MR-based attenuation correction for torso-PET/MR imaging: Pitfalls in mapping MR to CT data

Thomas Beyer; Markus Weigert; Harald H. Quick; Uwe Pietrzyk; Florian Vogt; Christoph Palm; Gerald Antoch; Stefan P. Müller; Andreas Bockisch

doi:10.1007/s00259-008-0734-0

MR-based attenuation correction for torso-PET/MR imaging: Pitfalls in mapping MR to CT data

Thomas Beyer^*, Markus Weigert, Harald H. Quick, Uwe Pietrzyk, Florian Vogt, Christoph Palm, Gerald Antoch, Stefan P. Müller, Andreas Bockisch

^*Corresponding author for this work

Clinic of Radiology and Nuclear Medicine

102 Citations (Scopus)

Abstract

Purpose: MR-based attenuation correction (AC) will become an integral part of combined PET/MR systems. Here, we propose a toolbox to validate MR-AC of clinical PET/MRI data sets. Methods: Torso scans of ten patients were acquired on a combined PET/CT and on a 1.5-T MRI system. MR-based attenuation data were derived from the CT following MR-CT image co-registration and subsequent histogram matching. PET images were reconstructed after CT- (PET_CT) and MR-based AC (PET_MRI). Lesion-to-background (L/B) ratios were estimated on PET_CT and PET_MRI. Results: MR-CT histogram matching leads to a mean voxel intensity difference in the CT- and MR-based attenuation images of 12% (max). Mean differences between PET_MRI and PET_CT were 19% (max). L/B ratios were similar except for the lung where local misregistration and intensity transformation leads to a biased PET_MRI. Conclusion: Our toolbox can be used to study pitfalls in MR-AC. We found that co-registration accuracy and pixel value transformation determine the accuracy of PET_MRI.

Original language	English
Journal	European Journal of Nuclear Medicine and Molecular Imaging
Volume	35
Issue number	6
Pages (from-to)	1142-1146
Number of pages	5
ISSN	1619-7070
DOIs	https://doi.org/10.1007/s00259-008-0734-0
Publication status	Published - 06.2008

Funding

The selection, validation and implementation of the co-registration algorithms were supported by the German Research Foundation (DFG): BE 3169/2-2.

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This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 9 Industry, Innovation, and Infrastructure

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@article{338fc37a34e14d8cbe1ebcc34b09095e,

title = "MR-based attenuation correction for torso-PET/MR imaging: Pitfalls in mapping MR to CT data",

abstract = "Purpose: MR-based attenuation correction (AC) will become an integral part of combined PET/MR systems. Here, we propose a toolbox to validate MR-AC of clinical PET/MRI data sets. Methods: Torso scans of ten patients were acquired on a combined PET/CT and on a 1.5-T MRI system. MR-based attenuation data were derived from the CT following MR-CT image co-registration and subsequent histogram matching. PET images were reconstructed after CT- (PETCT) and MR-based AC (PETMRI). Lesion-to-background (L/B) ratios were estimated on PETCT and PETMRI. Results: MR-CT histogram matching leads to a mean voxel intensity difference in the CT- and MR-based attenuation images of 12\% (max). Mean differences between PETMRI and PETCT were 19\% (max). L/B ratios were similar except for the lung where local misregistration and intensity transformation leads to a biased PETMRI. Conclusion: Our toolbox can be used to study pitfalls in MR-AC. We found that co-registration accuracy and pixel value transformation determine the accuracy of PETMRI.",

author = "Thomas Beyer and Markus Weigert and Quick, \{Harald H.\} and Uwe Pietrzyk and Florian Vogt and Christoph Palm and Gerald Antoch and M{\"u}ller, \{Stefan P.\} and Andreas Bockisch",

note = "Funding Information: The selection, validation and implementation of the co-registration algorithms were supported by the German Research Foundation (DFG): BE 3169/2-2. Copyright: Copyright 2008 Elsevier B.V., All rights reserved.",

year = "2008",

month = jun,

doi = "10.1007/s00259-008-0734-0",

language = "English",

volume = "35",

pages = "1142--1146",

journal = "European Journal of Nuclear Medicine and Molecular Imaging",

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AU - Beyer, Thomas

AU - Weigert, Markus

AU - Quick, Harald H.

AU - Pietrzyk, Uwe

AU - Vogt, Florian

AU - Palm, Christoph

AU - Antoch, Gerald

AU - Müller, Stefan P.

AU - Bockisch, Andreas

N1 - Funding Information: The selection, validation and implementation of the co-registration algorithms were supported by the German Research Foundation (DFG): BE 3169/2-2. Copyright: Copyright 2008 Elsevier B.V., All rights reserved.

PY - 2008/6

Y1 - 2008/6

N2 - Purpose: MR-based attenuation correction (AC) will become an integral part of combined PET/MR systems. Here, we propose a toolbox to validate MR-AC of clinical PET/MRI data sets. Methods: Torso scans of ten patients were acquired on a combined PET/CT and on a 1.5-T MRI system. MR-based attenuation data were derived from the CT following MR-CT image co-registration and subsequent histogram matching. PET images were reconstructed after CT- (PETCT) and MR-based AC (PETMRI). Lesion-to-background (L/B) ratios were estimated on PETCT and PETMRI. Results: MR-CT histogram matching leads to a mean voxel intensity difference in the CT- and MR-based attenuation images of 12% (max). Mean differences between PETMRI and PETCT were 19% (max). L/B ratios were similar except for the lung where local misregistration and intensity transformation leads to a biased PETMRI. Conclusion: Our toolbox can be used to study pitfalls in MR-AC. We found that co-registration accuracy and pixel value transformation determine the accuracy of PETMRI.

AB - Purpose: MR-based attenuation correction (AC) will become an integral part of combined PET/MR systems. Here, we propose a toolbox to validate MR-AC of clinical PET/MRI data sets. Methods: Torso scans of ten patients were acquired on a combined PET/CT and on a 1.5-T MRI system. MR-based attenuation data were derived from the CT following MR-CT image co-registration and subsequent histogram matching. PET images were reconstructed after CT- (PETCT) and MR-based AC (PETMRI). Lesion-to-background (L/B) ratios were estimated on PETCT and PETMRI. Results: MR-CT histogram matching leads to a mean voxel intensity difference in the CT- and MR-based attenuation images of 12% (max). Mean differences between PETMRI and PETCT were 19% (max). L/B ratios were similar except for the lung where local misregistration and intensity transformation leads to a biased PETMRI. Conclusion: Our toolbox can be used to study pitfalls in MR-AC. We found that co-registration accuracy and pixel value transformation determine the accuracy of PETMRI.

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MR-based attenuation correction for torso-PET/MR imaging: Pitfalls in mapping MR to CT data

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