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Abstract #3424

Joint MR-PET reconstruction using vector valued Total Generalized Variation

Florian Knoll 1,2 , Martin Holler 3 , Thomas Koesters 1,2 , and Daniel K Sodickson 1,2

1 Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, United States, 2 Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, United States, 3 Department of Mathematics and Scientific Computing, University of Graz, Graz, Austria

It was recently shown that simultaneously acquired data from state-of-the-art MR-PET systems can be reconstructed simultaneously using the concept of joint sparsity, yielding benefits for both MR and PET reconstructions. In this work we propose a new dedicated regularization functional for multi-modality imaging that exploits common structures of the MR and PET images. The two modalities are treated as single multi-channel images and an extension of the second order Total Generalized Variation functional for vector valued data is used as a dedicated multi-modality sparsifying transform.

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