A Symmetric Regressor for MRI-Based Assessment of Striatal Dopamine Transporter Uptake in Parkinson's Disease

2024-04-18

arXiv.org

AI Summary

Current dopamine transporter (DAT) imaging, though effective for assessing Parkinson’s disease (PD) severity, is costly and involves ionizing radiation, limiting its clinical adoption—especially in primary care. This paper proposes a non-invasive, MRI-based alternative: a symmetric deep regression model that predicts bilateral striatal DAT binding using only T1-weighted MRI of the substantia nigra. Methodologically, we introduce three key innovations: (1) a bilateral paired input–output architecture; (2) an anatomy-guided symmetric loss function leveraging prior knowledge of brain symmetry; and (3) symmetric Monte Carlo Dropout for calibrated uncertainty estimation and quantifiable clinical interpretability. Evaluated on a multicenter cohort of 734 subjects, our model significantly outperforms standard regression baselines—achieving lower prediction error, enhanced feature robustness, and higher coverage of predictive uncertainty intervals. The framework establishes a generalizable, radiation-free, and interpretable paradigm for quantitative PD severity assessment.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Predicts striatal DAT uptake from nigral MRI patches
• Incorporates symmetry between right and left nigrae
• Provides uncertainty estimation for DAT uptake prediction

Innovation

• Symmetric regressor predicts DAT uptake from MRI
• Paired input-output model for bilateral striata prediction
• Symmetric Monte-Carlo dropout enhances uncertainty estimation