M8: Reconstruction Techniques in Quantitative Imaging

In medical applications, it is important that the reconstructed data obtained by the applied imaging methods are comparable between different measurements. One way to achieve this, is to make sure that the recovered data is directly related to a concrete physical parameter. Especially in coupled physics methods, where a combination of different physical effects is used to obtain the image, this can be a very challenging problem and due to the large amount of involved physical parameters often only a qualitative reconstruction is possible.

In this minisymposium, we want to discuss new approaches on how to obtain such quantitative imaging reconstructions, for example in photoacoustic and optical tomography.

Organizers:
Peter Elbau, University of Vienna, Austria, This email address is being protected from spambots. You need JavaScript enabled to view it.
Leonidas Mindrinos, University of Vienna, Austria, This email address is being protected from spambots. You need JavaScript enabled to view it.

Invited Speakers (in alphabetical order):
Alexander Beigl, RICAM, Austria, This email address is being protected from spambots. You need JavaScript enabled to view it.
Optimal Control in Photoacoustic Tomography

Yves Capdeboscq, University of Oxford, United Kingdom, This email address is being protected from spambots. You need JavaScript enabled to view it.
New microstructures with sign changing properties

Aki Pulkkinen, University of Eastern Fınland, Fınland, This email address is being protected from spambots. You need JavaScript enabled to view it.
Estimation of optical properties directly from acoustic time series in QPAT

Kui Ren, The University of Texas at Austin, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.
TBA

Cong Shi, University of Göttingen, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Improved reconstruction method for real time magnetic resonance imaging using motion estimation