Uncertainty Quantification in CT pulmonary angiography

Adwaye Rambojun, Hend Komber, Jennifer Rossdale, Jay Suntharalingam, Jonathan Rodrigues, Matthias Ehrhardt, Audrey Repetti

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Number of pages6
JournalProceedings of the National Academy of Sciences
Publication statusSubmitted - Dec 2022


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