头动会干扰脑PET影像的阿尔茨海默病分类，影响机器学习效果。
Head Motion Degrades Machine Learning Classification of Alzheimer's Disease from Positron Emission Tomography
- 仅用PET图像构建分类模型，对比有无运动校正的效果。
- 未校正运动时，分类准确率下降10%和5%，关键数据丢失。
- 提醒临床使用PET+AI诊断时必须解决头动问题，尤其在老年患者中。
脑正电子发射断层扫描（PET）广泛用于阿尔茨海默病（AD）的研究与临床诊断，但其潜力受限于缺乏便携式运动校正方案，尤其在临床环境中。头部运动会降低图像质量并导致示踪剂摄取定量误差。本研究证明，它还会偏差基于机器学习的AD分类结果。我们提出一种仅依赖PET图像的二分类算法，发现其在运动校正后的图像上可实现高精度分类（正常认知与AD）。但在未校正运动的图像上，分类准确率显著下降：在128例¹¹C-UCB-J和173例¹⁸F-FDG扫描队列中，分别在20例测试样本上下降10%和5%。结果强调了高效运动校正方法对充分发挥PET驱动的机器学习诊断能力至关重要。
原文摘要 · Abstract (English)
Brain positron emission tomography (PET) imaging is broadly used in research and clinical routines to study, diagnose, and stage Alzheimer's disease (AD). However, its potential cannot be fully exploited yet due to the lack of portable motion correction solutions, especially in clinical settings. Head motion during data acquisition has indeed been shown to degrade image quality and induces tracer uptake quantification error. In this study, we demonstrate that it also biases machine learning-based AD classification. We start by proposing a binary classification algorithm solely based on PET images. We find that it reaches a high accuracy in classifying motion corrected images into cognitive normal or AD. We demonstrate that the classification accuracy substantially decreases when images lack motion correction, thereby limiting the algorithm's effectiveness and biasing image interpretation. We validate these findings in cohorts of 128 $^{11}$C-UCB-J and 173 $^{18}$F-FDG scans, two tracers highly relevant to the study of AD. Classification accuracies decreased by 10% and 5% on 20 $^{18}$F-FDG and 20 $^{11}$C-UCB-J testing cases, respectively. Our findings underscore the critical need for efficient motion correction methods to make the most of the diagnostic capabilities of PET-based machine learning.
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