期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 36, 期 1, 页码 1-16出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2016.2564989
关键词
Iterative image reconstruction; MR-PET; multi-modality imaging; total generalized variation; variational regularization methods
类别
资金
- NIH [R01 EB000447]
- [SFB F32]
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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