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SUMMARY:Uncertainty Quanti cation in CT pulmonary angiography - Audrey R
 epetti (Heriot-Watt University)
DTSTART:20230327T151000Z
DTEND:20230327T160000Z
UID:TALK198208@talks.cam.ac.uk
DESCRIPTION:Computed tomography (CT) imaging of the thorax is widely used 
 for the detection and&nbsp\;monitoring of pulmonary embolism (PE). However
 \, CT images can contain artifacts due to&nbsp\;the acquisition or the pro
 cesses involved in image reconstruction. Radiologists often have to&nbsp\;
 distinguish between such artifacts and actual PEs. Our main contribution c
 omes in the form of&nbsp\;a scalable hypothesis testing method for CT\, to
  enable quantifying uncertainty of possible PEs.&nbsp\;In particular\, we 
 introduce a Bayesian Framework to quantify the uncertainty of an observed&
 nbsp\;compact structure that can be identified as a PE. We assess the abil
 ity of the method to operate&nbsp\;under high noise environments and with 
 insufficient data.\nJoint work with&nbsp\;Adwaye M Rambojun\, Hend Komber\
 , Jennifer Rossdale\, Jay Suntharalingam\,&nbsp\;Jonathan C L Rodrigues\, 
 and Matthias J Ehrhardt
LOCATION:Seminar Room 1\, Newton Institute
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