Parcel2Progression：一种用于阿尔茨海默病诊断的解剖学感知纵向框架

中文摘要 AI 辅助

阿尔茨海默病（AD）进展是一个纵向过程，早期阶段存在细微的病理线索。然而，计算限制使得大多数神经影像模型要么牺牲空间信息，要么限制纵向扫描的数量。我们旨在克服这一瓶颈，充分利用高分辨率、可变长度的T1加权结构MRI（4D sMRI）扫描序列。我们引入了Parcel2Progression（P2P），这是一种纵向Transformer框架，它使用图谱引导的 parcel 编码器（Atlas-guided Parcel Encoder）将3D扫描标记化为一组更丰富的、基于解剖学的表示，然后纵向Transformer将不规则、任意长度的纵向访视与患者年龄进行整合。这种协同作用带来两个关键优势：（1）特定 parcel 的可解释性；（2）长期分析的计算可处理性，与 naive 4D ViT 的二次成本相比，该成本随扫描数量线性扩展。P2P在ADNI、AIBL和MIRIAD数据集上的轻度认知障碍（MCI）向AD转化预测以及AD与认知正常（CN）分类任务中，均优于现有工作和基线方法。利用纵向扫描使AD分类和MCI转化预测任务的平衡准确率分别比单扫描基线提高了高达5%和7%。使用 parcel 显著性和注意力rollouts的可解释性分析显示，AD和MCI受试者中存在临床一致的萎缩模式。我们还在合成数据集上展示了该框架在异常检测中的可靠性，并测试了该模型对其他神经退行性疾病（如额颞叶痴呆）的泛化能力。

英文摘要

Alzheimer's disease (AD) progression is a longitudinal process with subtle pathological cues in the early stages. Yet, computational constraints have limited most neuroimaging models to either compromise spatial information or limit the number of longitudinal scans. We aim to overcome this bottleneck and fully leverage high-resolution, variable-length T1w structural MRI (4D sMRI) scan sequences. We introduce Parcel2Progression (P2P), a Longitudinal Transformer Framework which tackles this challenge using an Atlas-guided Parcel Encoder that tokenizes 3D scans into a set of richer anatomically grounded representations. A Longitudinal Transformer then integrates irregular, arbitrary-length longitudinal visits with patient age. This synergy delivers two key advantages: (1) parcel-specific interpretability, and (2) computational tractability for long-term analysis, which scales linearly with the number of scans compared to a naive quadratic 4D ViT cost. P2P outperforms prior works and baselines in both MCI (Mild Cognitive Impairment) to AD conversion prediction and AD vs. CN (Cognitively Normal) classification tasks across ADNI, AIBL, and MIRIAD datasets. Leveraging longitudinal scans boosts performance over single-scan baselines by up to 5% and 7% in balanced accuracy for AD classification and MCI conversion prediction tasks, respectively. Interpretability analysis using parcel saliencies and attention rollouts reveals clinically consistent atrophy patterns in AD and MCI subjects. We also demonstrate the frameworks' reliability in anomaly detection using a synthetic dataset, and test the model's generalizability for other neurodegenerative diseases like Frontotemporal Dementia.

发表机构

• Indian Institute of Technology Hyderabad（印度理工学院海得拉巴分校）

机构由 AI 辅助整理，请以论文原文为准。