Symmetric 3D Convolutional Network with Uncertainty Estimation for MRI-Based Striatal DaT-Uptake Assessment in Parkinson’s Disease

Dopamine transporter (DaT) imaging is commonly used for monitoring Parkinson’s disease (PD), where the amount of striatal DaT uptake serves as the PD severity indicator. MRI of the nigral region has recently emerged as a safer and more available alternative. This work introduces a 3D convolutional network-based symmetric regressor for predicting the DaT-uptake amount from nigral MRI patches. Unlike the typical deep networks, the proposed model leverages the lateral symmetry between right and left nigrae by incorporating a paired input–output architecture that concurrently predicts DaT uptakes for both the right and left striata, while employing a symmetric loss that constrains the difference between right-to-left predictions. To improve model reliability, we also propose a symmetric Monte Carlo dropout strategy for providing fruitful uncertainty estimates about the prediction. Evaluated on 734 3D nigral patches, our symmetric regressor demonstrated a 12.11% improvement in prediction error compared to standard deep-learning models. Furthermore, the reliability was enhanced, resulting in a 5% reduction in the prediction uncertainty interval at a 95% coverage probability for the true DaT-uptake amount. Our findings demonstrate that integrating structural symmetry into model design is a powerful strategy for achieving accurate and reliable predictions for PD severity analysis.