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

Florian Knoll, Martin Holler, Thomas Koesters, Ricardo Otazo, Kristian Bredies, Daniel K. Sodickson

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number7466848
Pages (from-to)1-16
Number of pages16
JournalIEEE Transactions on Medical Imaging
Issue number1
StatePublished - Jan 2017


  • Iterative image reconstruction
  • MR-PET
  • multi-modality imaging
  • total generalized variation
  • variational regularization methods

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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