Harmonization of multi-site diffusion tensor imaging data
By Jean-Philippe Fortin, D. Parker, B. Tunç, Takanori Watanabe, Mark A. Elliott, Kosha Ruparel, David R. Roalf, Theodore D. Satterthwaite, Ruben C. Gur, Raquel E. Gur and 3 more. 2017
1,157 citations recorded by the source corpus.
Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer from technical between-scanner variation that hinders comparisons of images across imaging sites, scanners and over time. Using fractional anisotropy (FA) and mean diffusivity (MD) maps of 205 healthy participants acquired on two different scanners, we show that the DTI measurements are highly site-specific, highlighting the need of correcting for site effects before performing downstream statistical analyses. We first show evidence that combining DTI data from multiple sites, without harmonization, is counter-productive and negatively impacts the inference. Then, we propose and compare several harmonization approaches for DTI data, and show that ComBat, a popular batch-effect correction tool used in genomics, performs best at modeling and removing the unwanted inter-site variability in FA and MD maps. Using age as a biological phenotype of interest, we show that ComBat both preserves biological variability and removes the unwanted variation introduced by site. Finally, we assess the different harmonization methods in the presence of different levels of confounding between site and age, in addition to test robustness to small sample size studies.
Subfields: Radiology, Nuclear Medicine and Imaging
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Citations in the corpus
- Invariant Representations without Adversarial Training (2018, neutral): “Modern methods have included more nuanced feature generation and more complex models, but follow along the same vein [5, 6] “Regressing out” or “controlling for” unwanted factors can be effective, but place strong constraints on later analyses (namely, the observation of the same unwanted…”
- Evading the Adversary in Invariant Representation (2018, neutral): “Modern methods have included more nuanced feature generation and more complex models, but follow along the same vein [5, 6] “Regressing out” or “controlling for” unwanted factors can be effective, but place strong constraints on later analyses (namely, the observation of the same unwanted covariates”
- Harmonization of cortical thickness measurements across scanners and sites (2017, methodology): “Recently, ComBat (Johnson et al., 2007), a batch-effect correction tool commonly used in genomics, has been adapted for the modeling and removal of site effects in multi-site DTI studies (Fortin et al., 2017).”