Frontiers in aging neuroscience · 2026
Differentiating early Parkinson's disease from multiple system atrophy on multimodal PET/MRI using atlas-guided segmentation and vision transformer classification.
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Cong Chao, Zhang Xuewei, Rifat Shams Tabrez, Tan Hongyun, Sun Jinju, Peng Wenping, Chen Xiao
Аннотация
INTRODUCTION: Early differentiation between Parkinson's disease (PD) and multiple system atrophy-parkinsonian type (MSA-P) remains clinically challenging because of overlapping motor symptoms and subtle imaging abnormalities.
METHODS: We developed a fully automated multimodal PET/MRI framework for early PD-MSA-P differentiation that integrates atlas-guided subject-specific label generation, nnU-Net segmentation of the caudate nuclei and putamina, multimodal image fusion, and Vision Transformer classification with multi-slice voting (ViT+MSV). A total of 155 early-stage patients, including 121 with PD and 34 with MSA-P, were evaluated using patient-level stratified 4-fold cross-validation.
RESULTS: The segmentation model achieved an overall Dice score of 0.853 (95% CI, 0.830-0.877) and an IoU of 0.747 (95% CI, 0.725-0.770). ViT+MSV achieved a cross-validated AUC of 0.947 (95% CI, 0.905-0.981), an accuracy of 0.929 (95% CI, 0.890-0.968), a sensitivity of 0.942 (95% CI, 0.900-0.982), and a specificity of 0.882 (95% CI, 0.765-0.974). The combination of [18F]fluorodeoxyglucose (FDG) PET, [11C]CFT dopamine-transporter PET, and T2-weighted imaging (T2WI) showed the best overall classification performance. Grad-CAM visualizations showed that class-discriminative gradient-based saliency was mainly concentrated on the bilateral putamen.
DISCUSSION: These findings suggest that anatomically guided multimodal PET/MRI analysis with transformer-based classification may support early PD-MSA-P differentiation while providing spatially interpretable evidence for model decisions.
METHODS: We developed a fully automated multimodal PET/MRI framework for early PD-MSA-P differentiation that integrates atlas-guided subject-specific label generation, nnU-Net segmentation of the caudate nuclei and putamina, multimodal image fusion, and Vision Transformer classification with multi-slice voting (ViT+MSV). A total of 155 early-stage patients, including 121 with PD and 34 with MSA-P, were evaluated using patient-level stratified 4-fold cross-validation.
RESULTS: The segmentation model achieved an overall Dice score of 0.853 (95% CI, 0.830-0.877) and an IoU of 0.747 (95% CI, 0.725-0.770). ViT+MSV achieved a cross-validated AUC of 0.947 (95% CI, 0.905-0.981), an accuracy of 0.929 (95% CI, 0.890-0.968), a sensitivity of 0.942 (95% CI, 0.900-0.982), and a specificity of 0.882 (95% CI, 0.765-0.974). The combination of [18F]fluorodeoxyglucose (FDG) PET, [11C]CFT dopamine-transporter PET, and T2-weighted imaging (T2WI) showed the best overall classification performance. Grad-CAM visualizations showed that class-discriminative gradient-based saliency was mainly concentrated on the bilateral putamen.
DISCUSSION: These findings suggest that anatomically guided multimodal PET/MRI analysis with transformer-based classification may support early PD-MSA-P differentiation while providing spatially interpretable evidence for model decisions.