Artificial intelligence-based 68Ga-DOTATOC PET denoising for optimizing 68Ge/68Ga generator use throughout its lifetime
IntroductionThe yield per elution of a 68Ge/68Ga 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 ai...
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2023.1137514/full |
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| author | Elske Quak Kathleen Weyts Cyril Jaudet Cyril Jaudet Anaïs Prigent Anaïs Prigent Gauthier Foucras Gauthier Foucras Charline Lasnon Charline Lasnon |
| author_facet | Elske Quak Kathleen Weyts Cyril Jaudet Cyril Jaudet Anaïs Prigent Anaïs Prigent Gauthier Foucras Gauthier Foucras Charline Lasnon Charline Lasnon |
| author_sort | Elske Quak |
| collection | DOAJ |
| description | IntroductionThe yield per elution of a 68Ge/68Ga 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.MethodsAll patients addressed to our PET unit for a 68Ga-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_WeightDoseAI). 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.ResultsThe 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_WeightDoseAI 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_WeightDoseAI: 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).ConclusionThe degradation of PET image quality due to a reduction in injected dose at the end of the 68Ge/68Ga generator lifespan can be effectively counterbalanced by using AI-based PET denoising. |
| first_indexed | 2024-04-10T04:06:16Z |
| format | Article |
| id | doaj.art-ccd30444c56949429ca8849e79684ca5 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-04-10T04:06:16Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Medicine |
| spelling | doaj.art-ccd30444c56949429ca8849e79684ca52023-03-13T05:55:06ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2023-03-011010.3389/fmed.2023.11375141137514Artificial intelligence-based 68Ga-DOTATOC PET denoising for optimizing 68Ge/68Ga generator use throughout its lifetimeElske Quak0Kathleen Weyts1Cyril Jaudet2Cyril Jaudet3Anaïs Prigent4Anaïs Prigent5Gauthier Foucras6Gauthier Foucras7Charline Lasnon8Charline Lasnon9Nuclear Medicine Department, Comprehensive Cancer Centre François Baclesse, UNICANCER, Caen, FranceNuclear Medicine Department, Comprehensive Cancer Centre François Baclesse, UNICANCER, Caen, FranceNuclear Medicine Department, Comprehensive Cancer Centre François Baclesse, UNICANCER, Caen, FranceRadiophysics Department, Comprehensive Cancer Centre François Baclesse, UNICANCER, Caen, FranceNuclear Medicine Department, Comprehensive Cancer Centre François Baclesse, UNICANCER, Caen, FranceRadiopharmacy Department, Comprehensive Cancer Centre François Baclesse, UNICANCER, Caen, FranceNuclear Medicine Department, Comprehensive Cancer Centre François Baclesse, UNICANCER, Caen, FranceRadiopharmacy Department, Comprehensive Cancer Centre François Baclesse, UNICANCER, Caen, FranceNuclear Medicine Department, Comprehensive Cancer Centre François Baclesse, UNICANCER, Caen, FranceUNICAEN, INSERM 1086 ANTICIPE, Normandy University, Caen, FranceIntroductionThe yield per elution of a 68Ge/68Ga 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.MethodsAll patients addressed to our PET unit for a 68Ga-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_WeightDoseAI). 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.ResultsThe 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_WeightDoseAI 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_WeightDoseAI: 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).ConclusionThe degradation of PET image quality due to a reduction in injected dose at the end of the 68Ge/68Ga generator lifespan can be effectively counterbalanced by using AI-based PET denoising.https://www.frontiersin.org/articles/10.3389/fmed.2023.1137514/fullPETgallium-68artificial intelligencedenoisingdeep learning |
| spellingShingle | Elske Quak Kathleen Weyts Cyril Jaudet Cyril Jaudet Anaïs Prigent Anaïs Prigent Gauthier Foucras Gauthier Foucras Charline Lasnon Charline Lasnon Artificial intelligence-based 68Ga-DOTATOC PET denoising for optimizing 68Ge/68Ga generator use throughout its lifetime Frontiers in Medicine PET gallium-68 artificial intelligence denoising deep learning |
| title | Artificial intelligence-based 68Ga-DOTATOC PET denoising for optimizing 68Ge/68Ga generator use throughout its lifetime |
| title_full | Artificial intelligence-based 68Ga-DOTATOC PET denoising for optimizing 68Ge/68Ga generator use throughout its lifetime |
| title_fullStr | Artificial intelligence-based 68Ga-DOTATOC PET denoising for optimizing 68Ge/68Ga generator use throughout its lifetime |
| title_full_unstemmed | Artificial intelligence-based 68Ga-DOTATOC PET denoising for optimizing 68Ge/68Ga generator use throughout its lifetime |
| title_short | Artificial intelligence-based 68Ga-DOTATOC PET denoising for optimizing 68Ge/68Ga generator use throughout its lifetime |
| title_sort | artificial intelligence based 68ga dotatoc pet denoising for optimizing 68ge 68ga generator use throughout its lifetime |
| topic | PET gallium-68 artificial intelligence denoising deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2023.1137514/full |
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