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IEEE Trans Med Imaging. 2017 Jan;36(1):1-16. doi: 10.1109/TMI.2016.2564989.

Joint MR-PET Reconstruction Using a Multi-Channel Image Regularizer.

Author information

Bernard and Irene Schwartz Center for Biomedical Imaging, and the Center for Advanced Imaging Innovation and Research (CAIR), in the Department of Radiology at NYU School of Medicine, New York, NY, United States.
Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria. The Institute of Mathematics and Scientific Computing is a member of NAWI Graz ( and BioTechMed Graz (
Contributed equally


While current state of the art MR-PET scanners enable simultaneous MR and PET measurements, the acquired data sets are still usually reconstructed separately. We propose a new multi-modality reconstruction framework using second order Total Generalized Variation (TGV) as a dedicated multi-channel regularization functional that jointly reconstructs images from both modalities. In this way, information about the underlying anatomy is shared during the image reconstruction process while unique differences are preserved. Results from numerical simulations and in-vivo experiments using a range of accelerated MR acquisitions and different MR image contrasts demonstrate improved PET image quality, resolution, and quantitative accuracy.

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