New directions in hybrid data tomographyMS12

The reconstruction problems in optical and electrical tomography, such as Optical Diffusion Tomography and Electrical Impedance Tomography, are known to be severely ill-posed. In recent years several modalities have been introduced that circumvents the ill-posedness by introducing another physical modality. This leads to systems of coupled partial differential equations. By using the coupled-physics approach, reconstructions can then be computed with fine resolution and high contrast. To retrieve accurate information from the coupled data one solves the so-called quantitative reconstruction problem. In this mini-symposium we bring together experts working on different quantitative reconstruction problems with hybrid data and discuss future directions.

Tue 05 June at 13:30 in Room F (Palazzina A - Building A floor 2)
Quantitative reconstructions in DOT and PAT
Kui Ren ( University of Texas at Austin )
Acousto-electric tomography based on complete electrode model for isotropic and anisotropic tissues
Changyou Li ( Northwestern Plytechnical University )
Dynamical super-resolution with applications to ultrafast ultrasound
Francisco Romero ( ETH Zurich )
Lamé Parameters Estimation from Static Displacement Field Measurements in the Framework of Nonlinear Inverse Problems
Ekaterina Sherina ( Technical University of Denmark )
Tue 05 June at 16:00 in Room F (Palazzina A - Building A floor 2)
Why does stochastic gradient descent work for inverse problems ?
Bangti Jin ( University College London )
Non-zero constraints in quantitative coupled physics imaging
Giovanni S. Alberti ( University of Genoa )
Quantitative reconstructions by combining photoacoustic and optical coherence tomography
Peter Elbau ( Universität Wien )
Spectral properties of the forward operator in photo-acoustic tomography
Mirza Karamehmedović ( Technical University of Denmark )
Kim Knudsen ( Technical University of Denmark )
Cong Shi ( Georg-August-Universität Göttingen )
hybrid data tomography, inverse problems, partial differential equation models