UNLABELLED: PET/MRI is an emerging dual-modality imaging technology that requires new approaches to PET attenuation correction (AC). We assessed 2 algorithms for whole-body MRI-based AC (MRAC): a basic MR image segmentation algorithm and a method based on atlas registration and pattern recognition (AT&PR). METHODS: Eleven patients each underwent a whole-body PET/CT study and a separate multibed whole-body MRI study. The MR image segmentation algorithm uses a combination of image thresholds, Dixon fat-water segmentation, and component analysis to detect the lungs. MR images are segmented into 5 tissue classes (not including bone), and each class is assigned a default linear attenuation value. The AT&PR algorithm uses a database of previously aligned pairs of MRI/CT image volumes. For each patient, these pairs are registered to the patient MRI volume, and machine-learning techniques are used to predict attenuation values on a continuous scale. MRAC methods are compared via the quantitative analysis of AC PET images using volumes of interest in normal organs and on lesions. We assume the PET/CT values after CT-based AC to be the reference standard. RESULTS: In regions of normal physiologic uptake, the average error of the mean standardized uptake value was 14.1% ± 10.2% and 7.7% ± 8.4% for the segmentation and the AT&PR methods, respectively. Lesion-based errors were 7.5% ± 7.9% for the segmentation method and 5.7% ± 4.7% for the AT&PR method. CONCLUSION: The MRAC method using AT&PR provided better overall PET quantification accuracy than the basic MR image segmentation approach. This better quantification was due to the significantly reduced volume of errors made regarding volumes of interest within or near bones and the slightly reduced volume of errors made regarding areas outside the lungs.
UNLABELLED: PET/MRI is an emerging dual-modality imaging technology that requires new approaches to PET attenuation correction (AC). We assessed 2 algorithms for whole-body MRI-based AC (MRAC): a basic MR image segmentation algorithm and a method based on atlas registration and pattern recognition (AT&PR). METHODS: Eleven patients each underwent a whole-body PET/CT study and a separate multibed whole-body MRI study. The MR image segmentation algorithm uses a combination of image thresholds, Dixon fat-water segmentation, and component analysis to detect the lungs. MR images are segmented into 5 tissue classes (not including bone), and each class is assigned a default linear attenuation value. The AT&PR algorithm uses a database of previously aligned pairs of MRI/CT image volumes. For each patient, these pairs are registered to the patient MRI volume, and machine-learning techniques are used to predict attenuation values on a continuous scale. MRAC methods are compared via the quantitative analysis of AC PET images using volumes of interest in normal organs and on lesions. We assume the PET/CT values after CT-based AC to be the reference standard. RESULTS: In regions of normal physiologic uptake, the average error of the mean standardized uptake value was 14.1% ± 10.2% and 7.7% ± 8.4% for the segmentation and the AT&PR methods, respectively. Lesion-based errors were 7.5% ± 7.9% for the segmentation method and 5.7% ± 4.7% for the AT&PR method. CONCLUSION: The MRAC method using AT&PR provided better overall PET quantification accuracy than the basic MR image segmentation approach. This better quantification was due to the significantly reduced volume of errors made regarding volumes of interest within or near bones and the slightly reduced volume of errors made regarding areas outside the lungs.
Authors: Michael Souvatzoglou; Matthias Eiber; Axel Martinez-Moeller; Sebastian Fürst; Konstantin Holzapfel; Tobias Maurer; Sibylle Ziegler; Stephan Nekolla; Markus Schwaiger; Ambros J Beer Journal: Eur J Nucl Med Mol Imaging Date: 2013-05-24 Impact factor: 9.236
Authors: David Izquierdo-Garcia; Stephen J Sawiak; Karin Knesaurek; Jagat Narula; Valentin Fuster; Joseph Machac; Zahi A Fayad Journal: Eur J Nucl Med Mol Imaging Date: 2014-03-21 Impact factor: 9.236
Authors: Andrew P Leynes; Jaewon Yang; Florian Wiesinger; Sandeep S Kaushik; Dattesh D Shanbhag; Youngho Seo; Thomas A Hope; Peder E Z Larson Journal: J Nucl Med Date: 2017-10-30 Impact factor: 10.057
Authors: Pieter Mollet; Vincent Keereman; Jason Bini; David Izquierdo-Garcia; Zahi A Fayad; Stefaan Vandenberghe Journal: J Nucl Med Date: 2014-01-16 Impact factor: 10.057
Authors: Sergios Gatidis; Holger Schmidt; Christian la Fougère; Konstantin Nikolaou; Nina F Schwenzer; Jürgen F Schäfer Journal: Eur J Nucl Med Mol Imaging Date: 2016-08-26 Impact factor: 9.236