ArticleMagnetic resonance in medicine2020
Scanner invariant representations for diffusion MRI harmonization.
Article in Magnetic resonance in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 1 of them a synthesis that pooled it.
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Who cites it
78 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Challenges and opportunities for neuroimaging in young patients with traumatic brain injury: a coordinated effort towards advancing discovery from the ENIGMA pediatric moderate/severe TBI group.Brain imaging and behavior · 2021Pooled it
- ComBat-Predict Enhances Generalizability of Neuroimaging Models to New Sites.Human brain mapping · 2026Article
- Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods.Medical image analysis · 2026Review
- Alzheimer's Disease Brain Phenotypes are Age-dependent.bioRxiv : the preprint server for biology · 2026Article
- Multi-Site Transfer Classification of Major Depressive Disorder: An fMRI Study in 3335 Subjects.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- What needs to be standardized for reliable, reproducible, and robust tractography?GigaScience · 2026Review
- Deep learning to predict future cognitive decline: a multimodal approach using brain MRI and clinical data.Frontiers in neuroimaging · 2026Article
- A clinically interpretable model for Parkinson's disease: insights from complementary MRI modalities.Brain communications · 2026Article
- Scanner-agnostic MRI harmonization via SSIM-guided disentanglement.Frontiers in artificial intelligence · 2026Article
- White Matter Bundle Reconstruction From Single-Shell Diffusion Magnetic Resonance Imaging: Test-Retest Reliability and Predictive Capability Across Orientation Distribution Function Reconstruction Methods.Human brain mapping · 2025Article
- Challenges and best practices when using ComBAT to harmonize diffusion MRI data.Scientific reports · 2025Article
- Harmonizing 10,000 connectomes: site-invariant representation learning for multi-site analysis of network connectivity and cognitive impairment.Journal of medical imaging (Bellingham, Wash.) · 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
- Overcoming Site Variability in Multisite fMRI Studies: an Autoencoder Framework for Enhanced Generalizability of Machine Learning Models.Neuroinformatics · 2025Article
- Estimating Brain Similarity Networks With Diffusion MRI.Human brain mapping · 2025Article
- Harmonization of Structural Brain Connectivity Through Distribution Matching.Human brain mapping · 2025Article
- AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.Cell reports. Medicine · 2025Review
- Harmonization of Structural Brain Connectivity through Distribution Matching.bioRxiv : the preprint server for biology · 2025Article
- Investigation of ComBat Harmonization on Radiomic and Deep Features from Multi-Center Abdominal MRI Data.Journal of imaging informatics in medicine · 2025Article
18 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
purposeIn the present work, we describe the correction of diffusion-weighted MRI for site and scanner biases using a novel method based on invariant representation. THEORY AND
methodsPooled imaging data from multiple sources are subject to variation between the sources. Correcting for these biases has become very important as imaging studies increase in size and multi-site cases become more common. We propose learning an intermediate representation invariant to site/protocol variables, a technique adapted from information theory-based algorithmic fairness; by leveraging the data processing inequality, such a representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to underlying structures. To implement this, we use a deep learning method based on variational auto-encoders (VAE) to construct scanner invariant encodings of the imaging data.
resultsTo evaluate our method, we use training data from the 2018 MICCAI Computational Diffusion MRI (CDMRI) Challenge Harmonization dataset. Our proposed method shows improvements on independent test data relative to a recently published baseline method on each subtask, mapping data from three different scanning contexts to and from one separate target scanning context.
conclusionsAs imaging studies continue to grow, the use of pooled multi-site imaging will similarly increase. Invariant representation presents a strong candidate for the harmonization of these data.
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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.