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Artificial intelligence guided enhancement of digital PET: scans as fast as CT?
PURPOSE: Both digital positron emission tomography (PET) detector technologies and artificial intelligence based image post-reconstruction methods allow to reduce the PET acquisition time while maintaining diagnostic quality. The aim of this study was to acquire ultra-low-count fluorodeoxyglucose (F...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9606065/ https://www.ncbi.nlm.nih.gov/pubmed/35904589 http://dx.doi.org/10.1007/s00259-022-05901-x |
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author | Hosch, René Weber, Manuel Sraieb, Miriam Flaschel, Nils Haubold, Johannes Kim, Moon-Sung Umutlu, Lale Kleesiek, Jens Herrmann, Ken Nensa, Felix Rischpler, Christoph Koitka, Sven Seifert, Robert Kersting, David |
author_facet | Hosch, René Weber, Manuel Sraieb, Miriam Flaschel, Nils Haubold, Johannes Kim, Moon-Sung Umutlu, Lale Kleesiek, Jens Herrmann, Ken Nensa, Felix Rischpler, Christoph Koitka, Sven Seifert, Robert Kersting, David |
author_sort | Hosch, René |
collection | PubMed |
description | PURPOSE: Both digital positron emission tomography (PET) detector technologies and artificial intelligence based image post-reconstruction methods allow to reduce the PET acquisition time while maintaining diagnostic quality. The aim of this study was to acquire ultra-low-count fluorodeoxyglucose (FDG) ExtremePET images on a digital PET/computed tomography (CT) scanner at an acquisition time comparable to a CT scan and to generate synthetic full-dose PET images using an artificial neural network. METHODS: This is a prospective, single-arm, single-center phase I/II imaging study. A total of 587 patients were included. For each patient, a standard and an ultra-low-count FDG PET/CT scan (whole-body acquisition time about 30 s) were acquired. A modified pix2pixHD deep-learning network was trained employing 387 data sets as training and 200 as test cohort. Three models (PET-only and PET/CT with or without group convolution) were compared. Detectability and quantification were evaluated. RESULTS: The PET/CT input model with group convolution performed best regarding lesion signal recovery and was selected for detailed evaluation. Synthetic PET images were of high visual image quality; mean absolute lesion SUV(max) (maximum standardized uptake value) difference was 1.5. Patient-based sensitivity and specificity for lesion detection were 79% and 100%, respectively. Not-detected lesions were of lower tracer uptake and lesion volume. In a matched-pair comparison, patient-based (lesion-based) detection rate was 89% (78%) for PERCIST (PET response criteria in solid tumors)-measurable and 36% (22%) for non PERCIST-measurable lesions. CONCLUSION: Lesion detectability and lesion quantification were promising in the context of extremely fast acquisition times. Possible application scenarios might include re-staging of late-stage cancer patients, in whom assessment of total tumor burden can be of higher relevance than detailed evaluation of small and low-uptake lesions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00259-022-05901-x. |
format | Online Article Text |
id | pubmed-9606065 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-96060652022-10-28 Artificial intelligence guided enhancement of digital PET: scans as fast as CT? Hosch, René Weber, Manuel Sraieb, Miriam Flaschel, Nils Haubold, Johannes Kim, Moon-Sung Umutlu, Lale Kleesiek, Jens Herrmann, Ken Nensa, Felix Rischpler, Christoph Koitka, Sven Seifert, Robert Kersting, David Eur J Nucl Med Mol Imaging Original Article PURPOSE: Both digital positron emission tomography (PET) detector technologies and artificial intelligence based image post-reconstruction methods allow to reduce the PET acquisition time while maintaining diagnostic quality. The aim of this study was to acquire ultra-low-count fluorodeoxyglucose (FDG) ExtremePET images on a digital PET/computed tomography (CT) scanner at an acquisition time comparable to a CT scan and to generate synthetic full-dose PET images using an artificial neural network. METHODS: This is a prospective, single-arm, single-center phase I/II imaging study. A total of 587 patients were included. For each patient, a standard and an ultra-low-count FDG PET/CT scan (whole-body acquisition time about 30 s) were acquired. A modified pix2pixHD deep-learning network was trained employing 387 data sets as training and 200 as test cohort. Three models (PET-only and PET/CT with or without group convolution) were compared. Detectability and quantification were evaluated. RESULTS: The PET/CT input model with group convolution performed best regarding lesion signal recovery and was selected for detailed evaluation. Synthetic PET images were of high visual image quality; mean absolute lesion SUV(max) (maximum standardized uptake value) difference was 1.5. Patient-based sensitivity and specificity for lesion detection were 79% and 100%, respectively. Not-detected lesions were of lower tracer uptake and lesion volume. In a matched-pair comparison, patient-based (lesion-based) detection rate was 89% (78%) for PERCIST (PET response criteria in solid tumors)-measurable and 36% (22%) for non PERCIST-measurable lesions. CONCLUSION: Lesion detectability and lesion quantification were promising in the context of extremely fast acquisition times. Possible application scenarios might include re-staging of late-stage cancer patients, in whom assessment of total tumor burden can be of higher relevance than detailed evaluation of small and low-uptake lesions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00259-022-05901-x. Springer Berlin Heidelberg 2022-07-29 2022 /pmc/articles/PMC9606065/ /pubmed/35904589 http://dx.doi.org/10.1007/s00259-022-05901-x 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 Hosch, René Weber, Manuel Sraieb, Miriam Flaschel, Nils Haubold, Johannes Kim, Moon-Sung Umutlu, Lale Kleesiek, Jens Herrmann, Ken Nensa, Felix Rischpler, Christoph Koitka, Sven Seifert, Robert Kersting, David Artificial intelligence guided enhancement of digital PET: scans as fast as CT? |
title | Artificial intelligence guided enhancement of digital PET: scans as fast as CT? |
title_full | Artificial intelligence guided enhancement of digital PET: scans as fast as CT? |
title_fullStr | Artificial intelligence guided enhancement of digital PET: scans as fast as CT? |
title_full_unstemmed | Artificial intelligence guided enhancement of digital PET: scans as fast as CT? |
title_short | Artificial intelligence guided enhancement of digital PET: scans as fast as CT? |
title_sort | artificial intelligence guided enhancement of digital pet: scans as fast as ct? |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9606065/ https://www.ncbi.nlm.nih.gov/pubmed/35904589 http://dx.doi.org/10.1007/s00259-022-05901-x |
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