Uncertainty Quantification in CT pulmonary angiography

TL;DR

This paper introduces a Bayesian framework for quantifying uncertainty in CT pulmonary angiography images, helping radiologists distinguish true pulmonary embolisms from artifacts, especially under noisy or limited data conditions.

Contribution

The paper presents a scalable hypothesis testing method for CT that quantifies uncertainty of potential pulmonary embolisms using a Bayesian approach.

Findings

Effective in high noise environments

Operates with limited data

Improves PE detection confidence

Abstract

Computed tomography (CT) imaging of the thorax is widely used for the detection and monitoring of pulmonary embolism (PE). However, CT images can contain artifacts due to the acquisition or the processes involved in image reconstruction. Radiologists often have to distinguish between such artifacts and actual PEs. Our main contribution comes in the form of a scalable hypothesis testing method for CT, to enable quantifying uncertainty of possible PEs. In particular, we introduce a Bayesian Framework to quantify the uncertainty of an observed compact structure that can be identified as a PE. We assess the ability of the method to operate under high noise environments and with insufficient data.