Dopamine transporter (DAT) imaging is commonly used for monitoring Parkinson's disease (PD), where striatal DAT uptake amount is computed to assess PD severity. However, DAT imaging has a high cost and the risk of radiance exposure and is not available in general clinics. Recently, MRI patch of the nigral region has been proposed as a safer and easier alternative. This paper proposes a symmetric regressor for predicting the DAT uptake amount from the nigral MRI patch. Acknowledging the symmetry between the right and left nigrae, the proposed regressor incorporates a paired input-output model that simultaneously predicts the DAT uptake amounts for both the right and left striata. Moreover, it employs a symmetric loss that imposes a constraint on the difference between right-to-left predictions, resembling the high correlation in DAT uptake amounts in the two lateral sides. Additionally, we propose a symmetric Monte-Carlo (MC) dropout method for providing a fruitful uncertainty estimate of the DAT uptake prediction, which utilizes the above symmetry. We evaluated the proposed approach on 734 nigral patches, which demonstrated significantly improved performance of the symmetric regressor compared with the standard regressors while giving better explainability and feature representation. The symmetric MC dropout also gave precise uncertainty ranges with a high probability of including the true DAT uptake amounts within the range.

翻译：多巴胺转运体（DAT）成像常用于监测帕金森病（PD），通过计算纹状体DAT摄取量来评估PD严重程度。然而，DAT成像成本高昂，存在辐射暴露风险，且在普通诊所不易获得。最近，黑质区域的MRI图像片段被提出作为一种更安全、更便捷的替代方案。本文提出一种对称回归器，用于从黑质MRI图像片段预测DAT摄取量。考虑到左右黑质之间的对称性，所提出的回归器采用了一种配对输入-输出模型，可同时预测左右两侧纹状体的DAT摄取量。此外，它采用了一种对称损失函数，对左右两侧预测值之间的差异施加约束，这类似于两侧DAT摄取量存在的高度相关性。另外，我们提出了一种对称蒙特卡洛（MC）dropout方法，用于为DAT摄取预测提供有效的 uncertainty 估计，该方法利用了上述对称性。我们在734个黑质图像片段上评估了所提出的方法，结果表明，与标准回归器相比，对称回归器的性能显著提升，同时提供了更好的可解释性和特征表示。对称MC dropout也给出了精确的 uncertainty 范围，其包含真实DAT摄取量的概率很高。