Limited data problems in imagingMS7

Since the breakthrough of CT, many innovative concepts have been developed such as dynamic imaging, photoacoustic tomography, Compton imaging, etc, and studied via associated inverse problems. Each novel technique brings new mathematical challenges and technical constraints. The limited data issue constitutes the most common constraint and one of the main challenges. In this case, a substantial part of the data are unavailable changing deeply the nature of the ill-posed problem and making image reconstruction more complex. This minisymposium will bring together researchers from the inverse problems and imaging communities related to this issue and will promote discussions among participants.

A Complete Characterization of Artifacts in Arbitrary Limited Data Tomography Problems
Todd Quinto (Tufts University)
Challenges in learning-based MR image reconstruction
Kerstin Hammernik (Graz University of Technology)
Iterative image reconstruction for limited angular range scanning in digital breast tomosynthesis
Emil Sidky (University of Chicago)
Machine learning for imaging problems with limited data
Allard Hendriksen (CWI)
On limited data issues in Compton scattering imaging
Gaël Rigaud (Saarland University)
A new reconstruction strategy for compressed sensing photoacoustic tomography
Markus Haltmeier (University Innsbruck)
Reconstructions from incomplete tomographic data in PAT
Jürgen Frikel (OTH Regensburg)
Algorithms for dynamic tomography with limited data
Samuli Siltanen (University of Helsinki)
Jürgen Frikel (OTH Regensburg)
Bernadette Hahn (University of Würzburg)
Gaël Rigaud (Saarland University)
computed tomography, image reconstruction, inverse problems