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.

Interpretable Brain Age Prediction Using White Matter Fiber Orientation Distributions with Rotation-Equivariant Deep Learning
Oct 26, 2026Chenyang Fan, Robert D. Sanders, Elie Matar +5Brain Age Prediction Explainable Medical Image Analysis
School of Biomedical Engineering and Brain and Mind Centre at the University of Sydney, Sydney, NSW 2050 Australia · Brain and Mind Centre and Central Clinical School at the University of Sydney, Sydney, NSW, 2050 Australia · Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong SAR 999077, China +5
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
OpenMAP-HIE: Pathology Informed Score-based Diffusion Model for Unsupervised Neonatal Hypoxic-Ischemic Encephalopathy Lesion Segmentation
Oct 21, 2026Pengyu Kan, Craig Jones, Kenichi OishiMedical Image Segmentation Medical Image Anomaly Detection
Johns Hopkins University · Johns Hopkins University, University of Alberta · Johns Hopkins University School of Medicine
Implicit Neural Models for Direct Multi-Shell Diffusion MRI Signal Estimation
Oct 16, 2026Sanna Persson, Fabian Leander Sinzinger, Christoffer Olsson +1Diffusion MRI Implicit Neural Representations
Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
dMRI-F: A Foundation Model for Generalizable Diffusion MRI Analysis from Sparse Acquisitions
Oct 13, 2026Xi Zhu, Yijie Li, Xiaofan Wang +4Diffusion MRI Medical Imaging Foundation Models
University of Electronic Science and Technology of China.
Generalizable field of view extension for diffusion MRI via conditional flow matching
Oct 12, 2026Gaurav Rudravaram, Lianrui Zuo, Ema Topolnjak +22Diffusion MRI Domain Generalization
Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN USA · Dept. of Computer Science, Vanderbilt University, Nashville, TN USA · Dept. of Neurology, Vanderbilt Health, Nashville, TN USA +9
FiberGeoText: A Vision-Language Model for Population- Level Organization of Superficial White Matter
Oct 2, 2026Yuqian Chen, R. Jarrett Rushmore, Guikun Chen +5Neuroimaging Medical Image Representation Learning
Department of Radiology, Mass General Brigham, Boston MA, USA · Harvard Medical School, Boston MA, USA · Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston MA, USA +2
A foundation for systematic analysis of transformers and RNNs for tractography
Oct 1, 2026Emmanuelle Renauld, Philippe Poulin, Hugo Larochelle +2Recurrent Neural Networks Recurrent Transformers
Computer Sciences Department, Université de Sherbrooke · Mila, Université de Montréal · Imeka Solutions Inc, Sherbrooke, QC, Canada
Fiber-Resolved Microstructure Quantification from Multi-Shell Diffusion MRI using Detection Transformers
Sep 30, 2026Sebastian Endt, Marcus Wirth, Johannes Reinhold Schlund +1Diffusion MRI
AImotion Bavaria, Technische Hochschule Ingolstadt, Ingolstadt, Germany · TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany · TUM School of Natural Sciences, Technical University of Munich, Garching, Germany
GraphSVR: q-Space--Aware Graph-Based Slice-to-Volume Registration for Diffusion MRI
Sep 21, 2026Noga Kertes, Daphna Link Sourani, Alex M. Bronstein +1Medical Image Registration Diffusion MRI
Faculty of Biomedical Engineering, Technion – Israel Institute of Technology, Haifa, Israel · The May-Blum-Dahl MRI Research Center, Faculty of Biomedical Engineering, Technion – Israel Institute of Technology, Haifa, Israel · The Taub Faculty of Computer Science, Technion – Israel Institute of Technology, Haifa, Israel +1
DTI-Guided Volumetric Spherical Harmonics Regression for Single-to-Multi-Shell dMRI Synthesis
Sep 13, 2026Binghua Li, Christina Andica, Tong Liang +8Medical Image Synthesis Diffusion MRI
Juntendo University, Tokyo, Japan · RIKEN Center for Advanced Intelligence Project, Tokyo, Japan · Tokyo University of Agriculture and Technology, Tokyo, Japan
Large-Scale Pretraining for Improving Deep Learning-Based Geometric Distortion Correction of Diffusion-Weighted Imaging
Sep 6, 2026Saroj Khanal, Yashawant Kumar Yadav, Kritam Bhattarai +12Diffusion MRI Self-Supervised Pre-Training
Nepal Applied Mathematics and Informatics Institute for Research (NAAMII), Lalitpur, Nepal · University of Exeter, Exeter, Devon, UK · Institute of Engineering, Purwanchal Campus, Dharan, Nepal +9
Diffusion-Encoding Gaussian Field for Joint k-q dMRI Reconstruction
Sep 2, 2026Zhibo Chen, Yajuan Huang, Yu Guan +3Image Reconstruction Diffusion MRI
School of Information Engineering, Nanchang University, Nanchang 330031, China
Prior-Guided Implicit Neural Representations for Single-Subject Diffusion MRI Super-Resolution
Sep 1, 2026Abdulkader Ghandoura, Marsil Zakour, William Consagra +1Implicit Neural Representations Medical Image Super-Resolution
Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School Boston, MA, USA · School of Computation, Information and Technology Technical University of Munich Munich, Germany · Department of Statistics University of South Carolina Columbia, SC, USA
Deep Learning Segmentation of Diffusion-Weighted MRI Acute Ischaemic Stroke: A Pragmatic Evaluation Across Three Datasets
