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Abstract #1178

Optimal Transport Based Convex Hybrid Image and Motion-Field Reconstruction

Ingmar Middelhoff1, Matthias Schlögl2, Adrián Martín Fernández3, Silvio Fanzon4,5, Kristian Bredies4,5, and Rudolf Stollberger1,5
1Institute of Medical Engineering, TU Graz, Graz, Austria, 2Solgenium OG, Linz, Austria, 3Department of Information and Communications Technologies, Pompeu Fabra University, Barcelona, Spain, 4Institute of Mathematics and Scientific Computing, NAWI Graz, University of Graz, Graz, Austria, 5BioTechMed-Graz, Graz, Austria

In this study we present an approach that combines sub-sampled encoding reconstruction and simultaneous object motion computation. For that purpose Optimal Transport is used as convex regularization for motion-afflicted measurements. It reconstructs explicit pixel-wise motion fields simultaneously to the image series. Results based on simulated data show that 8-frame image series can be reconstructed in great detail from 4-fold undersampled k-space series data from a single coil. The high potential of the presented method could be shown for the reconstruction of undersampled image series. For the recovery of the motion fields, further improvements are still necessary.

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