Multi-channel (either multi-modality or multi-spectral) has become increasingly interesting in many areas like medical imaging, remote sensing, photography and geophysics to name just a few. A standard approach is to treat the channels separately but as there is an expected correlation between the channels it became popular to treat them jointly. The channels can be coupled by a prior that takes the structure of the scenery / anatomy / geology into account. In this minisymposium we bring together several researchers to present recent theoretical and computational advances in the area of multi-channel imaging and structural priors.

Edge Aligning Image Regularizations

Michael Moeller (University of Siegen)

Incorporating feature space classification in multi-spectral image reconstruction

Simon Arridge (University College London)

Coupled regularization with multiple data discrepancies

Martin Holler (École Polytechnique, Université Paris Saclay)

Magnetic particle imaging using prior information from MRI

Christine Bathke (Center for Industrial Mathematics (ZeTem), University of Bremen)

How Edge Alignment Can Improve Multimodal and Dynamic MR Reconstruction

Eva-Maria Brinkmann (University of Muenster)

Multimodal Sparse Reconstruction Via Learning Cross-Modality Maps

Joao Mota (Heriot-Watt University)

Faster PET-MR image reconstruction by stochastic optimization

Matthias J. Ehrhardt (University of Cambridge)

Organizers:

Simon Arridge (University College London)

Matthias J. Ehrhardt (University of Cambridge)

Keywords:

computed tomography, image reconstruction, inverse problems, nonlinear optimization, statistical inverse estimation methods