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        <datestamp>2026-09-25T05:17:32Z</datestamp>
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          <dc:title>Table 1_Differentiating early Parkinson’s disease from multiple system atrophy on multimodal PET/MRI using atlas-guided segmentation and vision transformer classification.docx</dc:title>
          <dc:creator>Chao Cong (14577518)</dc:creator>
          <dc:creator>Xuewei Zhang (1408315)</dc:creator>
          <dc:creator>Shams Tabrez Rifat (25107508)</dc:creator>
          <dc:creator>Hongyun Tan (25107511)</dc:creator>
          <dc:creator>Jinju Sun (10322066)</dc:creator>
          <dc:creator>Wenping Peng (25107514)</dc:creator>
          <dc:creator>Xiao Chen (260187)</dc:creator>
          <dc:subject>Neuroscience</dc:subject>
          <dc:subject>computer assisted diagnosis</dc:subject>
          <dc:subject>deep learning</dc:subject>
          <dc:subject>multimodal imaging</dc:subject>
          <dc:subject>multiple system atrophy</dc:subject>
          <dc:subject>Parkinson’s disease</dc:subject>
          <dc:description>Introduction&lt;p&gt;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.&lt;/p&gt;Methods&lt;p&gt;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.&lt;/p&gt;Results&lt;p&gt;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 [&lt;sup&gt;18&lt;/sup&gt;F]fluorodeoxyglucose (FDG) PET, [&lt;sup&gt;11&lt;/sup&gt;C]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.&lt;/p&gt;Discussion&lt;p&gt;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.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-25T05:17:32Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.3389/fnagi.2026.1928959.s001</dc:identifier>
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          <dc:rights>CC BY 4.0</dc:rights>
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