Uncertainty Quantification of Inclusion Boundaries in the Context of X-ray Tomography

TL;DR

This paper introduces a Bayesian goal-oriented framework for boundary detection in X-ray CT that efficiently quantifies uncertainty, especially effective in noisy or limited data scenarios.

Contribution

It presents a novel boundary-focused Bayesian approach that reduces problem dimensionality, enabling feasible MCMC-based uncertainty quantification in X-ray CT reconstructions.

Findings

Accurately detects boundaries in noisy and limited-angle data

Quantifies uncertainty effectively in boundary predictions

Demonstrates high performance on synthetic and real data

Abstract

In this work, we describe a Bayesian framework for reconstructing the boundaries of piecewise smooth regions in the X-ray computed tomography (CT) problem in an infinite-dimensional setting. In addition to the reconstruction, we are also able to quantify the uncertainty of the predicted boundaries. Our approach is goal oriented, meaning that we directly detect the discontinuities from the data, instead of reconstructing the entire image. This drastically reduces the dimension of the problem, which makes the application of Markov Chain Monte Carlo (MCMC) methods feasible. We show that our method provides an excellent platform for challenging X-ray CT scenarios (e.g., in case of noisy data, limited angle, or sparse angle imaging). We investigate the performance and accuracy of our method on synthetic data as well as on real-world data. The numerical results indicate that our method provides an accurate method in detecting boundaries of piecewise smooth regions and quantifies the uncertainty in the prediction.