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.

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Latest papers 42

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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
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
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
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
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
Piecewise Dynamic Diffusion Regularization for Reconstruction of Cardiac Cine MRI
Jul 3, 2026Florian Fürnrohr, Reinhard HeckelImage Reconstruction Accelerated MRI Reconstruction
Technical University of Munich · Munich Center for Machine Learning (MCML)
Self-Auditing Residual Drifting for Pathology-Preserving Accelerated Knee MRI
Jul 2, 2026Qing Lyu, Jianxu Wang, Mohammad Kawas +2Uncertainty Quantification Image Reconstruction
Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510 USA · Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
Jul 1, 2026Yu Guan, Tianjia Huang, Qinrong Cai +3Diffusion Models for Image Restoration Accelerated MRI Reconstruction
Measured-Subspace Consistency: A Plug-and-Play Operator for Diffusion Posterior Sampling in Accelerated MRI Reconstruction
Jun 26, 2026Junhyeok Lee, Kyu Sung ChoiBayesian Inverse Problems Accelerated MRI Reconstruction
Interdisciplinary Program in Cancer Biology, Seoul National University College of Medicine · Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital · Healthcare AI Research Institute, Seoul National University Hospital
TensorLDM: A Component-Wise Latent Diffusion Model for Volumetric DTI Reconstruction from Sparse DWIs
Jun 24, 2026Junhyeok Lee, Kyu Sung ChoiLatent Diffusion Models Accelerated MRI Reconstruction
Interdisciplinary Program in Cancer Biology, Seoul National University College of Medicine · Department of Radiology, Seoul National University Hospital · Department of Radiology, Seoul National University College of Medicine +1
Deep Unrolled Networks in Representation Space Applied to MRI Reconstruction
Jun 19, 2026Efe Ilıcak, Baris Imre, Chloé Najac +4Accelerated MRI Reconstruction Image Inverse Problems
Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
Trustworthy MRI Reconstruction via Bayesian Uncertainty Quantification with Sparsity Prior Models
Jun 15, 2026Ahmed Karam Eldaly, Matteo Figini, Daniel C. AlexanderSparse Recovery Uncertainty Quantification
Department of Computer Science, University of Exeter, Exeter EX4 4QF, U.K. · UCL Hawkes Institute, Department of Computer Science, University College London, London, U.K.
Variational Network with Wavelet-based UNET in Accelerated MRI Reconstruction from Under Sampled K-space Data
Jun 13, 2026Yasir Arafat Prodhan, Shaikh Anowarul FattahU-Net Wavelet Methods
Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh · This paper was produced by the IEEE Publication Technology Group. They are in Piscataway, NJ.
Physics-Driven Zero-Shot MRI Reconstruction with Non-local Image Priors
Jun 13, 2026Lingtong Zhang, Wenlei Li, Mu He +2Self-Supervised Learning Image Reconstruction
School of Information Science and Technology, University of Science and Technology of China, Hefei, China
Contrast-Informed Augmentation and Domain-Adversarial Training for Adult-to-Neonatal MR Reconstruction Generalization
Jun 11, 2026Stephen Moore, Lara Leijser, Richard Frayne +1Adversarial Training Data Augmentation
Biomedical Engineering, University of Calgary, Calgary, Alberta · Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta · Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Alberta +2
DD-INR: Dynamics-Driven Implicit Neural Representation for Accelerated Whole-Brain Functional MRI Reconstruction
Jun 9, 2026Qiaoxin Li, Caini Pan, Pierre-Antoine Comby +2Implicit Neural Representations Accelerated MRI Reconstruction
MIND · MIND, Inria, Palaiseau, France · Neurospin, CEA Paris Saclay, France +3
L-TGVN: Leveraging Longitudinal Priors for Personalized Rapid MRI
Jun 3, 2026Arda Atalık, Sumit Chopra, Daniel K. SodicksonAccelerated MRI Reconstruction Magnetic Resonance Imaging
NYU Center for Data Science, NY, USA · Center for Advanced Imaging Innovation and Research (CAI²R), Department of Radiology, NYU Grossman School of Medicine, NY, USA · Courant Institute of Mathematical Sciences, NY, USA +1
Prospective Dynamic 3D MRI Reconstruction via Latent-Space Motion Tracking from Single Measurement
Jun 2, 2026Lixuan Chen, Zhongnan Liu, Jesse Hamilton +3Accelerated MRI Reconstruction Magnetic Resonance Imaging
University of Michigan
Measurement Geometry and Design for Trustworthy Generative Inverse Problems
Jun 1, 2026Pengfei Jin, Na Li, Quanzheng LiComputational Imaging Accelerated MRI Reconstruction
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114 · School of Engineering and Applied Sciences, Harvard University, Boston, MA 02138
MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstruction
May 29, 2026Yinzhe Wu, Fanwen Wang, Zhenxuan Zhang +3Implicit Neural Representations Mixture of Experts
Department of Bioengineering and I-X, Imperial College London, London, SW7 2AZ, United Kingdom · Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, United Kingdom · Shanghai Pudong Hospital and Human Phenome Institute, Fudan University, Shanghai, China +3
Geometry-Correct Diffusion Posterior Sampling with Denoiser-Pullback Curvature Guidance and Manifold-Aligned Damping
May 27, 2026Seunghyeok Shin, Minwoo Kim, Dabin Kim +1Gauss-Newton Optimization Bayesian Inverse Problems
Department of Electrical and Computer Engineering, Inha University, Incheon, 22212, South Korea.
CoilDrop-MRI: Self-supervised physics-guided MRI reconstruction with coil dropout
May 26, 2026Tongxi Song, Ziyu Li, Zihan Li +6Image Reconstruction Accelerated MRI Reconstruction
School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University, Beijing, China · Oxford Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK · Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States +1
Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications
May 22, 2026Roman Pavelkin, Luis A. Zavala-Mondragon, Christiaan G. A. Viviers +1Flow Matching Image Reconstruction
Eindhoven University of Technology, Eindhoven
MotionDPS: Motion-Compensated 3D Brain MRI Reconstruction
May 21, 2026Antonio Ortiz-Gonzalez, Erich Kobler, Lukas Schletter +1Accelerated MRI Reconstruction Magnetic Resonance Imaging
Life and Medical Sciences Institute, University of Bonn, Bonn, Germany · Institute for Machine Learning, LIT AI Lab, Department of Virtual Morphology, Clinical Research Institute Medical AI, Johannes Kepler University Linz, Linz, Austria · German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany +1
SO-Mamba: State-Ownership Mamba for Unrolled MRI Reconstruction
May 21, 2026Pengcheng Fang, Hongli Chen, Fangfang Tang +3Mamba Learning to Optimize
University of Southampton · University of Queensland · Soochow University
Motion-Robust Deep Reconstruction for Free-Breathing Cardiac Cine MRI
May 20, 2026Mahmut Yurt, Kanghyun Ryu, Zhitao Li +8Accelerated MRI Reconstruction Magnetic Resonance Imaging
Stanford University · KIST · GE Healthcare

