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Artificial intelligence-based PET denoising could allow a two-fold reduction in [(18)F]FDG PET acquisition time in digital PET/CT

PURPOSE: We investigated whether artificial intelligence (AI)-based denoising halves PET acquisition time in digital PET/CT. METHODS: One hundred ninety-five patients referred for [(18)F]FDG PET/CT were prospectively included. Body PET acquisitions were performed in list mode. Original “PET90” (90 s...

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Autores principales: Weyts, Kathleen, Lasnon, Charline, Ciappuccini, Renaud, Lequesne, Justine, Corroyer-Dulmont, Aurélien, Quak, Elske, Clarisse, Bénédicte, Roussel, Laurent, Bardet, Stéphane, Jaudet, Cyril
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399218/
https://www.ncbi.nlm.nih.gov/pubmed/35593925
http://dx.doi.org/10.1007/s00259-022-05800-1
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author Weyts, Kathleen
Lasnon, Charline
Ciappuccini, Renaud
Lequesne, Justine
Corroyer-Dulmont, Aurélien
Quak, Elske
Clarisse, Bénédicte
Roussel, Laurent
Bardet, Stéphane
Jaudet, Cyril
author_facet Weyts, Kathleen
Lasnon, Charline
Ciappuccini, Renaud
Lequesne, Justine
Corroyer-Dulmont, Aurélien
Quak, Elske
Clarisse, Bénédicte
Roussel, Laurent
Bardet, Stéphane
Jaudet, Cyril
author_sort Weyts, Kathleen
collection PubMed
description PURPOSE: We investigated whether artificial intelligence (AI)-based denoising halves PET acquisition time in digital PET/CT. METHODS: One hundred ninety-five patients referred for [(18)F]FDG PET/CT were prospectively included. Body PET acquisitions were performed in list mode. Original “PET90” (90 s/bed position) was compared to reconstructed ½-duration PET (45 s/bed position) with and without AI-denoising, “PET45AI and PET45”. Denoising was performed by SubtlePET™ using deep convolutional neural networks. Visual global image quality (IQ) 3-point scores and lesion detectability were evaluated. Lesion maximal and peak standardized uptake values using lean body mass (SUL(max) and SUL(peak)), metabolic volumes (MV), and liver SUL(mean) were measured, including both standard and EARL(1) (European Association of Nuclear Medicine Research Ltd) compliant SUL. Lesion-to-liver SUL ratios (LLR) and liver coefficients of variation (CV(liv)) were calculated. RESULTS: PET45 showed mediocre IQ (scored poor in 8% and moderate in 68%) and lesion concordance rate with PET90 (88.7%). In PET45AI, IQ scores were similar to PET90 (P = 0.80), good in 92% and moderate in 8% for both. The lesion concordance rate between PET90 and PET45AI was 836/856 (97.7%), with 7 lesions (0.8%) only detected in PET90 and 13 (1.5%) exclusively in PET45AI. Lesion EARL(1) SUL(peak) was not significantly different between both PET (P = 0.09). Lesion standard SUL(peak), standard and EARL1 SUL(max), LLR and CV(liv) were lower in PET45AI than in PET90 (P < 0.0001), while lesion MV and liver SUL(mean) were higher (P < 0.0001). Good to excellent intraclass correlation coefficients (ICC) between PET90 and PET45AI were observed for lesion SUL and MV (ICC ≥ 0.97) and for liver SUL(mean) (ICC ≥ 0.87). CONCLUSION: AI allows [(18)F]FDG PET duration in digital PET/CT to be halved, while restoring degraded ½-duration PET image quality. Future multicentric studies, including other PET radiopharmaceuticals, are warranted. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00259-022-05800-1.
