Multi-site MRI harmonization via attention-guided deep domain adaptation for brain disorder identification

Structural magnetic resonance imaging (MRI) has shown great clinical and practical values in computer-aided brain disorder identification. Multi-site MRI data increase sample size and statistical power, but are susceptible to inter site heterogeneity caused by different scanners, scanning protocols, and subject cohorts. Multi-site MRI harmonization (MMH) helps alleviate the inter-site difference for subsequent analysis. Some MMH methods performed at imaging level or feature extraction level are concise but lack robustness and flexibility to some extent. Even though several machine/deep learning-based methods have been proposed for MMH, some of them require a portion of labeled data in the to-be-analyzed target domain or ignore the potential contributions of different brain regions to the identification of brain disorders. In this work, we propose an attention-guided deep domain adaptation (AD(2)A) framework for MMH and apply it to automated brain disorder identification with multi-site MRIs. The proposed framework does not need any category label information of target data, and can also automatically identify discriminative regions in whole-brain MR images. Specifically, the proposed AD(2)A is composed of three key modules: (1) an MRI feature encoding module to extract representations of input MRIs, (2) an attention discovery module to automatically locate discriminative dementia-related regions in each whole-brain MRI scan, and (3) a domain transfer module trained with adversarial learning for knowledge transfer between the source and target domains. Experiments have been performed on 2572 subjects from four benchmark datasets with T1-weighted structural MRIs, with results demonstrating the effectiveness of the proposed method in both tasks of brain disorder identification and disease progression prediction. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Structural magnetic resonance imaging (MRI) has great value in computer-aided brain disorder identification. Multi-site MRI data can increase sample size and statistical power, but may face challenges due to differences between sites. A proposed attention-guided deep domain adaptation framework shows effectiveness in automated brain disorder identification with multi-site MRIs.