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Department of Applied Mathematics and Theoretical Physics

In 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 known. 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 posterior distribution can be approximated with a relatively small number of samples. We show that our method provides an excellent platform for challenging X-ray CT scenarios (e.g. in case of noisy data, limited angle, or sparse angle imaging). We investigate the accuracy and the efficiency of our method on synthetic data. Furthermore, we apply the method to the real-world data, tomographic X-ray data of a lotus root filled with attenuating objects. The numerical results indicate that our method provides an accurate method in detecting boundaries between piecewise smooth regions and quantifies the uncertainty in the prediction, in the context of X-ray CT.

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Meeting ID: 969 3295 0870 Passcode: DRHjehPj

Further information


Sep 22nd 2021
12:30 to 13:30


Virtual (see abstract for Zoom link)


Babak Maboudi Afkham (Technical University of Denmark)


CMIH Hub seminar series