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Uncertainty Quantification of Inclusion Boundaries

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RNTW02 - Rich and non-linear tomography in medical imaging, materials and non destructive testing

In this talk, we will describe a Bayesian framework for reconstructing the boundaries of piecewise constant regions in the X-ray computed tomography (CT) problem in an infinite-dimensional setting. In addition to the reconstruction, we are also able to quantify the uncertainty of the predicted boundaries. Our approach is goal oriented, meaning that we directly detect the discontinuities from the data, instead of reconstructing the entire image. This drastically reduces the dimension of the problem, which makes the application of Markov Chain Monte Carlo (MCMC) methods feasible. We will show that the new method provides an excellent platform for challenging X-ray CT scenarios (e.g., in case of noisy data, limited angle, or sparse angle imaging).

This talk is part of the Isaac Newton Institute Seminar Series series.

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