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)

Organizers:

Jürgen Frikel (OTH Regensburg)

Bernadette Hahn (University of Würzburg)

Gaël Rigaud (Saarland University)

Keywords:

computed tomography, image reconstruction, inverse problems