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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer Berlin Heidelberg
2022
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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. |
format | Online Article Text |
id | pubmed-9399218 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
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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