Enhancing computation speed and accuracy in deep image prior-based parameter mapping.

Abstract

Purpose: To make Deep Image Prior (DIP)-based parameter mapping faster, more accurate, and suitable for clinical applications, with added support for multislice and 3D datasets.

Methods: DIP leverages the inherent structure of an untrained image generator to address various inverse imaging tasks, including denoising. In this study, we enhance DIP-based denoising for parameter mapping with warm-start across neighboring image slices and different patient subjects. This approach leverages spatial similarity to reduce computation time. Additionally, we introduce an early-stopping criterion that selects the denoising level based on MRI signal noise. We further investigate uncertainty calibration through dropout probability tuning to address issues with miscalibrated uncertainty estimates from Monte Carlo dropout. Furthermore, we explore reducing computation time by tuning learning rates and network complexity.

Results: We show that reusing image generator weights with warm-start significantly accelerates the denoising of large datasets, reducing computation time by 78% to 95% across various tasks. The early stopping approach proved effective, eliminating the need to manually select the number of optimization steps. Dropout probability tuning helps mitigate the issue of miscalibrated uncertainty, though further refinements are necessary, particularly to achieve better calibration on a per-pixel level. Additionally, tuning learning rates and network complexity provided valuable insights into optimizing the model for different tasks.

Conclusion: The proposed developments enable DIP-based parameter mapping to become faster, more accurate, and, consequently, more practical and scalable for clinical applications involving larger datasets.