We just submitted our second major paper on how to improve dynamic, high-resolution 3D photoacoustic tomography (4D PAT) by novel imaging techniques. It combines compressed sensing data acquisition with a generic spatio-temporal regularization framework that incorporates a PDE-based motion model. The paper can be found on arXiv, big thanks to all the co-authors! [Update: It has been published in SIAM Imaging Science.]
Blending deep learning and iterative image reconstruction has shown great promise to obtain high quality reconstructions from noisy, sub-sampled data and is therefore hot topic in inverse problems at the moment. We adopted an particular approach to enhance the reconstruction of blood vessel structures from sub-sampled, limited-view 3D photoacoustic tomography (PAT) in vivo. Many thanks to Andreas Hauptmann, who did the main work for this exciting project. The paper with all the results can be found on arXiv. [Update: It has been published in IEEE-TMI.]
My colleague Ville Rimpiläinen wrote a nice conference proceedings about our work on using the Bayesian approximation error (BAE) approach to compensate for errors in EEG source reconstructions caused by the inherent uncertainty in the skull conductivity. It can be found on arXiv and he will hopefully present this work at the European Medical and Biological Engineering Conference (EMBEC) in Tampere, Finland.