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Artificial intelligence-based (68)Ga-DOTATOC PET denoising for optimizing (68)Ge/(68)Ga generator use throughout its lifetime

INTRODUCTION: The yield per elution of a (68)Ge/(68)Ga generator decreases during its lifespan. This affects the number of patients injected per elution or the injected dose per patient, thereby negatively affecting the cost of examinations and the quality of PET images due to increased image noise....

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Autores principales: Quak, Elske, Weyts, Kathleen, Jaudet, Cyril, Prigent, Anaïs, Foucras, Gauthier, Lasnon, Charline
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040856/
https://www.ncbi.nlm.nih.gov/pubmed/36993807
http://dx.doi.org/10.3389/fmed.2023.1137514
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author Quak, Elske
Weyts, Kathleen
Jaudet, Cyril
Prigent, Anaïs
Foucras, Gauthier
Lasnon, Charline
author_facet Quak, Elske
Weyts, Kathleen
Jaudet, Cyril
Prigent, Anaïs
Foucras, Gauthier
Lasnon, Charline
author_sort Quak, Elske
collection PubMed
description INTRODUCTION: The yield per elution of a (68)Ge/(68)Ga generator decreases during its lifespan. This affects the number of patients injected per elution or the injected dose per patient, thereby negatively affecting the cost of examinations and the quality of PET images due to increased image noise. We aimed to investigate whether AI-based PET denoising can offset this decrease in image quality parameters. METHODS: All patients addressed to our PET unit for a (68)Ga-DOTATOC PET/CT from April 2020 to February 2021 were enrolled. Forty-four patients underwent their PET scans according to Protocol_FixedDose (150 MBq) and 32 according to Protocol_WeightDose (1.5 MBq/kg). Protocol_WeightDose examinations were processed using the Subtle PET software (Protocol_WeightDose(AI)). Liver and vascular SUV mean were recorded as well as SUVmax, SUVmean and metabolic tumour volume (MTV) of the most intense tumoural lesion and its background SUVmean. Liver and vascular coefficients of variation (CV), tumour-to-background and tumour-to-liver ratios were calculated. RESULTS: The mean injected dose of 2.1 (0.4) MBq/kg per patient was significantly higher in the Protocol_FixedDose group as compared to 1.5 (0.1) MBq/kg for the Protocol_WeightDose group. Protocol_WeightDose led to noisier images than Protocol_FixedDose with higher CVs for liver (15.57% ± 4.32 vs. 13.04% ± 3.51, p = 0.018) and blood-pool (28.67% ± 8.65 vs. 22.25% ± 10.37, p = 0.0003). Protocol_WeightDose(AI) led to less noisy images than Protocol_WeightDose with lower liver CVs (11.42% ± 3.05 vs. 15.57% ± 4.32, p < 0.0001) and vascular CVs (16.62% ± 6.40 vs. 28.67% ± 8.65, p < 0.0001). Tumour-to-background and tumour-to-liver ratios were lower for protocol_WeightDose(AI): 6.78 ± 3.49 vs. 7.57 ± 4.73 (p = 0.01) and 5.96 ± 5.43 vs. 6.77 ± 6.19 (p < 0.0001), respectively. MTVs were higher after denoising whereas tumour SUVmax were lower: the mean% differences in MTV and SUVmax were + 11.14% (95% CI = 4.84–17.43) and −3.92% (95% CI = −6.25 to −1.59). CONCLUSION: The degradation of PET image quality due to a reduction in injected dose at the end of the (68)Ge/(68)Ga generator lifespan can be effectively counterbalanced by using AI-based PET denoising.
