Fang Yuqi, Wang Wei, Wang Qianqian, Li Hong-Jun, Liu Mingxia
Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China.
Med Image Comput Comput Assist Interv. 2024 Oct;15011:113-123. doi: 10.1007/978-3-031-72120-5_11. Epub 2024 Oct 3.
Asymptomatic neurocognitive impairment (ANI) is a predominant form of cognitive impairment among individuals infected with human immunodeficiency virus (HIV). The current diagnostic criteria for ANI primarily rely on subjective clinical assessments, possibly leading to different interpretations among clinicians. Some recent studies leverage structural or functional MRI containing objective biomarkers for ANI analysis, offering clinicians companion diagnostic tools. However, they mainly utilize a single imaging modality, neglecting complementary information provided by structural and functional MRI. To this end, we propose an attention-enhanced structural and functional MRI fusion (ASFF) framework for HIV-associated ANI analysis. Specifically, the ASFF first extracts data-driven and human-engineered features from structural MRI, and also captures functional MRI features via a graph isomorphism network and Transformer. A is then designed to model the underlying relationship between structural and functional MRI. Additionally, a is introduced to encourage consistency of multimodal features, facilitating effective feature fusion. Experimental results on 137 subjects from an HIV-associated ANI dataset with T1-weighted MRI and resting-state functional MRI show the effectiveness of our ASFF in ANI identification. Furthermore, our method can identify both modality-shared and modality-specific brain regions, which may advance our understanding of the structural and functional pathology underlying ANI.
无症状神经认知障碍（ANI）是人类免疫缺陷病毒（HIV）感染者中认知障碍的主要形式。目前ANI的诊断标准主要依赖主观临床评估，这可能导致临床医生之间的不同解读。最近的一些研究利用包含客观生物标志物的结构或功能磁共振成像（MRI）进行ANI分析，为临床医生提供辅助诊断工具。然而，它们主要使用单一成像模态，忽略了结构和功能MRI提供的互补信息。为此，我们提出了一种用于HIV相关ANI分析的注意力增强型结构和功能MRI融合（ASFF）框架。具体而言，ASFF首先从结构MRI中提取数据驱动和人工设计的特征，并通过图同构网络和Transformer捕捉功能MRI特征。然后设计一个 来建模结构和功能MRI之间的潜在关系。此外，引入一个 以促进多模态特征的一致性，便于进行有效的特征融合。对来自一个包含T1加权MRI和静息态功能MRI的HIV相关ANI数据集的137名受试者的实验结果表明，我们的ASFF在ANI识别中是有效的。此外，我们的方法可以识别模态共享和模态特定的脑区，这可能会增进我们对ANI潜在结构和功能病理学的理解。
