ArticleImaging neuroscience (Cambridge, Mass.)2024
Diffusion MRI with Machine Learning.
Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of advanced diffusion MRI based tractometry of the visual pathway in glaucoma: a systematic review.Frontiers in neuroscience · 2025Pooled it
- Article
- Detailed Delineation of the Fetal Brain in Diffusion MRI via Multi-Task Learning.IEEE transactions on medical imaging · 2026Article
- What needs to be standardized for reliable, reproducible, and robust tractography?GigaScience · 2026Review
- FORCE: FORward modeling for Complex microstructure Estimation.Research square · 2025Article
- Deep Learning for fODF Estimation in Infant Brains: Model Comparison, Ground-Truth Impact, and Domain Shift Mitigation.Human brain mapping · 2025Article
- Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes.Medical image analysis · 2025Article
- Implicit neural representation of multi-shell constrained spherical deconvolution for continuous modeling of diffusion MRI.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Development and evaluation of a deep learning model for multi-frequency Gibbs artifact elimination.Quantitative imaging in medicine and surgery · 2025Article
- A detailed spatiotemporal atlas of the white matter tracts for the fetal brain.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Streamline tractography of the fetal brain in utero with machine learning.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Anatomically constrained tractography of the fetal brain.NeuroImage · 2024Article
- RapidParc: A global-context transformer for parallel, accurate, and lesion-robust tractogram parcellation.Imaging neuroscience (Cambridge, Mass.)Article
- Massively parallelized brain tractography using compute clusters, supercomputers, and graphics processing units.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.