Dong Aimei, Li Zhigang, Wang Mingliang, Shen Dinggang, Liu Mingxia
School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Science), Jinan, China.
Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Front Neurosci. 2021 Mar 12;15:634124. doi: 10.3389/fnins.2021.634124. eCollection 2021.
Multimodal heterogeneous data, such as structural magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF), are effective in improving the performance of automated dementia diagnosis by providing complementary information on degenerated brain disorders, such as Alzheimer's prodromal stage, i.e., mild cognitive impairment. Effectively integrating multimodal data has remained a challenging problem, especially when these heterogeneous data are incomplete due to poor data quality and patient dropout. Besides, multimodal data usually contain noise information caused by different scanners or imaging protocols. The existing methods usually fail to well handle these heterogeneous and noisy multimodal data for automated brain dementia diagnosis. To this end, we propose a high-order Laplacian regularized low-rank representation method for dementia diagnosis using block-wise missing multimodal data. The proposed method was evaluated on 805 subjects (with incomplete MRI, PET, and CSF data) from the real Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Experimental results suggest the effectiveness of our method in three tasks of brain disease classification, compared with the state-of-the-art methods.
多模态异构数据，如结构磁共振成像（MRI）、正电子发射断层扫描（PET）和脑脊液（CSF），通过提供有关退化性脑部疾病（如阿尔茨海默病前驱期，即轻度认知障碍）的补充信息，在提高自动化痴呆诊断性能方面是有效的。有效地整合多模态数据仍然是一个具有挑战性的问题，尤其是当这些异构数据由于数据质量差和患者退出而不完整时。此外，多模态数据通常包含由不同扫描仪或成像协议引起的噪声信息。现有的方法通常无法很好地处理这些异构且有噪声的多模态数据用于自动化脑痴呆诊断。为此，我们提出了一种用于痴呆诊断的高阶拉普拉斯正则化低秩表示方法，该方法使用分块缺失的多模态数据。所提出的方法在来自真实阿尔茨海默病神经影像倡议（ADNI）队列的805名受试者（具有不完整的MRI、PET和CSF数据）上进行了评估。实验结果表明，与现有最先进的方法相比，我们的方法在脑部疾病分类的三项任务中是有效的。
