Multimodal cross enhanced fusion network for diagnosis of Alzheimer's disease and subjective memory complaints

Yilin Leng; Wenju Cui; Yunsong Peng; Caiying Yan; Yuzhu Cao; Zhuangzhi Yan; Shuangqing Chen; Xi Jiang; Jian Zheng; Alzheimer’s Disease Neuroimaging Initiative

doi:10.1016/j.compbiomed.2023.106788

Multimodal cross enhanced fusion network for diagnosis of Alzheimer's disease and subjective memory complaints

Comput Biol Med. 2023 May:157:106788. doi: 10.1016/j.compbiomed.2023.106788. Epub 2023 Mar 15.

Authors

Yilin Leng¹, Wenju Cui², Yunsong Peng³, Caiying Yan⁴, Yuzhu Cao², Zhuangzhi Yan⁵, Shuangqing Chen⁶, Xi Jiang⁷, Jian Zheng⁸; Alzheimer’s Disease Neuroimaging Initiative

Affiliations

¹ Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China; Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China. Electronic address: keai77lyl@shu.edu.cn.
² Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
³ Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, Guizhou, 550002, China.
⁴ Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 211103, China.
⁵ Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China.
⁶ Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 211103, China. Electronic address: sznaonao@163.com.
⁷ Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China. Electronic address: xijiang@uestc.edu.cn.
⁸ Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China. Electronic address: zhengj@sibet.ac.cn.

PMID: 36958233
DOI: 10.1016/j.compbiomed.2023.106788

Abstract

Deep learning methods using multimodal imagings have been proposed for the diagnosis of Alzheimer's disease (AD) and its early stages (SMC, subjective memory complaints), which may help to slow the progression of the disease through early intervention. However, current fusion methods for multimodal imagings are generally coarse and may lead to suboptimal results through the use of shared extractors or simple downscaling stitching. Another issue with diagnosing brain diseases is that they often affect multiple areas of the brain, making it important to consider potential connections throughout the brain. However, traditional convolutional neural networks (CNNs) may struggle with this issue due to their limited local receptive fields. To address this, many researchers have turned to transformer networks, which can provide global information about the brain but can be computationally intensive and perform poorly on small datasets. In this work, we propose a novel lightweight network called MENet that adaptively recalibrates the multiscale long-range receptive field to localize discriminative brain regions in a computationally efficient manner. Based on this, the network extracts the intensity and location responses between structural magnetic resonance imagings (sMRI) and 18-Fluoro-Deoxy-Glucose Positron Emission computed Tomography (FDG-PET) as an enhancement fusion for AD and SMC diagnosis. Our method is evaluated on the publicly available ADNI datasets and achieves 97.67% accuracy in AD diagnosis tasks and 81.63% accuracy in SMC diagnosis tasks using sMRI and FDG-PET. These results achieve state-of-the-art (SOTA) performance in both tasks. To the best of our knowledge, this is one of the first deep learning research methods for SMC diagnosis with FDG-PET.

Keywords: Alzheimer’s disease (AD) diagnosis; Cross enhanced fusion; Multiscale long-range receptive field; Subjective memory complaints (SMC) diagnosis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Alzheimer Disease* / diagnostic imaging
Fluorodeoxyglucose F18
Humans
Magnetic Resonance Imaging / methods
Neural Networks, Computer
Positron-Emission Tomography / methods

Substances

Fluorodeoxyglucose F18