Solving Inverse Problems in minutes: Software for imagingMS76

Within the research community, there is a growing need to test and evaluate reconstruction methods on various problems. Likewise, implementations based on re-usable components foster collaborations. Hence, software frameworks that can handle large data and complex mathematical constructions is becoming increasingly important in research. Over time, a number of software packages for inverse problems has emerged, including efficient implementations of forward models and state-of-the-art solution methods. The aim of this minisymposium is to pick up current trends and highlight software packages for inverse problems, to encourage collaboration and to make promising mathematical and numerical approaches better known in the community.

Software for prototyping inverse problems with real data
Holger Kohr (Thermo Fisher Scientific)
STIR: an Open Source library for PET and SPECT image reconstruction
Kris Thielemans (Institute of Nuclear Medicine, University of College London)
Solving inverse problems in imaging with Shearlab.jl
Héctor Andrade Loarca (TU Berlin)
Iterative tomography within minutes using RTK
Cyril Mory (CREATIS, Lyon)
Learning to Solve Inverse Problems with ODL
Jonas Adler (KTH Royal Institute of Technology)
Using the ASTRA Toolbox for implementing tomographic reconstruction algorithms
Willem Jan Palenstijn (Centrum Wiskunde & Informatica, Amsterdam)
The RVL Framework Applied to Full Waveform Inversion
Mario Bencomo (Department of Computational and Applied Mathematics (CAAM), Rice University)
Jonas Adler (KTH Royal Institute of Technology)
Holger Kohr (Thermo Fisher Scientific)
Ozan Öktem (KTH - Royal Institute of Technology)
computed tomography, deep learning, image enhancement, image reconstruction, image reconstruction, image registration, image representation, inverse problems, machine learning, nonlinear optimization, regularization, software