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spelling pubmed-100408562023-03-28 Artificial intelligence-based (68)Ga-DOTATOC PET denoising for optimizing (68)Ge/(68)Ga generator use throughout its lifetime Quak, Elske Weyts, Kathleen Jaudet, Cyril Prigent, Anaïs Foucras, Gauthier Lasnon, Charline Front Med (Lausanne) Medicine INTRODUCTION: The yield per elution of a (68)Ge/(68)Ga generator decreases during its lifespan. This affects the number of patients injected per elution or the injected dose per patient, thereby negatively affecting the cost of examinations and the quality of PET images due to increased image noise. We aimed to investigate whether AI-based PET denoising can offset this decrease in image quality parameters. METHODS: All patients addressed to our PET unit for a (68)Ga-DOTATOC PET/CT from April 2020 to February 2021 were enrolled. Forty-four patients underwent their PET scans according to Protocol_FixedDose (150 MBq) and 32 according to Protocol_WeightDose (1.5 MBq/kg). Protocol_WeightDose examinations were processed using the Subtle PET software (Protocol_WeightDose(AI)). Liver and vascular SUV mean were recorded as well as SUVmax, SUVmean and metabolic tumour volume (MTV) of the most intense tumoural lesion and its background SUVmean. Liver and vascular coefficients of variation (CV), tumour-to-background and tumour-to-liver ratios were calculated. RESULTS: The mean injected dose of 2.1 (0.4) MBq/kg per patient was significantly higher in the Protocol_FixedDose group as compared to 1.5 (0.1) MBq/kg for the Protocol_WeightDose group. Protocol_WeightDose led to noisier images than Protocol_FixedDose with higher CVs for liver (15.57% ± 4.32 vs. 13.04% ± 3.51, p = 0.018) and blood-pool (28.67% ± 8.65 vs. 22.25% ± 10.37, p = 0.0003). Protocol_WeightDose(AI) led to less noisy images than Protocol_WeightDose with lower liver CVs (11.42% ± 3.05 vs. 15.57% ± 4.32, p < 0.0001) and vascular CVs (16.62% ± 6.40 vs. 28.67% ± 8.65, p < 0.0001). Tumour-to-background and tumour-to-liver ratios were lower for protocol_WeightDose(AI): 6.78 ± 3.49 vs. 7.57 ± 4.73 (p = 0.01) and 5.96 ± 5.43 vs. 6.77 ± 6.19 (p < 0.0001), respectively. MTVs were higher after denoising whereas tumour SUVmax were lower: the mean% differences in MTV and SUVmax were + 11.14% (95% CI = 4.84–17.43) and −3.92% (95% CI = −6.25 to −1.59). CONCLUSION: The degradation of PET image quality due to a reduction in injected dose at the end of the (68)Ge/(68)Ga generator lifespan can be effectively counterbalanced by using AI-based PET denoising. Frontiers Media S.A. 2023-03-13 /pmc/articles/PMC10040856/ /pubmed/36993807 http://dx.doi.org/10.3389/fmed.2023.1137514 Text en Copyright © 2023 Quak, Weyts, Jaudet, Prigent, Foucras and Lasnon. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Quak, Elske
Weyts, Kathleen
Jaudet, Cyril
Prigent, Anaïs
Foucras, Gauthier
Lasnon, Charline
Artificial intelligence-based (68)Ga-DOTATOC PET denoising for optimizing (68)Ge/(68)Ga generator use throughout its lifetime
title Artificial intelligence-based (68)Ga-DOTATOC PET denoising for optimizing (68)Ge/(68)Ga generator use throughout its lifetime
title_full Artificial intelligence-based (68)Ga-DOTATOC PET denoising for optimizing (68)Ge/(68)Ga generator use throughout its lifetime
title_fullStr Artificial intelligence-based (68)Ga-DOTATOC PET denoising for optimizing (68)Ge/(68)Ga generator use throughout its lifetime
title_full_unstemmed Artificial intelligence-based (68)Ga-DOTATOC PET denoising for optimizing (68)Ge/(68)Ga generator use throughout its lifetime
title_short Artificial intelligence-based (68)Ga-DOTATOC PET denoising for optimizing (68)Ge/(68)Ga generator use throughout its lifetime
title_sort artificial intelligence-based (68)ga-dotatoc pet denoising for optimizing (68)ge/(68)ga generator use throughout its lifetime
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040856/
https://www.ncbi.nlm.nih.gov/pubmed/36993807
http://dx.doi.org/10.3389/fmed.2023.1137514
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