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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