ArticleNeuroImage2020
Intensity warping for multisite MRI harmonization.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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.
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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
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neuroimage analysis using artificial intelligence approaches: a systematic review.Medical & biological engineering & computing · 2024Pooled it
- Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods.Medical image analysis · 2026Review
- Unpaired volumetric harmonization of brain MRI with conditional latent diffusion.Medical image analysis · 2026Article
- Harmonization of Structural Brain Connectivity Through Distribution Matching.Human brain mapping · 2025Article
- Characterization of Portable Ultra-Low Field MRI Scanners for Multi-Center Structural Neuroimaging.Human brain mapping · 2025Article
- 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
- An evaluation of image-based and statistical techniques for harmonizing brain volume measurements.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Are white matter hyperintensities associated with neuroborreliosis? The answer is twofold.Neuroradiology · 2025Article
- Optical Coherence Tomography Harmonization with Anatomy-Guided Latent Metric Schrödinger Bridges.Advances in neural information processing systems · 2025Article
- Superpixel-ComBat modeling: A joint approach for harmonization and characterization of inter-scanner variability in T1-weighted images.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Learning multi-site harmonization of magnetic resonance images without traveling human phantoms.Communications engineering · 2024Article
- MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data.Medical image analysis · 2023Article
- Image data harmonization tools for the analysis of post-traumatic epilepsy development in preclinical multisite MRI studies.Epilepsy research · 2023Article
- Bayesian statistical modeling to predict observer-specific optimal windowing parameters in magnetic resonance imaging.Heliyon · 2023Article
- Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization.NeuroImage · 2023Review
- Cross-scanner harmonization methods for structural MRI may need further work: A comparison study.NeuroImage · 2023Article
- Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.An international journal on information fusion · 2022Article
- Multimodal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions.Human brain mapping · 2022Article
Corrections and comments
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Authors and funding
22 authors.
Funding
Abstract
In multisite neuroimaging studies there is often unwanted technical variation across scanners and sites. These "scanner effects" can hinder detection of biological features of interest, produce inconsistent results, and lead to spurious associations. We propose mica (multisite image harmonization by cumulative distribution function alignment), a tool to harmonize images taken on different scanners by identifying and removing within-subject scanner effects. Our goals in the present study were to (1) establish a method that removes scanner effects by leveraging multiple scans collected on the same subject, and, building on this, (2) develop a technique to quantify scanner effects in large multisite studies so these can be reduced as a preprocessing step. We illustrate scanner effects in a brain MRI study in which the same subject was measured twice on seven scanners, and assess our method's performance in a second study in which ten subjects were scanned on two machines. We found that unharmonized images were highly variable across site and scanner type, and our method effectively removed this variability by aligning intensity distributions. We further studied the ability to predict image harmonization results for a scan taken on an existing subject at a new site using cross-validation.
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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.