Diffusion MRI

MRI: Magnetic Resonance Imaging

Diffusion MRI uses the direction and magnitude of water movement to estimate tissue properties, map white-matter pathways, and detect abnormalities such as stroke lesions. Common approaches include diffusion tensor and multi-compartment models, tractography, and machine-learning methods for estimating microstructure from measured signals. Current work also improves denoising, motion and distortion correction, reconstruction from sparse acquisitions, and harmonization across scanners and protocols. A central challenge is making these methods generalize across people and acquisition settings while preserving measurement consistency and the reliability of downstream biological and clinical conclusions.

Latest papers

51 papers8 papers in the last four weeks, up 60% on the four weeks before. 0.0% of all new papers.

Lesion-DDPM: Lesion-Enhanced 3D Diffusion for MS MRI Synthesis
Jun 13, 2026Weidong Zhang, Yongchan Jung, Shafayat Mowla Anik +6Medical Image Synthesis Diffusion MRI
Northeastern University, Vancouver, BC · Fairleigh Dickinson University, Vancouver, BC · University of Colorado at Colorado Springs, Colorado Springs, CO +3
Tractogram foundation model
Jun 3, 2026Guikun Chen, Yuqian Chen, Yijie Li +5Medical Imaging Foundation Models Representation Learning
The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou 310058, Zhejiang, China · Department of Radiology, Brigham and Women’s Hospital, Mass General Brigham, Boston 02115, Massachusetts, USA · Harvard Medical School, Boston 02115, Massachusetts, USA +4
An Attention-Based Denoising Model for Diffusion Weighted Imaging
Jun 2, 2026Prithviraj Verma, Pawan Kumar, Chandan Deshani +1Magnetic Resonance Imaging Diffusion MRI
Institute of Infrastructure Technology Research and Management (IITRAM), India · University of Sheffield, United Kingdom
Realistic noise synthesis reduces bias and improves tissue microstructure estimation with supervised machine learning
Jun 1, 2026Bradley G. Karat, Maëliss Jallais, Ali R. Khan +3Parameter Estimation Distribution Shift
Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Canada · Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, New York, New York, USA · Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom +3
Wavelet-Fusion Diffusion Model for Multimodal Brain MRI Synthesis with Modality and Metadata Conditioning
May 30, 2026Muhammad Nabi Yasinzai, Remika Mito, Mangor PedersenVariational Autoencoders Latent Diffusion Models
Department of Psychology & Neuroscience, Auckland University of Technology, Auckland, New Zealand. · Department of Psychiatry, University of Melbourne, Melbourne, Victoria 3053, Australia. · Auckland University of Technology, Auckland, New Zealand.
AutoIQ: An Ensemble Framework for Automatic Assessment of Geometric Distortion in Prostate Diffusion-Weighted Imaging
May 29, 2026Haoran Sun, Lixia Wang, Yin-Chen Hsu +13Medical Imaging Magnetic Resonance Imaging
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA · Siemens Medical Solutions USA Inc., Los Angeles, CA, USA · Siemens Healthineers AG, Forchheim, Germany +5
A physics-informed foundation model for quantitative diffusion MRI
May 29, 2026Zihan Li, Jialan Zheng, Ziyu Li +18Physics-Informed Diffusion Models Medical Imaging Foundation Models
School of Biomedical Engineering, Tsinghua University, Beijing, China. · Oxford Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom. · Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China. +4
Layer Selection in Feature-Based Losses Affects Image Quality and Microstructural Consistency in Deep Learning Super-Resolution of Brain Diffusion MRI
May 15, 2026David Lohr, Rene WernerPerceptual Loss Medical Image Super-Resolution
Institue for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, 20246, Germany · Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany · Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
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.
Self-Supervised Spatial And Zero-Shot Angular Super-Resolution by Spatial-Angular Implicit Representation For Rotating-View SNR-Efficient Diffusion MRI
May 4, 2026Yinzhe Wu, Hongyu Rui, Fanwen Wang +3Implicit Neural Representations Medical Image Super-Resolution
Department of Bioengineering and I-X, Imperial College London, London, UK · Cardiovascular Research Centre, Royal Brompton Hospital, London, UK
PRISM: Differentiable Analysis-by-Synthesis for Fixel Recovery in Diffusion MRI
Mar 31, 2026Mohamed Abouagour, Atharva Shah, Eleftherios GaryfallidisDiffusion MRI Parameter Estimation
Indiana University, Bloomington, IN, USA
FINDER: Zero-Shot Field-Integrated Network for Distortion-free EPI Reconstruction in Diffusion MRI
Mar 27, 2026Namgyu Han, Seong Dae Yun, Chaeeun Lim +7Image Reconstruction Diffusion MRI
Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, South Korea · Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany · Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA +2
Diffusion MRI Transformer with a Diffusion Space Rotary Positional Embedding (D-RoPE)
Mar 26, 2026Gustavo Chau Loo Kung, Mohammad Abbasi, Camila Blank +6Diffusion MRI Medical Image Representation Learning
Stanford University
Robust-ComBat: Mitigating Outlier Effects in Diffusion MRI Data Harmonization
Mar 18, 2026Yoan David, Pierre-Marc Jodoin, Alzheimer's Disease Neuroimaging Initiative +1Medical Image Harmonization Medical Image Analysis
VitaLab, Dep of Computer Science, University of Sherbrooke, Sherbrooke, QC, J1K 2R1, Canada · Imeka Solutions Inc., Sherbrooke, J1H 4A7, QC, Canada · A comprehensive list of authors and affiliations of this consortium appear at the end of the paper.
TractoRC: A Unified Probabilistic Learning Framework for Joint Tractography Registration and Clustering
Mar 11, 2026Yijie Li, Xi Zhu, Junyi Wang +3Clustering Diffusion MRI
University of Electronic Science and Technology of China, Chengdu, China · Nanjing University of Science and Technology, Nanjing, China · Harvard Medical School and Brigham and Women’s Hospital, Boston. USA
HARP: HARmonizing in-vivo diffusion MRI using Phantom-only training
Mar 5, 2026Hwihun Jeong, Qiang Liu, Kathryn E. Keenan +7Medical Image Harmonization Diffusion MRI
Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America · College of Engineering, Northeastern University, Boston, Massachusetts, United States of America · Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, United States of America +6
Spinverse: Differentiable Physics for Permeability-Aware Microstructure Reconstruction from Diffusion MRI
Mar 4, 2026Prathamesh Pradeep Khole, Mario M. Brenes, Zahra Kais Petiwala +5Diffusion MRI PDE Inverse Problems
University of California Santa Cruz, Santa Cruz, CA, USA · University of California San Diego, San Diego, CA, USA · Inria-Saclay, France +1
Slice-wise quality assessment of high b-value breast DWI via deep learning-based artifact detection
Mar 4, 2026Ameya Markale, Luise Brock, Ihor Horishnyi +10Medical Image Classification Magnetic Resonance Imaging
Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Maximiliansplatz 3, 91054 Erlangen, Germany · Institute of Computer Science, Polish Academy of Science, Warsaw, Poland · Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany +2
Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction
Feb 26, 2026Chenhe Du, Xuanyu Tian, Qing Wu +4Image Reconstruction Diffusion-Based Inverse Problems
ShanghaiTech University · Shanghai Jiao Tong University
Unsupervised Denoising of Diffusion-Weighted Images with Bias and Variance Corrected Noise Modeling
Feb 22, 2026Jine Xie, Zhicheng Zhang, Yunwei Chen +2Image Denoising Diffusion MRI
School of Biomedical Engineering, Southern Medical University, Guangzhou, China · Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China · JancsiLab, JancsiTech, Hong Kong, China +1
BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet
Nov 17, 2025Min Gu Kwak, Yeonju Lee, Hairong Wang +2Diffusion Models for Image Restoration Medical Image Synthesis
Department of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA · H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA,2026 USA · Operations Research and Industrial Engineering, Cockrell School of Engineering, University of Texas at Austin, Austin, TX, USAJul +2