Aug 26, 2026Atle Bjørnerud, Till Schellhorn, Thor H. Skattør +4Medical Image Segmentation U-Net
Computational Radiology & Artificial Intelligence Unit, Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway · Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway · Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway +5
An integrated diffusion-weighted imaging processing and interpretation platform for MR-guided radiotherapy
Aug 20, 2026Yunxiang Li, Yan Dai, Yen-Peng Liao +3Diffusion MRI Longitudinal Medical Image Analysis
Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA · Department of Radiation Oncology, University of California San Francisco, San Francisco, California, USA
Evaluating ADC-only deep learning pipelines for breast cancer detection and segmentation using standalone diffusion-weighted MRI
Aug 18, 2026Pablo García Marcos, Paula Puerta Gonzĺez, Guillermo Lorenzo +4Breast Cancer Medical Image Segmentation
Computer Sciences Department, University of Oviedo, Asturias, Spain · Biomedical Engineering Center (BME), University of Oviedo, Asturias, Spain · Electrical Engineering Department, University of Oviedo, Asturias, Spain +6
A Deep RL based Framework for Targeted White Matter Tractography
Aug 13, 2026Ankita JoshiReinforcement Learning Magnetic Resonance Imaging
SCHOOL OF COMPUTING AND ELECTRICAL ENGINEERING, INDIAN INSTITUTE OF TECHNOLOGY MANDI · Indian Institute of Technology Mandi, 175005 - INDIA
MRI super-resolution in ten sampling steps using a diffusion bridge model
Aug 9, 2026Mojtaba Safari, Hang Yu, Zach Eidex +10Medical Image Super-Resolution Diffusion-Based Inverse Problems
Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL, United States · Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States · Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, United States +3
Protocol generalisation for brain tissue microstructure estimation via hypernetwork-controlled geometric deep learning
Aug 3, 2026Andrea Brigliadori, Leevi Kerkela, Hui ZhangEquivariant Neural Networks Magnetic Resonance Imaging
Hawkes Institute and Department of Computer Science, University College London, London, United Kingdom
Eddeep: a deep-learning framework for fast eddy-current distortion correction in diffusion MRI
Jul 28, 2026Antoine Legouhy, Ross Callaghan, Yuchuan Qiao +4Medical Image Registration Magnetic Resonance Imaging
Hawkes Institute & Department of Computer Science, University College London, London, UK · Institut Pasteur, Universit´e Paris Cit´e, Unit´e de Neuroanatomie Appliqu´ee et Th´eorique, France · AINOSTICS ltd., Manchester, UK +3
SIINR: Structurally Informed Implicit Neural Representations for super-resolution with uncertainty quantification of clinical quality diffusion MRI datasets
Jul 22, 2026Tom Hendriks, William Consagra, Anna Vilanova +2Implicit Neural Representations Uncertainty Quantification
Department of Computer Science and Mathematics, Eindhoven University of Technology, Groene Loper 5, Eindhoven, 5612 AP, Noord-Brabant, The Netherlands. · Department of Statistics, University of South Carolina, Columbia, 29225, South Carolina, United States of America. · Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, 399 Revolution Drive, Boston, 02215, MA, United States.
EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI
Jul 3, 2026Hasan Ulutas, Muhammet Emin Sahin, Mustafa Fatih Erkoc +4U-Net Magnetic Resonance Imaging
Yozgat Bozok University, Department of Computer Engineering, Yozgat, Turkey · Izmir Bakircay University, Department of Computer Engineering, Izmir, Turkey · Queen Mary’s Digital Environment Research Institute (DERI), London, UK +3
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
BrainFIBRE: A Foundation Model via Information Decomposition for Brain Microstructure
Jul 1, 2026Zijian Dong, Yi Lin, Ji Fang +3Disentangled Representation Learning Medical Imaging Foundation Models
National University of Singapore, Singapore
Distortion-Corrected Diffusion MRI Using Rotated-View EPI and Joint Field-Map/Image Estimation with Gaussian Primitives
Jun 30, 2026Wenqi Huang, Zhitao Li, Nan Wang +8Image Reconstruction Magnetic Resonance Imaging
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, and the School of Computation, Information and Technology, TUM, Munich, Germany · Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China · Department of Radiology, Stanford University, Stanford, CA, USA +2
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
Cross-Modality Structural Guidance in 3D Latent Diffusion for Robust FLAIR Super-Resolution
Jun 24, 2026Haoyu Lan, Jiazhen Zhang, John Onofrey +4Diffusion Models for Image Restoration Medical Image Super-Resolution
University of Southern California · Yale University
BrainG3N: A Dual-Purpose Tokenizer for Controllable 3D Brain MRI Generation
Jun 17, 2026Max Van Puyvelde, Ibrahim Gulluk, Wim Van Criekinge +1Masked Autoencoders Latent Diffusion Models
Department of Biomedical Data Science, Stanford University School of Medicine · Department of Mathematical Modelling, Statistics & Bioinformatics, Ghent University · Department of Electrical Engineering, Stanford University

