Estimating Uncertainty in PET Image Reconstruction via Deep Posterior Sampling

TL;DR

This paper introduces a deep learning method using posterior sampling with GANs to quantify uncertainty in PET image reconstruction, aiding medical decision-making by providing more reliable imaging results.

Contribution

It presents a novel deep learning approach that approximates posterior sampling for uncertainty quantification in PET imaging, integrating MRI and low-dose PET data.

Findings

Generates high-quality posterior samples

Provides meaningful uncertainty estimates

Improves reliability of PET reconstructions

Abstract

Positron emission tomography (PET) is an important functional medical imaging technique often used in the evaluation of certain brain disorders, whose reconstruction problem is ill-posed. The vast majority of reconstruction methods in PET imaging, both iterative and deep learning, return a single estimate without quantifying the associated uncertainty. Due to ill-posedness and noise, a single solution can be misleading or inaccurate. Thus, providing a measure of uncertainty in PET image reconstruction can help medical practitioners in making critical decisions. This paper proposes a deep learning-based method for uncertainty quantification in PET image reconstruction via posterior sampling. The method is based on training a conditional generative adversarial network whose generator approximates sampling from the posterior in Bayesian inversion. The generator is conditioned on reconstruction from a low-dose PET scan obtained by a conventional reconstruction method and a high-quality magnetic resonance image and learned to estimate a corresponding standard-dose PET scan reconstruction. We show that the proposed model generates high-quality posterior samples and yields physically-meaningful uncertainty estimates.