Joint learning framework of cross-modal synthesis and diagnosis for Alzheimer's disease by mining underlying shared modality information

• Published in: Medical image analysis Vol. 91; p. 103032
• Main Authors: Wang, Chenhui, Piao, Sirong, Huang, Zhizhong, Gao, Qi, Zhang, Junping, Li, Yuxin, Shan, Hongming
• Format: Journal Article
• Language: English
• Published: Netherlands 01.01.2024
• Subjects: Alzheimer Disease - pathology; Cognitive Dysfunction - diagnostic imaging; Humans; Learning; Magnetic Resonance Imaging - methods; Neuroimaging - methods; Positron-Emission Tomography - methods; Magnetic resonance imaging (MRI); Positron emission tomography (PET); Joint learning; Cross-modal synthesis; Alzheimer’s disease (AD) diagnosis
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2023.103032

Alzheimer's disease (AD) is one of the most common neurodegenerative disorders presenting irreversible progression of cognitive impairment. How to identify AD as early as possible is critical for intervention with potential preventive measures. Among various neuroimaging modalities used to diagnose AD, functional positron emission tomography (PET) has higher sensitivity than structural magnetic resonance imaging (MRI), but it is also costlier and often not available in many hospitals. How to leverage massive unpaired unlabeled PET to improve the diagnosis performance of AD from MRI becomes rather important. To address this challenge, this paper proposes a novel joint learning framework of unsupervised cross-modal synthesis and AD diagnosis by mining underlying shared modality information, improving the AD diagnosis from MRI while synthesizing more discriminative PET images. We mine underlying shared modality information in two aspects: diversifying modality information through the cross-modal synthesis network and locating critical diagnosis-related patterns through the AD diagnosis network. First, to diversify the modality information, we propose a novel unsupervised cross-modal synthesis network, which implements the inter-conversion between 3D PET and MRI in a single model modulated by the AdaIN module. Second, to locate shared critical diagnosis-related patterns, we propose an interpretable diagnosis network based on fully 2D convolutions, which takes either 3D synthesized PET or original MRI as input. Extensive experimental results on the ADNI dataset show that our framework can synthesize more realistic images, outperform the state-of-the-art AD diagnosis methods, and have better generalization on external AIBL and NACC datasets.