Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution

TL;DR

This paper introduces a Bayesian CNN approach for 3D super-resolution in diffusion MRI, combining heteroscedastic noise modeling and variational dropout to improve accuracy and quantify uncertainty, benefiting clinical applications.

Contribution

It presents a novel Bayesian CNN framework that models both intrinsic and parameter uncertainty for super-resolution in diffusion MRI, achieving state-of-the-art results.

Findings

Improved super-resolution accuracy over existing methods.

Quantification of uncertainty enhances clinical risk assessment.

Beneficial impact on downstream tractography analysis.

Abstract

In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of medical image transformation problems, such as super-resolution (SR) and image synthesis. However, the highly ill-posed nature of such problems results in inevitable ambiguity in the learning of networks. We propose to account for intrinsic uncertainty through a per-patch heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference in the form of variational dropout. We show that the combined benefits of both lead to the state-of-the-art performance SR of diffusion MR brain images in terms of errors compared to ground truth. We further show that the reduced error scores produce tangible benefits in downstream tractography. In addition, the probabilistic nature of the methods naturally confers a mechanism to quantify uncertainty over the super-resolved output. We demonstrate through experiments on both healthy and pathological brains the potential utility of such an uncertainty measure in the risk assessment of the super-resolved images for subsequent clinical use.