Time-dependent imaging problems have a broad range of applications and are a lively field of research. Classical tomographic techniques represent inverse problems that are stationary in the sense that neither the searched quantity, nor the data depend on time. So far solution methods for dynamic inverse problems seemed too time-consuming and demanded too much memory capacity to become interesting for real-world applications. However, imaging modalities with data and/or parameters that depend on time attracted much notice over the last years, demanding for innovative inversion and analysis techniques that particularly take into account the physical meaning of the additional temporal variable.

On dynamic photoacoustic imaging with optical flow constraints

Marta Betcke (University College London)

Dynamic inverse problems for wave equations

Thies Gerken (University of Bremen)

Algorithms for motion compensation

Bernadette Hahn (University of Würzburg)

Reconstruction and Decomposition of Dynamic and Undersampled MR Data

Meike Kinzel (University of Münster)

A reconstruction method for multi-modal imaging

Leonidas Mindrinos (Computational Science Center, University of Vienna)

Numerical treatment of inverse heat transfer problems

Dimitri Rothermel (Saarland University)

All-at-once versus reduced version of Landweber-Kaczmarz for parameter identification in time dependent problems

Tram Thi Ngoc Nguyen (Alpen-Adria-Universität Klagenfurt)

Regularizing sequential subspace optimization for the identification of the stored energy of a hyperelastic structure

Rebecca Klein (Saarland University)

Organizers:

Thomas Schuster (Saarland University)

Anne Wald (Saarland University)

Keywords:

computed tomography, image reconstruction, image registration, image representation, inverse problems