High-resolution x-ray imaging from sparse, incomplete and uncertain data MS55

As the sophistication and speed of today's experiments grow, collecting the most informative data has become greatly relevant, necessitating the development of algorithms that can provide good quality reconstructions from measurements due to reduced photon efficiency, higher radiation doses, and inaccuracies of the stages, which leads to noisy and sparse datasets. In this talk, I will target scanning-based x-ray imaging and microscopy applications, and describe how computational approaches can be adopted to enable faster and reliable information extraction from measurements.

This presentation is part of Minisymposium “MS55 - Advances of regularization techniques in iterative reconstruction (2 parts)
organized by: Zichao (Wendy) Di (Argonne National Lab) , Marc Aurèle Gilles (Cornell University) .

Doga Gursoy (Argonne National Laboratory)
computed tomography, image reconstruction, inverse problems