Jun 13, 2026J/K move · Enter open · S save

Lesion-DDPM: Lesion-Enhanced 3D Diffusion for MS MRI Synthesis

Weidong Zhang, Yongchan Jung, Shafayat Mowla Anik, Furen Xiao+5

Northeastern University, Vancouver, BC · Fairleigh Dickinson University, Vancouver, BC · University of Colorado at Colorado Springs, Colorado Springs, CO+3

Medical Image Synthesis Diffusion MRI Neuroimaging Conditional Diffusion Models

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3D FLAIR MRI is widely recommended as one of the standard MRI sequences for brain imaging in multiple sclerosis (MS), but publicly available MS datasets remain relatively small and vary across scanners, acquisition protocols, and lesion patterns. This scarcity and variability hinder the development of robust neuroimaging machine learning models and are particularly challenging for generative models that aim to synthesize images while preserving small, sparse lesions. We propose Lesion-DDPM, a 3D conditional diffusion framework for lesion-aware FLAIR synthesis that incorporates multi-level anatomical mask injection together with a lesion-weighted reconstruction loss to emphasize lesion voxels while maintaining global brain structure. Using a curated subset of the MSLesSeg dataset, we compare Lesion-DDPM with representative state-of-the-art GAN- and diffusion-based models, assessing both image-generation metrics and downstream 3D U-Net segmentation. In our experiments, Lesion-DDPM achieved the lowest lesion-region reconstruction error among all methods. In a downstream 3D U-Net lesion segmentation task, a model trained only on Lesion-DDPM-generated scans and evaluated on real MRIs reached a Dice score of 0.616 compared with 0.569 for the best competing synthetic dataset. When Lesion-DDPM images were added to the real training set, the Dice score further increased to 0.685.