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SUMMARY:Uncertainty Quantification of Inclusion Boundaries in the Context 
 of X-ray Tomography
DTSTART:20211130T161500
DTEND:20211130T171500
DTSTAMP:20260505T081339Z
UID:3e5c556419cd7810fbd22761d7fdc6069a340dd1dbbf3aeb2ed5d8bc
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr. Babak Maboudi Afkham\, DTU\nIn my talk\, I will describe 
 a Bayesian framework for the X-ray computed tomography (CT) problem in an 
 infinite-dimensional setting. We consider reconstructing piecewise smooth 
 fields with discontinuities where the interface between regions is not kno
 wn. Furthermore\, we quantify the uncertainty in the prediction. Directly 
 detecting the discontinuities\, instead of reconstructing the entire image
 \, drastically reduces the dimension of the problem. Therefore\, the poste
 rior distribution can be approximated with a relatively small number of sa
 mples. We show that our method provides an excellent platform for challeng
 ing X-ray CT scenarios (e.g. in case of noisy data\, limited angle\, or sp
 arse angle imaging). We investigate the accuracy and the efficiency of our
  method on synthetic data. Furthermore\, we apply the method to the real-w
 orld data\, tomographic X-ray data of a lotus root filled with attenuating
  objects. The numerical results indicate that our method provides an accur
 ate method in detecting boundaries between piecewise smooth regions and qu
 antifies the uncertainty in the prediction\, in the context of X-ray CT.
LOCATION:https://epfl.zoom.us/j/84030108577?pwd=bHh2Z3J2YllvTWdteHA3MHhVcn
 IyUT09
STATUS:CONFIRMED
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