Pauline Huet1, Samuel Burg2, Dominique Le Guludec2, Fabien Hyafil2, Irène Buvat3. 1. U1023 Inserm/CEA/Paris Sud University-ERL 9218 CNRS, CEA-SHFJ, Orsay, France IMNC UMR 8165 CNRS, Paris Sud University, Orsay, France; and. 2. Department of Nuclear Medicine, Bichat University Hospital, Assistance Publique-Hôpitaux de Paris, UMR 1148, Inserm and Paris Diderot-Paris 7 University, Département Hospitalo-Universitaire Fire, Paris, France. 3. U1023 Inserm/CEA/Paris Sud University-ERL 9218 CNRS, CEA-SHFJ, Orsay, France irene.buvat@u-psud.fr.
Abstract
UNLABELLED: PET with (18)F-FDG shows promise for the evaluation of metabolic activities in atherosclerotic plaques. Although recommendations regarding the acquisition and measurement protocols to be used for (18)F-FDG PET imaging of atherosclerosis inflammation have been published, there is no consensus regarding the most appropriate protocols, and the image reconstruction approach has been especially overlooked. Given the small size of the targeted lesions, the reconstruction and measurement methods might strongly affect the results. We determined the differences in results due to the protocol variability and identified means of increasing the measurement reliability. METHODS: An extensive literature search was performed to characterize the variability in atherosclerosis imaging and quantification protocols. Highly realistic simulations of atherosclerotic carotid lesions based on real patient data were designed to determine how the acquisition and processing protocol parameters affected the measured values. RESULTS: In 49 articles, we identified 53 different acquisition protocols, 51 reconstruction protocols, and 46 quantification methods to characterize atherosclerotic lesions from (18)F-FDG PET images. The most important parameters affecting the measurement accuracy were the number of iterations used for reconstruction and the postfiltering applied to the reconstructed images, which could together make the measured standardized uptake values (SUVs) vary by a factor greater than 3. Image sampling, acquisition duration, and metrics used for the measurements also affected the results to a lesser extent (SUV varying by a factor of 1.3 at most). For an acceptable SUV variability, the lowest bias in SUV was observed using an 8-min acquisition per bed position; ordered-subset expectation maximization reconstruction with at least 120 maximum likelihood expectation maximization equivalent iterations, including a point spread function model using a 1 mm(3) voxel size; and no postfiltering. Because of the partial-volume effect, measurement bias remained greater than 60%. The use and limitations of the target-to-blood activity ratio metrics are also presented and discussed. CONCLUSION: (18)F-FDG PET protocol harmonization is needed in atherosclerosis imaging. Optimized protocols can significantly reduce the measurement errors in wall activity estimates, but PET systems with higher spatial resolution and advanced partial-volume corrections will be required to accurately assess plaque inflammation from (18)F-FDG PET.
UNLABELLED: PET with (18)F-FDG shows promise for the evaluation of metabolic activities in atherosclerotic plaques. Although recommendations regarding the acquisition and measurement protocols to be used for (18)F-FDG PET imaging of atherosclerosis inflammation have been published, there is no consensus regarding the most appropriate protocols, and the image reconstruction approach has been especially overlooked. Given the small size of the targeted lesions, the reconstruction and measurement methods might strongly affect the results. We determined the differences in results due to the protocol variability and identified means of increasing the measurement reliability. METHODS: An extensive literature search was performed to characterize the variability in atherosclerosis imaging and quantification protocols. Highly realistic simulations of atherosclerotic carotid lesions based on real patient data were designed to determine how the acquisition and processing protocol parameters affected the measured values. RESULTS: In 49 articles, we identified 53 different acquisition protocols, 51 reconstruction protocols, and 46 quantification methods to characterize atherosclerotic lesions from (18)F-FDG PET images. The most important parameters affecting the measurement accuracy were the number of iterations used for reconstruction and the postfiltering applied to the reconstructed images, which could together make the measured standardized uptake values (SUVs) vary by a factor greater than 3. Image sampling, acquisition duration, and metrics used for the measurements also affected the results to a lesser extent (SUV varying by a factor of 1.3 at most). For an acceptable SUV variability, the lowest bias in SUV was observed using an 8-min acquisition per bed position; ordered-subset expectation maximization reconstruction with at least 120 maximum likelihood expectation maximization equivalent iterations, including a point spread function model using a 1 mm(3) voxel size; and no postfiltering. Because of the partial-volume effect, measurement bias remained greater than 60%. The use and limitations of the target-to-blood activity ratio metrics are also presented and discussed. CONCLUSION: (18)F-FDG PET protocol harmonization is needed in atherosclerosis imaging. Optimized protocols can significantly reduce the measurement errors in wall activity estimates, but PET systems with higher spatial resolution and advanced partial-volume corrections will be required to accurately assess plaque inflammation from (18)F-FDG PET.
Authors: Fabien Hyafil; Andreas Schindler; Dominik Sepp; Tilman Obenhuber; Anna Bayer-Karpinska; Tobias Boeckh-Behrens; Sabine Höhn; Marcus Hacker; Stephan G Nekolla; Axel Rominger; Martin Dichgans; Markus Schwaiger; Tobias Saam; Holger Poppert Journal: Eur J Nucl Med Mol Imaging Date: 2015-10-03 Impact factor: 9.236
Authors: Mootaz Eldib; Jason Bini; Olivier Lairez; David D Faul; Niels Oesingmann; Zahi A Fayad; Venkatesh Mani Journal: Am J Nucl Med Mol Imaging Date: 2015-06-15
Authors: Pavlos P Kafouris; Iosif P Koutagiar; Alexandros T Georgakopoulos; Nikoletta K Pianou; Marinos G Metaxas; George M Spyrou; Constantinos D Anagnostopoulos Journal: Int J Cardiovasc Imaging Date: 2019-01-31 Impact factor: 2.357