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Abstract #1547

ESPReSSo: A Compressed Sensing partial k-space acquisition and reconstruction

Thomas Kstner 1,2 , Sergios Gatidis 1 , Christian Wrslin 1 , Nina Schwenzer 1 , Bin Yang 2 , and Holger Schmidt 3

1 Department of Radiology, Universtity of Tbingen, Tbingen, Germany, 2 Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, 3 Department of Preclinical Imaging & Radiopharmacy, Universtity of Tbingen, Tbingen, Germany

For a clinical feasible Motion Correction setup in a PET/MR system, one should have accurate and sharp images which are acquired as fast as possible. Compressed Sensing promises high acquisition accelerations, whilst penalizing image quality with regard to sharpness. In order to sample the high frequencies denser, we propose a new subsampling scheme which reduces the sampled k-space region to a smaller subset. The k-space reduction has to be corrected for during the Compressed Sensing reconstruction process which uses a combined FOCUSS and POCS algorithm. The framework is called ESPReSSo (comprEssed Sensing PaRtial SubSampling).

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