Multi-scale discriminative regions analysis in FDG-PET imaging for early diagnosis of Alzheimer's disease. (1st August 2022)

• Record Type: Journal Article
• Authors: Zhang, Jin; He, Xiaohai; Qing, Linbo; Xu, Yining; Liu, Yan; Chen, Honggang
• Is Part Of: Journal of neural engineering. Volume 19:Number 4(2022)
• Publication Date: 2022-08-01
• Subjects: Alzheimer's disease -- fluorodeoxyglucose positron emission tomography -- artificial intelligence -- medical image processing -- mild cognitive impairment; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ac8450
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . Alzheimer's disease (AD) is a degenerative brain disorder, one of the main causes of death in elderly people, so early diagnosis of AD is vital to prompt access to medication and medical care. Fluorodeoxyglucose positron emission tomography (FDG-PET) proves to be effective to help understand neurological changes via measuring glucose uptake. Our aim is to explore information-rich regions of FDG-PET imaging, which enhance the accuracy and interpretability of AD-related diagnosis. Approach . We develop a novel method for early diagnosis of AD based on multi-scale discriminative regions in FDG-PET imaging, which considers the diagnosis interpretability. Specifically, a multi-scale region localization module is discussed to automatically identify disease-related discriminative regions in full-volume FDG-PET images in an unsupervised manner, upon which a confidence score is designed to evaluate the prioritization of regions according to the density distribution of anomalies. Then, the proposed multi-scale region classification module adaptively fuses multi-scale region representations and makes decision fusion, which not only reduces useless information but also offers complementary information. Most of previous methods concentrate on discriminating AD from cognitively normal (CN), while mild cognitive impairment, a transitional state, facilitates early diagnosis. Therefore, our method is further applied to multiple AD-related diagnosis tasks, not limited to AD vs. CN. Main results . Experimental results on the Alzheimer's Disease Neuroimaging Initiative dataset show that the proposed method achieves superior performance over state-of-the-art FDG-PET-based approaches. Besides, some cerebral cortices highlighted by extracted regions cohere with medical research, further demonstrating the superiority. Significance . This work offers an effective method to achieve AD diagnosis and detect disease-affected regions in FDG-PET imaging. Our results could be beneficial for providing an additional opinion on the clinical diagnosis.