain MRI Reconstruction
Aug 28, 2026Dat Tat Mai, Thai Viet Pham, Thu Nguyen Thi Dang +1Medical Imaging Brain MRI
School of Science, Engineering & Technology, RMIT University Vietnam, Ho Chi Minh City, Vietnam · School of Computing Technologies, RMIT University, Melbourne, VIC 3000, Australia · School of Health and Biomedical Science, RMIT University, Melbourne, VIC 3000, Australia
FlowMoDL: Model-Based Deep Learning with Conjugate-Gradient Data Consistency for Highly Accelerated 4D Flow MRI Reconstruction
Aug 26, 2026Tristan Gottwald, Michelle Bruch, Mubashir-Ul Hassan +6Accelerated MRI Reconstruction 4D Reconstruction
Institute of Information Processing (tnt), Leibniz University Hannover, Germany · L3S, Leibniz University Hannover, Hannover, Germany · Department of Data Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany +2
Consistency Models for Fast MRI Reconstruction Using Regularization by Denoising
Aug 20, 2026Merve Gülle, Junno Yun, Yaşar Utku Alçalar +1Accelerated MRI Reconstruction Diffusion-Based Inverse Problems
Department of Electrical and Computer Engineering, University of Minnesota, MN, USA · Center for Magnetic Resonance Research, University of Minnesota, MN, USA
Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction
Aug 18, 2026Veronika Spieker, Wenqi Huang, Cemre Ariyurek +5Magnetic Resonance Imaging Accelerated MRI Reconstruction
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany · School of Computation and Information Technology, Technical University of Munich, Germany · Department of Radiology, Boston Children’s Hospital and Harvard Medical School, USA +3
Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors
Aug 18, 2026Mahdi Saberi, Yaşar Utku Alçalar, Merve Gülle +2Accelerated MRI Reconstruction Magnetic Resonance Imaging
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA · Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA · Department of Medicine (Cardiology), University of Minnesota, Minneapolis, MN, USA
UMPIRE-Net: Unrolled Magnitude-Phase Regularization Network for Accelerated MRI
Aug 14, 2026Mahdi Saberi, Toygan Kiliç, Mehmet AkçakayaAccelerated MRI Reconstruction Physics-Informed ML
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA · Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
PE-CSNet: An equivariant network architecture with learnable patch-based sparse representation
Aug 11, 2026Kai Li, Haitao Long, Bo Zhang +2Deep Unfolding Compressed Sensing
Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong. · SKLMS and Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China and School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China · SKLMS and Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
K-space Gaussian Representation for Parallel MRI
Jul 29, 2026Yu Guan, Mingyu Hu, Jiale Hu +3Accelerated MRI Reconstruction Magnetic Resonance Imaging
School of Advanced Manufacturing and the School of Information Engineering, Nanchang University, Nanchang 330031, China · Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen 518055, China · School of Information Engineering, Nanchang University, Nanchang 330031, China +1
SHFormer: Dynamic Spectral Filtering Convolutional Neural Network and High-pass Kernel Generation Transformer for Adaptive MRI Reconstruction
Jul 22, 2026Sriprabha Ramanarayanan, Rahul G. S., Mohammad Al Fahim +3Hybrid CNN-Transformer Architectures Frequency-Domain Feature Learning
Healthcare Technology Innovation Centre, IITM, India · Department of Electrical Engineering, Indian Institute of Technology Madras (IITM), India · GE John F Welch Technology Center, GE Healthcare, India
Frequency-Hierarchical Active k-Space Sampling for Diagnostic MRI
Jul 22, 2026Ruru Xu, Kian Anvari Hamedani, Zhikai Yang +1Active Learning Accelerated MRI Reconstruction
Computer Engineering Department, Istanbul Technical University, Istanbul, Turkey · Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada · Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada +1
Enhancing Implicit Neural Representations with Image Feature Embedding for Unsupervised Cardiac Cine MRI Reconstruction
Jul 5, 2026Donghang Lyu, Marius Staring, Yiming Dong +3Implicit Neural Representations Accelerated MRI Reconstruction
Division of Image Processing, Department of Radiology, Leiden University Medical Center, 2333ZA Leiden, Netherlands · C.J. Gorter Center, Department of Radiology, Leiden University Medical Center, 2333ZA Leiden, Netherlands · Philips Innovative Technologies, 22335 Hamburg, Germany +1

Oct 24, 2026J/K move · Enter open · S save

Scalable Unrolled Chains for MRI Reconstruction

Emiljo Mëhillaj, Ender Konukoglu

Computer Vision Lab, ETH Zurich, Zurich, Switzerland · LOOP Zurich - Medical Research Center, Zurich, Switzerland

Accelerated MRI Reconstruction Memory-Efficient Training

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Unrolled variational networks achieve state-of-the-art performance for accelerated MRI reconstruction, but standard end-to-end training becomes prohibitively memory intensive as the number and width of cascades increase. We propose a memory-efficient training framework that updates a randomly sampled cascade while retaining supervision from the final reconstruction loss. The error signal is propagated from the end of the chain to the sampled cascade using an activation-light backward pass that avoids storing full chain activations, thereby decoupling training memory from chain length. A time-conditioned network shared across cascades further keeps the parameter count independent of chain length. The backward pass is approximate only through frozen normalization statistics, and a trained exact gradient control shows the approximation leaves reconstruction quality unchanged. The framework remains trainable at chain lengths and cascade widths where standard end-to-end backpropagation exhausts GPU memory. On fastMRI single-coil knee and multi-coil brain reconstruction, our method consistently outperforms other local training approaches. On high-SNR knee slices, our method surpasses E2E-VarNet at

8\times

and matches it at

4\times

, while on the complete brain validation set it surpasses E2E-VarNet at

8\times

and achieves comparable performance at

4\times

, with fewer parameters, lower peak memory, and faster per-iteration training. Code is available at https://github.com/emehillaj/scalable-unrolled-mri.