ArticleNeuroImage2019
Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms.
Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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Who cites it
68 citing papers in PubMed.
- Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods.Medical image analysis · 2026Review
- What needs to be standardized for reliable, reproducible, and robust tractography?GigaScience · 2026Review
- Mapping functional connectivity in the pigeon brain with wide-field optical imaging.Neurophotonics · 2026Article
- Diffusion MRI sampling schemes bias diffusion metrics and tractography.Frontiers in neuroimaging · 2026Article
- Methodological Evolution of Diffusion MRI in Chronic Pain: A Narrative Review.Journal of pain research · 2026Review
- Challenges and best practices when using ComBAT to harmonize diffusion MRI data.Scientific reports · 2025Article
- Effect of a consistent reconstruction algorithm on inter-scanner reproducibility in diffusion MRI.Medical physics · 2025Article
- The Impact of Multiband and In-Plane Acceleration on White Matter Microstructure Analysis.Human brain mapping · 2025Article
- Deep Learning for fODF Estimation in Infant Brains: Model Comparison, Ground-Truth Impact, and Domain Shift Mitigation.Human brain mapping · 2025Article
- Cross-site harmonization of diffusion MRI data without matched training subjects.Magnetic resonance in medicine · 2025Article
- Harmonization of Structural Brain Connectivity Through Distribution Matching.Human brain mapping · 2025Article
- Harmonization of Structural Brain Connectivity through Distribution Matching.bioRxiv : the preprint server for biology · 2025Article
- A multi-site, multi-modal travelling-heads resource for brain MRI harmonisation.Scientific data · 2025Article
- Implicit neural representation of multi-shell constrained spherical deconvolution for continuous modeling of diffusion MRI.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025Article
- Introducing QuantConn: Overcoming challenging diffusion acquisitions with harmonization.Computational diffusion MRI. CDMRI (Workshop) · 2025Article
- Harmonization of diffusion MRI on healthy subjects using NeuroCombat and LongCombat: a B-Q MINDED brain intra- and inter-scanner study.Frontiers in neuroscience · 2025Article
- Learning disentangled representations to harmonize connectome network measures.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal.Magnetic resonance imaging · 2024Article
- Article
8 more citing papers are in PubMed but not listed here.
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19 authors.
Funding
Abstract
Diffusion MRI is being used increasingly in studies of the brain and other parts of the body for its ability to provide quantitative measures that are sensitive to changes in tissue microstructure. However, inter-scanner and inter-protocol differences are known to induce significant measurement variability, which in turn jeopardises the ability to obtain 'truly quantitative measures' and challenges the reliable combination of different datasets. Combining datasets from different scanners and/or acquired at different time points could dramatically increase the statistical power of clinical studies, and facilitate multi-centre research. Even though careful harmonisation of acquisition parameters can reduce variability, inter-protocol differences become almost inevitable with improvements in hardware and sequence design over time, even within a site. In this work, we present a benchmark diffusion MRI database of the same subjects acquired on three distinct scanners with different maximum gradient strength (40, 80, and 300 mT/m), and with 'standard' and 'state-of-the-art' protocols, where the latter have higher spatial and angular resolution. The dataset serves as a useful testbed for method development in cross-scanner/cross-protocol diffusion MRI harmonisation and quality enhancement. Using the database, we compare the performance of five different methods for estimating mappings between the scanners and protocols. The results show that cross-scanner harmonisation of single-shell diffusion data sets can reduce the variability between scanners, and highlight the promises and shortcomings of today's data harmonisation techniques.
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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.