Uncertainty quantification in computed tomography pulmonary angiography

Author:

Rambojun Adwaye M1ORCID,Komber Hend2ORCID,Rossdale Jennifer2ORCID,Suntharalingam Jay23,Rodrigues Jonathan C L2ORCID,Ehrhardt Matthias J1ORCID,Repetti Audrey45ORCID

Affiliation:

1. Department of Mathematical Sciences, University of Bath , Bath BA2 7JU , UK

2. Royal United Hospital , Bath BA1 3NG , UK

3. Department of Life Sciences, University of Bath , Bath BA2 7JU , UK

4. School of Engineering and Physical Sciences, School of Mathematical and Computer Sciences, Heriot-Watt University , Edinburgh EH14 4AS , UK

5. Maxwell Institute for Mathematical Sciences , Edinburgh EH8 9BT , UK

Abstract

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. We provide a proof of concept 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.

Funder

EPSRC

Leverhulme Trust

Publisher

Oxford University Press (OUP)

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