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

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

doi:10.1109/TMI.2016.2564989

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

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

Biomedical Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

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 language	English (US)
Article number	7466848
Pages (from-to)	1-16
Number of pages	16
Journal	IEEE Transactions on Medical Imaging
Volume	36
Issue number	1
DOIs	https://doi.org/10.1109/TMI.2016.2564989
State	Published - Jan 2017

Keywords

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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10.1109/TMI.2016.2564989

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abstract = "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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AU - Knoll, Florian

AU - Holler, Martin

AU - Koesters, Thomas

AU - Otazo, Ricardo

AU - Bredies, Kristian

AU - Sodickson, Daniel K.

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AB - 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.

KW - Iterative image reconstruction

KW - MR-PET

KW - multi-modality imaging

KW - total generalized variation

KW - variational regularization methods

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

Abstract

Keywords

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