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Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [(11)C]PHNO brain PET-MR studies

INTRODUCTION: Q.Clear is a Bayesian penalised likelihood (BPL) reconstruction algorithm available on General Electric (GE) Positron Emission Tomography (PET)-Computed Tomography (CT) and PET-Magnetic Resonance (MR) scanners. This algorithm is regulated by a β value which acts as a noise penalisation...

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Autores principales: Ribeiro, Daniela, Hallett, William, Howes, Oliver, McCutcheon, Robert, Nour, Matthew M., Tavares, Adriana A. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8859021/
https://www.ncbi.nlm.nih.gov/pubmed/35184229
http://dx.doi.org/10.1186/s13550-022-00883-1
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author Ribeiro, Daniela
Hallett, William
Howes, Oliver
McCutcheon, Robert
Nour, Matthew M.
Tavares, Adriana A. S.
author_facet Ribeiro, Daniela
Hallett, William
Howes, Oliver
McCutcheon, Robert
Nour, Matthew M.
Tavares, Adriana A. S.
author_sort Ribeiro, Daniela
collection PubMed
description INTRODUCTION: Q.Clear is a Bayesian penalised likelihood (BPL) reconstruction algorithm available on General Electric (GE) Positron Emission Tomography (PET)-Computed Tomography (CT) and PET-Magnetic Resonance (MR) scanners. This algorithm is regulated by a β value which acts as a noise penalisation factor and yields improvements in signal to noise ratio (SNR) in clinical scans, and in contrast recovery and spatial resolution in phantom studies. However, its performance in human brain imaging studies remains to be evaluated in depth. This pilot study aims to investigate the impact of Q.Clear reconstruction methods using different β value versus ordered subset expectation maximization (OSEM) on brain kinetic modelling analysis of low count brain images acquired in the PET-MR. METHODS: Six [(11)C]PHNO PET-MR brain datasets were reconstructed with Q.Clear with β100–1000 (in increments of 100) and OSEM. The binding potential relative to non-displaceable volume (BP(ND)) were obtained for the Substantia Nigra (SN), Striatum (St), Globus Pallidus (GP), Thalamus (Th), Caudate (Cd) and Putamen (Pt), using the MIAKAT™ software. Intraclass correlation coefficients (ICC), repeatability coefficients (RC), coefficients of variation (CV) and bias from Bland–Altman plots were reported. Statistical analysis was conducted using a 2-way ANOVA model with correction for multiple comparisons. RESULTS: When comparing a standard OSEM reconstruction of 6 iterations/16 subsets and 5 mm filter with Q.Clear with different β values under low counts, the bias and RC were lower for Q.Clear with β100 for the SN (RC = 2.17), Th (RC = 0.08) and GP (RC = 0.22) and with β200 for the St (RC = 0.14), Cd (RC = 0.18)and Pt (RC = 0.10). The p-values in the 2-way ANOVA model corroborate these findings. ICC values obtained for Th, St, GP, Pt and Cd demonstrate good reliability (0.87, 0.99, 0.96, 0.99 and 0.96, respectively). For the SN, ICC values demonstrate poor reliability (0.43). CONCLUSION: BP(ND) results obtained from quantitative low count brain PET studies using [(11)C]PHNO and reconstructed with Q.Clear with β < 400, which is the value used for clinical [(18)F]FDG whole-body studies, demonstrate the lowest bias versus the typical iterative reconstruction method OSEM. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13550-022-00883-1.
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spelling pubmed-88590212022-02-23 Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [(11)C]PHNO brain PET-MR studies Ribeiro, Daniela Hallett, William Howes, Oliver McCutcheon, Robert Nour, Matthew M. Tavares, Adriana A. S. EJNMMI Res Original Research INTRODUCTION: Q.Clear is a Bayesian penalised likelihood (BPL) reconstruction algorithm available on General Electric (GE) Positron Emission Tomography (PET)-Computed Tomography (CT) and PET-Magnetic Resonance (MR) scanners. This algorithm is regulated by a β value which acts as a noise penalisation factor and yields improvements in signal to noise ratio (SNR) in clinical scans, and in contrast recovery and spatial resolution in phantom studies. However, its performance in human brain imaging studies remains to be evaluated in depth. This pilot study aims to investigate the impact of Q.Clear reconstruction methods using different β value versus ordered subset expectation maximization (OSEM) on brain kinetic modelling analysis of low count brain images acquired in the PET-MR. METHODS: Six [(11)C]PHNO PET-MR brain datasets were reconstructed with Q.Clear with β100–1000 (in increments of 100) and OSEM. The binding potential relative to non-displaceable volume (BP(ND)) were obtained for the Substantia Nigra (SN), Striatum (St), Globus Pallidus (GP), Thalamus (Th), Caudate (Cd) and Putamen (Pt), using the MIAKAT™ software. Intraclass correlation coefficients (ICC), repeatability coefficients (RC), coefficients of variation (CV) and bias from Bland–Altman plots were reported. Statistical analysis was conducted using a 2-way ANOVA model with correction for multiple comparisons. RESULTS: When comparing a standard OSEM reconstruction of 6 iterations/16 subsets and 5 mm filter with Q.Clear with different β values under low counts, the bias and RC were lower for Q.Clear with β100 for the SN (RC = 2.17), Th (RC = 0.08) and GP (RC = 0.22) and with β200 for the St (RC = 0.14), Cd (RC = 0.18)and Pt (RC = 0.10). The p-values in the 2-way ANOVA model corroborate these findings. ICC values obtained for Th, St, GP, Pt and Cd demonstrate good reliability (0.87, 0.99, 0.96, 0.99 and 0.96, respectively). For the SN, ICC values demonstrate poor reliability (0.43). CONCLUSION: BP(ND) results obtained from quantitative low count brain PET studies using [(11)C]PHNO and reconstructed with Q.Clear with β < 400, which is the value used for clinical [(18)F]FDG whole-body studies, demonstrate the lowest bias versus the typical iterative reconstruction method OSEM. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13550-022-00883-1. Springer Berlin Heidelberg 2022-02-20 /pmc/articles/PMC8859021/ /pubmed/35184229 http://dx.doi.org/10.1186/s13550-022-00883-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Research
Ribeiro, Daniela
Hallett, William
Howes, Oliver
McCutcheon, Robert
Nour, Matthew M.
Tavares, Adriana A. S.
Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [(11)C]PHNO brain PET-MR studies
title Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [(11)C]PHNO brain PET-MR studies
title_full Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [(11)C]PHNO brain PET-MR studies
title_fullStr Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [(11)C]PHNO brain PET-MR studies
title_full_unstemmed Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [(11)C]PHNO brain PET-MR studies
title_short Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [(11)C]PHNO brain PET-MR studies
title_sort assessing the impact of different penalty factors of the bayesian reconstruction algorithm q.clear on in vivo low count kinetic analysis of [(11)c]phno brain pet-mr studies
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8859021/
https://www.ncbi.nlm.nih.gov/pubmed/35184229
http://dx.doi.org/10.1186/s13550-022-00883-1
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