Accelerated MRI Reconstruction

MRI: Magnetic Resonance Imaging

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 42

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Next-Acceleration-Scale Prediction for Autoregressive MRI Reconstruction
May 19, 2026Yilmaz Korkmaz, Vishal M. PatelVisual Autoregressive Models Computational Imaging
Johns Hopkins University, Baltimore MD 21218, USA
Rapid online deep artifact suppression for real-time spiral bSSFP CMR with blipped-CAIPI simultaneous multi-slice imaging at 1.5 T
May 18, 2026Julius Åkesson, Iulius Dragonu, Einar Heiberg +6Accelerated MRI Reconstruction Magnetic Resonance Imaging
1: Centre for Translational Cardiovascular Imaging, Institute of Cardiovascular Science, University College London, London, United Kingdom. · 2: Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden. · 3: Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden. +1
SIREM: Speech-Informed MRI Reconstruction with Learned Sampling
May 18, 2026Md Hasan, Nyvenn Castro, Daiqi Liu +6Accelerated MRI Reconstruction Magnetic Resonance Imaging
1Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany · Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany · 3Institut für Informationsverarbeitung, Leibniz Universität Hannover, Hannover, Germany +1
CHASM: Cross-frequency Harmonized Axis-Separable Mixing for Spectral Token Operators
May 14, 2026Pengcheng Fang, Hongli Chen, Yuxia Chen +3Frequency-Domain Feature Learning Accelerated MRI Reconstruction
University of Southampton · University of Queensland · 3Chengdu University of Technology +1
NexOP: Joint Optimization of NEX-Aware k-space Sampling and Image Reconstruction for Low-Field MRI
May 12, 2026Tal Oved, Efrat ShimronImage Reconstruction Accelerated MRI Reconstruction
Department of Electrical and Computer Engineering, Technion – Israel Institute of Technology, Haifa, 3200004, Israel · May-Blum-Dahl Technion Human MRI Research Center, Technion - Israel Institute of Technology, Haifa, 3200004, Israel · Department of Biomedical Engineering, Technion – Israel Institute of Technology, Haifa, 3200004, Israel
Product-of-Gaussian-Mixture Diffusion Models for Joint Nonlinear MRI Reconstruction
May 11, 2026Laurenz Nagler, Martin Zach, Thomas PockAccelerated MRI Reconstruction Magnetic Resonance Imaging
Graz University of Technology, Institute of Visual Computing, 8010 Graz, Austria. · Ecole Polytechnique Fédérale de Lausanne, Biomedical Imaging Group and Center for Biomedical Imaging, 1015 Lausanne, Switzerland.
Model-based Dynamic 3D MRI Reconstructions using Neural Fields and Tensor Product Expansions
May 8, 2026Ray Sheombarsing, Max van Riel, David Heesterbeek +2Implicit Neural Representations 3D Medical Imaging
Computational Imaging Group for MRI Therapy & Diagnostics, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models
May 2, 2026M. Berk Sahin, Dilek Yalcinkaya, Abolfazl Hashemi +1Accelerated MRI Reconstruction Magnetic Resonance Imaging
Purdue University
Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction?
Apr 24, 2026Anam Hashmi, Mayug Maniparambil, Julia Dietlmeier +2Domain Generalization Vision Foundation Model Adaptation
1Dublin City University, Ireland · University College Dublin, Ireland
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
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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Next-Acceleration-Scale Prediction for Autoregressive MRI Reconstruction

Yilmaz Korkmaz, Vishal M. Patel

Johns Hopkins University, Baltimore MD 21218, USA

Visual Autoregressive Models Computational Imaging Accelerated MRI Reconstruction Learning Using Privileged Information

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MRI reconstruction is an inherently ill-posed inverse problem, since incomplete measurements admit many plausible solutions. This ambiguity becomes more severe under high acceleration, where pixel-domain continuous predictors tend to average over feasible reconstructions and suppress high-frequency anatomy. We address this limitation by moving reconstruction to discrete multi-scale latent space and posing it as autoregressive next-acceleration-scale prediction. Leveraging discrete priors proven effective in visual autoregressive modeling, our method restricts the solution to compact sequences of codebook tokens, enabling sharp reconstructions even from extremely sparse measurements. This discrete autoregressive formulation also aligns naturally with modern large language model post-training techniques. Building on this observation, we introduce on-policy privileged information distillation for visual autoregressive modeling, where a teacher is provided training only privileged context that is unavailable at inference, in our case fully sampled acquisitions, and supervises a student trained on its own rollouts, leading to consistent reconstruction gains. Through extensive experiments on the fastMRI benchmark, we show that our approach delivers improved reconstruction performance across diverse sampling patterns under extreme undersampling. Project website is here.

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