How microlocal analysis can inform algorithm developmentMS33

In limited data tomography, some data are missing. Because of the missing data, certain features of the object might be invisible from the data, and algorithms might create streaks are independent of the object. I will explain the artifacts and use microlocal analysis to explain how to suppress them while keeping most of the visible features of the object. I will provide reconstructions from real and simulated data that illustrate the theory.

This presentation is part of Minisymposium “MS33 - Advances in reconstruction algorithms for computed tomography (4 parts)
organized by: Gunay Dogan (Theiss Research, NIST) , Harbir Antil (George Mason University) , Elena Loli Piccolomini (Dept. Computer Science and Engineering, University of Bologna) , Samuli Siltanen (University of Helsinki) .

Todd Quinto (Tufts University)
Leise Borg (University of Copenhagen)
Jakob Jorgensen (University of Manchester)
Jürgen Frikel (OTH Regensburg)
computed tomography, image reconstruction, inverse problems, microlocal analysis