Oct 26, 2026J/K move · Enter open · S save

Interpretable Brain Age Prediction Using White Matter Fiber Orientation Distributions with Rotation-Equivariant Deep Learning

Chenyang Fan, Robert D. Sanders, Elie Matar, Yixuan Yuan+4

School of Biomedical Engineering and Brain and Mind Centre at the University of Sydney, Sydney, NSW 2050 Australia · Brain and Mind Centre and Central Clinical School at the University of Sydney, Sydney, NSW, 2050 Australia · Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong SAR 999077, China+5

Brain Age Prediction Explainable Medical Image Analysis Diffusion MRI Equivariant Neural Networks

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Diffusion MRI (dMRI) provides information about white matter microstructure that can support brain age prediction. However, many existing prediction models use diffusion tensor imaging (DTI) scalar metrics that omit orientation information. Deep learning is increasingly used for brain age prediction, but conventional CNNs do not explicitly respect the geometric structure of orientation-dependent features. We present an interpretable brain age prediction framework that combines white matter fiber orientation distributions (WM-FODs) with an SE(3) (Special Euclidean group in 3 dimensions) equivariant CNN. The network operates directly on spherical harmonic (SH) coefficients of the FOD, preserving their transformation properties under rotation. Evaluation in 692 participants aged 36-90 years from the Human Connectome Project Aging dataset showed that the combined framework outperformed the evaluated DTI-based models and conventional CNNs. Averaging predictions from five cross-validation models yielded a mean absolute error of 3.54 years and Pearson's correlation of 0.958. Our results indicated that the performance advantage depended on combining WM-FODs with equivariant processing. Prediction accuracy was highest at a maximum SH order of Lmax = 2 and declined at higher orders. Tract-level occlusion, where each segmented tract was masked in turn, revealed age-dependent patterns of model sensitivity, with importance peaks in frontal projection pathways at 36-44 years, the fornix at 45-54 years, the callosal isthmus at 55-64 years, the middle cerebellar peduncle at 65-74 years, and the callosal genu at 75-90 years. These findings support rotation-equivariant processing of WM-FODs for accurate brain age prediction with anatomically interpretable model sensitivity.