Uncertainty quantification in computed tomography pulmonary angiography

Adwaye M. Rambojun, Hend Komber, Jennifer Rossdale, Jay Suntharalingam, Jonathan C. L. Rodrigues, Matthias J. Ehrhardt*, Audrey Repetti*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.

Original languageEnglish
Article numberpgad404
JournalPNAS Nexus
Volume3
Issue number1
Early online date23 Jan 2024
DOIs
Publication statusPublished - Jan 2024

Keywords

  • Bayesian
  • medical imaging
  • optimization
  • pulmonary embolism
  • uncertainty quantification

ASJC Scopus subject areas

  • General

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