ArticleCommunications engineering2024
Learning multi-site harmonization of magnetic resonance images without traveling human phantoms.
Article in Communications engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
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Who cites it
17 citing papers in PubMed.
- Harmonized Acquisition of Connectome MRI and Clinical Data for Stroke, Parkinson's Disease and Glioma: Delphi-Derived Protocol Recommendations by the European Academy of Neurology.European journal of neurology · 2026Review
- Feynman-Kac Reweighted Schrödinger Bridge Matching for Surface-based Tau PET Harmonization.ArXiv · 2026Article
- Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods.Medical image analysis · 2026Review
- Review
- Adaptive multi-stage domain unlearning for white-matter lesion segmentation.Frontiers in medicine · 2026Article
- Scanner-agnostic MRI harmonization via SSIM-guided disentanglement.Frontiers in artificial intelligence · 2026Article
- Easy-to-use and easy-to-interpret quality control of 3D gradient echo T1-weighted MR acquisition sequences for improved test-retest stability of MRI-based hippocampus volumetry.Journal of Alzheimer's disease : JAD · 2025Article
- Changing cognitive chimera states in human brain networks with age: Variations in cognitive integration and segregation.PLoS computational biology · 2025Article
- Automated characterization of abdominal MRI exams using deep learning.Scientific reports · 2025Article
- Morphological Brain Analysis Using Ultra Low-Field MRI.Human brain mapping · 2025Article
- Data Harmonization with StyleTransfer-GANs: Enhancing Non-Invasive IDH Classification in Brain Tumors.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- HeteroMRI: Robust white matter abnormality classification across multi-scanner MRI data.GigaScience · 2025Article
- When no answer is better than a wrong answer: A causal perspective on batch effects.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Deep learning for the harmonization of structural MRI scans: a survey.Biomedical engineering online · 2024Review
- Article
- MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data.Medical image analysis · 2023Article
- Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses.Frontiers in neurology · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Harmonization improves Magn. Reson. Imaging (MRI) data consistency and is central to effective integration of diverse imaging data acquired across multiple sites. Recent deep learning techniques for harmonization are predominantly supervised in nature and hence require imaging data of the same human subjects to be acquired at multiple sites. Data collection as such requires the human subjects to travel across sites and is hence challenging, costly, and impractical, more so when sufficient sample size is needed for reliable network training. Here we show how harmonization can be achieved with a deep neural network that does not rely on traveling human phantom data. Our method disentangles site-specific appearance information and site-invariant anatomical information from images acquired at multiple sites and then employs the disentangled information to generate the image of each subject for any target site. We demonstrate with more than 6,000 multi-site T1- and T2-weighted images that our method is remarkably effective in generating images with realistic site-specific appearances without altering anatomical details. Our method allows retrospective harmonization of data in a wide range of existing modern large-scale imaging studies, conducted via different scanners and protocols, without additional data collection.
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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.