Evaluation of Image‐Level Harmonization Methods for Multi‐Center MR Neuroimaging

作者：B Ho, Dong‐Hoon Kim, Ashwin Kumar, Skylar E Weiss, Hillary Vossler, Elizabeth C. Mormino, Greg Zaharchuk, the Alzheimer's Disease Neuroimaging Initiative · 发表于：Journal of Magnetic Resonance Imaging · 年份：2026 · DOI：10.1002/jmri.70221 · 被引用次数：7 · 研究领域：Advanced MRI Techniques and Applications、Functional Brain Connectivity Studies、Dementia and Cognitive Impairment Research

BACKGROUND: Multi-center imaging studies create large-scale data that are useful for identifying pathological patterns and robust training of deep learning models. However, variation due to site and scanner differences can confound analyses, emphasizing the need for harmonization. PURPOSE: To evaluate scanner-related differences in T1w and T2-FLAIR images in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and assess the performance of publicly available image-level harmonization tools. STUDY TYPE: Retrospective. POPULATION: Scanner group analysis: 1143 ADNI3 subjects (233 GE, 173 Philips, 250 Siemens, with 487 Siemens subjects used as an independent reference group). Within-subject comparison: paired multi-vendor scan sessions from 8 subjects. FIELD STRENGTH/SEQUENCE: 3.0T, T1w, and T2-FLAIR MRI sequences. ASSESSMENT: Gray/white matter contrast ratio (G/W ratio), white matter hyperintensity (WMH) volume, and image feature similarity metrics (Fréchet Inception Distance [FID], Learned Perceptual Image Patch Similarity [LPIPS]) were compared across scanner vendors before and after harmonization with statistical (ComBat) and deep learning (HACA3) algorithms. STATISTICAL TESTS: One-way ANOVA and post hoc Games-Howell tests were conducted to assess differences between scanner groups across image pipelines (baseline, post-harmonization). Repeated-measures ANOVA and post hoc paired t-tests with Bonferroni correction were used to evaluate similarity metric changes pre- ...