mlsanityData to 14 Oct 2026

Diffusion MRI with Machine Learning

arXiv 2402.00019 ↗First posted 1 Jan 2024eess.IV cs.CV cs.LGcited by 11

Not on the map. Why?

Abstract (arXiv)

hspace2mm Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique capabilities including noninvasive probing of tissue microstructure and structural connectivity. It is widely used for clinical assessment of disease and injury, and for neuroscience research. Analyzing the dMRI data to extract useful information for medical and scientific purposes can be challenging. The dMRI measurements may suffer from strong noise and artifacts, and may exhibit high inter-session and inter-scanner variability in the data, as well as inter-subject heterogeneity in brain structure. Moreover, the relationship between measurements and the phenomena of interest can be highly complex. Recent years have witnessed increasing use of machine learning methods for dMRI analysis. This manuscript aims to assess these efforts, with a focus on methods that have addressed data preprocessing and harmonization, microstructure mapping, tractography, and white matter tract analysis. We study the main findings, strengths, and weaknesses of the existing methods and suggest topics for future research. We find that machine learning may be exceptionally suited to tackle some of the difficult tasks in dMRI analysis. However, for this to happen, several shortcomings of existing methods and critical unresolved issues need to be addressed. There is a pressing need to improve evaluation practices, to increase the availability of rich training datasets and validation benchmarks, as well as model generalizability, reliability, and explainability concerns.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 101 of its 434 references that are in the map. First posted on arXiv 1 Jan 2024.

Builds on

101 of 434 references.

References · 101

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2017On Calibration of Modern Neural Networkscited by 4,960
  4. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  5. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  6. 2016Adversarial examples in the physical worldcited by 1,769
  7. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  8. 2016Neural Architecture Search with Reinforcement Learningcited by 1,499
  9. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,423
  10. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  11. 2016Group Equivariant Convolutional Networkscited by 1,165
  12. 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 662
38 more
  1. 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 533
  2. 2018Spherical CNNscited by 456
  3. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
  4. 2018Accurate Uncertainties for Deep Learning Using Calibrated Regressioncited by 422
  5. 2018Noise2Void - Learning Denoising from Single Noisy Imagescited by 364
  6. 2019Noise2Self: Blind Denoising by Self-Supervisioncited by 276
  7. 2017Learning SO(3) Equivariant Representations with Spherical CNNscited by 248
  8. 2019Deep learning with noisy labels: exploring techniques and remedies in medical image analysiscited by 173
  9. 2019A General Framework for Uncertainty Estimation in Deep Learningcited by 119
  10. 2019Evaluating and Calibrating Uncertainty Prediction in Regression Taskscited by 87
  11. 2018DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applicationscited by 62
  12. 2018TractSeg - Fast and accurate white matter tract segmentationcited by 52
  13. 2018A Comprehensive Analysis of Deep Regressioncited by 48
  14. 2019The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort studycited by 38
  15. 2017Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolutioncited by 30
  16. 2020Patch2Self: Denoising Diffusion MRI with Self-Supervised Learningcited by 29
  17. 2019Scanner Invariant Representations for Diffusion MRI Harmonizationcited by 27
  18. 2018q-Space Novelty Detection with Variational Autoencoderscited by 22
  19. 2021Rotation-Equivariant Deep Learning for Diffusion MRIcited by 20
  20. 2022Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitionscited by 20
  21. 2020A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applicationscited by 19
  22. 2020Tractogram filtering of anatomically non-plausible fibers with geometric deep learningcited by 17
  23. 2020Filtering in tractography using autoencoders (FINTA)cited by 17
  24. 2020Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patientscited by 16
  25. 2021SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRIcited by 16
  26. 2020SuperDTI: Ultrafast diffusion tensor imaging and fiber tractography with deep learningcited by 14
  27. 2022Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellationcited by 14
  28. 2022A microstructure estimation Transformer inspired by sparse representation for diffusion MRIcited by 14
  29. 2015The White Matter Query Language: A Novel Approach for Describing Human White Matter Anatomycited by 13
  30. 2021DIFFnet: Diffusion parameter mapping network generalized for input diffusion gradient schemes and bvaluescited by 12
  31. 2018DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractographycited by 11
  32. 2021Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRIcited by 11
  33. 2021VolterraNet: A higher order convolutional network with group equivariance for homogeneous manifoldscited by 11
  34. 2023Spatio-Angular Convolutions for Super-resolution in Diffusion MRIcited by 11
  35. 2023TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performancecited by 11
  36. 2023TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representationcited by 11
  37. 2019Combined tract segmentation and orientation mapping for bundle-specific tractographycited by 10
  38. 2019Tractography and machine learning: Current state and open challengescited by 10

50 most cited of 101 All 101 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 4; 2026: 3.

11 papers.

  1. 2025MRI Super-Resolution with Deep Learning: A Comprehensive Surveycited by 7
  2. 2024Detailed delineation of the fetal brain in diffusion MRI via multi-task learningcited by 3
  3. 2024Anatomically Constrained Tractography of the Fetal Braincited by 2
  4. 2024TractShapeNet: Efficient Multi-Shape Learning with 3D Tractography Point Cloudscited by 2
  5. 2025DDTracking: A Deep Generative Framework for Diffusion MRI Tractography with Streamline Local-Global Spatiotemporal Modelingcited by 2
  6. 2024Streamline tractography of the fetal brain in utero with machine learningcited by 1
  7. 2025Physics-Guided Diffusion Transformer with Spherical Harmonic Posterior Sampling for High-Fidelity Angular Super-Resolution in Diffusion MRIcited by 1
  8. 2026SIINR: Structurally Informed Implicit Neural Representations for super-resolution with uncertainty quantification of clinical quality diffusion MRI datasetscited by 1
  9. 2025Revolutionizing Brain Tumor Imaging: Generating Synthetic 3D FA Maps from T1-Weighted MRI using CycleGAN Modelscited by 0
  10. 2026TensorLDM: A Component-Wise Latent Diffusion Model for Volumetric DTI Reconstruction from Sparse DWIscited by 0
  11. 2026Protocol generalisation for brain tissue microstructure estimation via hypernetwork-controlled geometric deep learningcited by 0