Florian Knoll1, Martin Holler2, Thomas Koesters1, Martijn Cloos1, Ricardo Otazo1, Kristian Bredies2, and Daniel K Sodickson1
1Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, United States, 2Mathematics and Scientific Computing, University of Graz, Graz, Austria
A
typical clinical imaging protocol covers a large number of different
image contrasts and, in the era of multi-modality systems, even
different imaging modalities. While the resulting datasets share a
substantial amount of structural information, they consist of
fundamentally different contrasts and signal values and show unique
features and image content. We propose a reconstruction framework
based on nuclear-norm second-order Total Generalized Variation that
exploits structural similarity both between different contrasts and
modalities while still being flexible with respect to signal
intensity and unique features. Numerical simulations and in vivo
MR-Fingerprinting experiments demonstrate improved PET resolution and
improved depiction of quantitative values. The proposed approach
allows a 6 minute whole brain coverage exam that provides both
quantitative PET and MR-relaxation parameters.
