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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