Enhancing Reliability in Model-based DL Reconstruction: A Systematic Study of MC Dropout for Uncertainty Quantification

Motivation: Monte Carlo (MC) Dropout, a powerful uncertainty quantification (UQ) method for deep learning-based reconstruction, can impact reconstruction performance. Finding ways to enhance reliability assessment without compromising performance is essential. Goal(s): This study aims to provide advisory information on how to incorporate MC Dropout into a model-based unrolled neural network, and to evaluate the reliability of UQ. Approach: Different architectures with varying dropout rates are used to assess image quality. Images with visible structural aberrations and artificial perturbation are tested. Results: Findings indicate that appropriate MC Dropout configurations improve reconstruction quality, and UQ maps effectively identify structural anomalies in images. Impact: This research enhances the reliability of DL reconstructions by systematically investigating MC Dropout’s impact on reconstruction performance, particularly in scenarios lacking ground-truth references. The findings guide the incorporation of uncertainty quantification techniques, improving the overall quality of medical imaging applications.