Oct 6, 2026, cs.CVJ/K move · Enter open · S save

Beyond Training from Scratch: Foundation Models for Data-Efficient and Generalizable Cardiac MRI Reconstruction

Anam Hashmi, Mayug Maniparambil, Julia Dietlmeier, Kathleen M. Curran+1

Research Ireland Centre for Research Training in Machine Learning, Ireland · Dublin City University, Ireland · University College Dublin, Ireland+1

Image Reconstruction Vision Foundation Model Adaptation Accelerated MRI Reconstruction Cardiac MRI

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Cardiac magnetic resonance imaging reconstruction aims to recover high-quality images from undersampled acquisitions, enabling faster scans while preserving diagnostic fidelity. Recent reconstruction methods are typically trained from scratch and often require large amounts of task-specific data, limiting their robustness under data scarcity and distribution shifts. In this work, we investigate whether pretrained vision foundation models can serve as effective priors for accelerated cardiac MRI reconstruction. We propose a reconstruction framework that integrates frozen and parameter-efficiently adapted visual encoders, including CLIP, BiomedCLIP, and DINOv2, within a transformer-based reconstruction architecture. Extensive experiments on the CMRxRecon2023 and CMRxRecon2024 benchmarks demonstrate that pretrained representations consistently outperform a transformer trained from scratch across multiple acceleration factors. We further evaluate performance under limited supervision and cross-dataset transfer, showing that foundation models provide superior data efficiency and generalization. While frozen representations are particularly effective in extreme low-data regimes, Low-Rank Adaptation (LoRA) yields additional gains when moderate amounts of training data are available. Among the evaluated backbones, DINOv2 achieves the strongest overall performance. These findings highlight the potential of vision foundation models as robust and transferable priors for cardiac MRI reconstruction.

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