机构：[1]Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.[2]Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC 27599, USA.[3]School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China.[4]Department of Acupuncture and Rehabilitation, The Affiliated Hospital of TCM of Guangzhou Medical University, Guangzhou 510130, Guangdong, China.[5]Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.[6]Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510000, Guangdong, China.[7]Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
Magnetic resonance imaging (MRI) and positron emission tomography (PET) are increasingly used to forecast progression trajectories of cognitive decline caused by preclinical and prodromal Alzheimer's disease (AD). Many existing studies have explored the potential of these two distinct modalities with diverse machine and deep learning approaches. But successfully fusing MRI and PET can be complex due to their unique characteristics and missing modalities. To this end, we develop a hybrid multimodality fusion (HMF) framework with cross-domain knowledge transfer for joint MRI and PET representation learning, feature fusion, and cognitive decline progression forecasting. Our HMF consists of three modules: 1) a module to impute missing PET images, 2) a module to extract multimodality features from MRI and PET images, and 3) a module to fuse the extracted multimodality features. To address the issue of small sample sizes, we employ a cross-domain knowledge transfer strategy from the ADNI dataset, which includes 795 subjects, to independent small-scale AD-related cohorts, in order to leverage the rich knowledge present within the ADNI. The proposed HMF is extensively evaluated in three AD-related studies with 272 subjects across multiple disease stages, such as subjective cognitive decline and mild cognitive impairment. Experimental results demonstrate the superiority of our method over several state-of-the-art approaches in forecasting progression trajectories of AD-related cognitive decline.
基金：
M. Yu, A. Bozoki, and M. Liu were partly supported by NIH
grant AG073297.
语种：
外文
PubmedID：
第一作者：
第一作者机构：[1]Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.[2]Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC 27599, USA.
通讯作者：
推荐引用方式(GB/T 7714)：
Yu Minhui,Liu Yunbi,Wu Jinjian,et al.Hybrid Multimodality Fusion with Cross-Domain Knowledge Transfer to Forecast Progression Trajectories in Cognitive Decline[J].Medical Image Computing And Computer-Assisted Intervention : MICCAI ... International Conference On Medical Image Computing And Computer-Assisted Intervention.2023,14394:265-275.doi:10.1007/978-3-031-47425-5_24.
APA：
Yu Minhui,Liu Yunbi,Wu Jinjian,Bozoki Andrea,Qiu Shijun...&Liu Mingxia.(2023).Hybrid Multimodality Fusion with Cross-Domain Knowledge Transfer to Forecast Progression Trajectories in Cognitive Decline.Medical Image Computing And Computer-Assisted Intervention : MICCAI ... International Conference On Medical Image Computing And Computer-Assisted Intervention,14394,
MLA：
Yu Minhui,et al."Hybrid Multimodality Fusion with Cross-Domain Knowledge Transfer to Forecast Progression Trajectories in Cognitive Decline".Medical Image Computing And Computer-Assisted Intervention : MICCAI ... International Conference On Medical Image Computing And Computer-Assisted Intervention 14394.(2023):265-275
