MR-based Attenuation Correction for Torso-PET/MR Imaging: Pitfalls in Mapping MR to CT Data

Overview

Journal Eur J Nucl Med Mol Imaging

Specialties Biophysics
Nuclear Medicine
Radiology

Date 2008 Feb 20

PMID 18283452

Citations 45

Authors

Thomas Beyer

Markus Weigert

Harald H Quick

Uwe Pietrzyk

Florian Vogt

Christoph Palm

Gerald Antoch

Stefan P Muller

Andreas Bockisch

Affiliations

Soon will be listed here.

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).

Citing Articles

A review of PET attenuation correction methods for PET-MR.

Krokos G, MacKewn J, Dunn J, Marsden P EJNMMI Phys. 2023; 10(1):52.

PMID: 37695384 PMC: 10495310. DOI: 10.1186/s40658-023-00569-0.

Intrascanner Reproducibility of an SPM-based Head MR-based Attenuation Correction Method.

Izquierdo-Garcia D, Eldaief M, Vangel M, Catana C IEEE Trans Radiat Plasma Med Sci. 2020; 3(3):327-333.

PMID: 32537528 PMC: 7291765. DOI: 10.1109/trpms.2018.2868946.

PET/MRI attenuation estimation in the lung: A review of past, present, and potential techniques.

Lillington J, Brusaferri L, Klaser K, Shmueli K, Neji R, Hutton B Med Phys. 2019; 47(2):790-811.

PMID: 31794071 PMC: 7027532. DOI: 10.1002/mp.13943.

Pseudo CT Estimation from MRI Using Patch-based Random Forest.

Yang X, Lei Y, Shu H, Rossi P, Mao H, Shim H Proc SPIE Int Soc Opt Eng. 2019; 10133.

PMID: 31607771 PMC: 6788808. DOI: 10.1117/12.2253936.

mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification.

Qian P, Chen Y, Kuo J, Zhang Y, Jiang Y, Zhao K IEEE Trans Med Imaging. 2019; 39(4):819-832.

PMID: 31425065 PMC: 7284852. DOI: 10.1109/TMI.2019.2935916.

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