M6: Inverse Imaging Problems with Correlations

Recent advances in imaging technologies, such as tomography and microscopy, have led to a significant increase in spatial as well as temporal resolution, and provide image data across different imaging sensors and spectral scales. These developments can enable insights at unprecedented scale. Mathematical imaging and inverse problems is at the heart of many of these technologies.

While these developments have the potential for exciting new applications and ground-breaking discoveries in medicine and biology they pose new challenges to the inverse problems community. In particular, taking into consideration time, different imaging sensors, and spectral imaging data necessitates new mathematical models and numerical algorithms that are able to take advantage of the intrinsic correlation present in these data to provide a reliable reconstruction and efficient analysis of large datasets.

The proposed minisymposium focuses on dynamic, multi-spectral, and multi-sensor aspects of modern imaging modalities. It will provide a platform for the scientific community to discuss novel ideas that exploit correlations in time, across different spectral channels, and across multiple imaging sensors for tasks such as image reconstruction of dynamic and multi-modal imaging data, multi-contrast and spectral image reconstruction, magnetic resonance fingerprinting, and the analysis of, for example, motion in dynamic tomography and microscopy data. The goal of this minisymposium is to bring together researchers from the inverse problems community, to give them the opportunity to present their recent work, and to foster discussion among participants.

Organizers:
Lukas F. Lang, University of Cambridge, United Kingdom, This email address is being protected from spambots. You need JavaScript enabled to view it.
Carola-Bibiane Schönlieb, University of Cambridge, United Kingdom, This email address is being protected from spambots. You need JavaScript enabled to view it.

Invited Speakers (in alphabetical order):
Simon R Arridge, University College London, UK, This email address is being protected from spambots. You need JavaScript enabled to view it.
Incorporating feature space classification in multi-spectral image reconstruction

Martin Benning, University of Cambridge, UK, This email address is being protected from spambots. You need JavaScript enabled to view it.
A non-convex joint reconstruction-segmentation model for the computation of dynamic bubbly flows

Martin Holler, Ecole Polytechnique, This email address is being protected from spambots. You need JavaScript enabled to view it.
Coupled regularization with multiple data discrepancies

Lukas Lang, University of Cambridge, UK, This email address is being protected from spambots. You need JavaScript enabled to view it.,
Joint motion estimation and source identification with an application to the analysis of cell membranes

Felix Lucka, Centrum Wiskunde & Informatica, Netherlands, This email address is being protected from spambots. You need JavaScript enabled to view it.
Variational Models for Dynamic Tomography