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spelling pubmed-93992182022-08-25 Artificial intelligence-based PET denoising could allow a two-fold reduction in [(18)F]FDG PET acquisition time in digital PET/CT Weyts, Kathleen Lasnon, Charline Ciappuccini, Renaud Lequesne, Justine Corroyer-Dulmont, Aurélien Quak, Elske Clarisse, Bénédicte Roussel, Laurent Bardet, Stéphane Jaudet, Cyril Eur J Nucl Med Mol Imaging Original Article PURPOSE: We investigated whether artificial intelligence (AI)-based denoising halves PET acquisition time in digital PET/CT. METHODS: One hundred ninety-five patients referred for [(18)F]FDG PET/CT were prospectively included. Body PET acquisitions were performed in list mode. Original “PET90” (90 s/bed position) was compared to reconstructed ½-duration PET (45 s/bed position) with and without AI-denoising, “PET45AI and PET45”. Denoising was performed by SubtlePET™ using deep convolutional neural networks. Visual global image quality (IQ) 3-point scores and lesion detectability were evaluated. Lesion maximal and peak standardized uptake values using lean body mass (SUL(max) and SUL(peak)), metabolic volumes (MV), and liver SUL(mean) were measured, including both standard and EARL(1) (European Association of Nuclear Medicine Research Ltd) compliant SUL. Lesion-to-liver SUL ratios (LLR) and liver coefficients of variation (CV(liv)) were calculated. RESULTS: PET45 showed mediocre IQ (scored poor in 8% and moderate in 68%) and lesion concordance rate with PET90 (88.7%). In PET45AI, IQ scores were similar to PET90 (P = 0.80), good in 92% and moderate in 8% for both. The lesion concordance rate between PET90 and PET45AI was 836/856 (97.7%), with 7 lesions (0.8%) only detected in PET90 and 13 (1.5%) exclusively in PET45AI. Lesion EARL(1) SUL(peak) was not significantly different between both PET (P = 0.09). Lesion standard SUL(peak), standard and EARL1 SUL(max), LLR and CV(liv) were lower in PET45AI than in PET90 (P < 0.0001), while lesion MV and liver SUL(mean) were higher (P < 0.0001). Good to excellent intraclass correlation coefficients (ICC) between PET90 and PET45AI were observed for lesion SUL and MV (ICC ≥ 0.97) and for liver SUL(mean) (ICC ≥ 0.87). CONCLUSION: AI allows [(18)F]FDG PET duration in digital PET/CT to be halved, while restoring degraded ½-duration PET image quality. Future multicentric studies, including other PET radiopharmaceuticals, are warranted. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00259-022-05800-1. Springer Berlin Heidelberg 2022-05-20 2022 /pmc/articles/PMC9399218/ /pubmed/35593925 http://dx.doi.org/10.1007/s00259-022-05800-1 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 Article
Weyts, Kathleen
Lasnon, Charline
Ciappuccini, Renaud
Lequesne, Justine
Corroyer-Dulmont, Aurélien
Quak, Elske
Clarisse, Bénédicte
Roussel, Laurent
Bardet, Stéphane
Jaudet, Cyril
Artificial intelligence-based PET denoising could allow a two-fold reduction in [(18)F]FDG PET acquisition time in digital PET/CT
title Artificial intelligence-based PET denoising could allow a two-fold reduction in [(18)F]FDG PET acquisition time in digital PET/CT
title_full Artificial intelligence-based PET denoising could allow a two-fold reduction in [(18)F]FDG PET acquisition time in digital PET/CT
title_fullStr Artificial intelligence-based PET denoising could allow a two-fold reduction in [(18)F]FDG PET acquisition time in digital PET/CT
title_full_unstemmed Artificial intelligence-based PET denoising could allow a two-fold reduction in [(18)F]FDG PET acquisition time in digital PET/CT
title_short Artificial intelligence-based PET denoising could allow a two-fold reduction in [(18)F]FDG PET acquisition time in digital PET/CT
title_sort artificial intelligence-based pet denoising could allow a two-fold reduction in [(18)f]fdg pet acquisition time in digital pet/ct
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399218/
https://www.ncbi.nlm.nih.gov/pubmed/35593925
http://dx.doi.org/10.1007/s00259-022-05800-1
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