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Acquisition time reduction in pediatric (99m)Tc‐DMSA planar imaging using deep learning
PURPOSE: Given the potential risk of motion artifacts, acquisition time reduction is desirable in pediatric (99m)Tc‐dimercaptosuccinic acid (DMSA) scintigraphy. The aim of this study was to evaluate the performance of predicted full‐acquisition‐time images from short‐acquisition‐time pediatric (99m)...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10243333/ https://www.ncbi.nlm.nih.gov/pubmed/37021382 http://dx.doi.org/10.1002/acm2.13978 |
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author | Ichikawa, Shota Sugimori, Hiroyuki Ichijiri, Koki Yoshimura, Takaaki Nagaki, Akio |
author_facet | Ichikawa, Shota Sugimori, Hiroyuki Ichijiri, Koki Yoshimura, Takaaki Nagaki, Akio |
author_sort | Ichikawa, Shota |
collection | PubMed |
description | PURPOSE: Given the potential risk of motion artifacts, acquisition time reduction is desirable in pediatric (99m)Tc‐dimercaptosuccinic acid (DMSA) scintigraphy. The aim of this study was to evaluate the performance of predicted full‐acquisition‐time images from short‐acquisition‐time pediatric (99m)Tc‐DMSA planar images with only 1/5th acquisition time using deep learning in terms of image quality and quantitative renal uptake measurement accuracy. METHODS: One hundred and fifty‐five cases that underwent pediatric (99m)Tc‐DMSA planar imaging as dynamic data for 10 min were retrospectively collected for the development of three deep learning models (DnCNN, Win5RB, and ResUnet), and the generation of full‐time images from short‐time images. We used the normalized mean squared error (NMSE), peak signal‐to‐noise ratio (PSNR), and structural similarity index metrics (SSIM) to evaluate the accuracy of the predicted full‐time images. In addition, the renal uptake of (99m)Tc‐DMSA was calculated, and the difference in renal uptake from the reference full‐time images was assessed using scatter plots with Pearson correlation and Bland–Altman plots. RESULTS: The predicted full‐time images from the deep learning models showed a significant improvement in image quality compared to the short‐time images with respect to the reference full‐time images. In particular, the predicted full‐time images obtained by ResUnet showed the lowest NMSE (0.4 [0.4−0.5] %) and the highest PSNR (55.4 [54.7−56.1] dB) and SSIM (0.997 [0.995−0.997]). For renal uptake, an extremely high correlation was achieved in all short‐time and three predicted full‐time images (R (2) > 0.999 for all). The Bland–Altman plots showed the lowest bias (−0.10) of renal uptake in ResUnet, while short‐time images showed the lowest variance (95% confidence interval: −0.14, 0.45) of renal uptake. CONCLUSIONS: Our proposed method is capable of producing images that are comparable to the original full‐acquisition‐time images, allowing for a reduction of acquisition time/injected dose in pediatric (99m)Tc‐DMSA planar imaging. |
format | Online Article Text |
id | pubmed-10243333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102433332023-06-07 Acquisition time reduction in pediatric (99m)Tc‐DMSA planar imaging using deep learning Ichikawa, Shota Sugimori, Hiroyuki Ichijiri, Koki Yoshimura, Takaaki Nagaki, Akio J Appl Clin Med Phys Medical Imaging PURPOSE: Given the potential risk of motion artifacts, acquisition time reduction is desirable in pediatric (99m)Tc‐dimercaptosuccinic acid (DMSA) scintigraphy. The aim of this study was to evaluate the performance of predicted full‐acquisition‐time images from short‐acquisition‐time pediatric (99m)Tc‐DMSA planar images with only 1/5th acquisition time using deep learning in terms of image quality and quantitative renal uptake measurement accuracy. METHODS: One hundred and fifty‐five cases that underwent pediatric (99m)Tc‐DMSA planar imaging as dynamic data for 10 min were retrospectively collected for the development of three deep learning models (DnCNN, Win5RB, and ResUnet), and the generation of full‐time images from short‐time images. We used the normalized mean squared error (NMSE), peak signal‐to‐noise ratio (PSNR), and structural similarity index metrics (SSIM) to evaluate the accuracy of the predicted full‐time images. In addition, the renal uptake of (99m)Tc‐DMSA was calculated, and the difference in renal uptake from the reference full‐time images was assessed using scatter plots with Pearson correlation and Bland–Altman plots. RESULTS: The predicted full‐time images from the deep learning models showed a significant improvement in image quality compared to the short‐time images with respect to the reference full‐time images. In particular, the predicted full‐time images obtained by ResUnet showed the lowest NMSE (0.4 [0.4−0.5] %) and the highest PSNR (55.4 [54.7−56.1] dB) and SSIM (0.997 [0.995−0.997]). For renal uptake, an extremely high correlation was achieved in all short‐time and three predicted full‐time images (R (2) > 0.999 for all). The Bland–Altman plots showed the lowest bias (−0.10) of renal uptake in ResUnet, while short‐time images showed the lowest variance (95% confidence interval: −0.14, 0.45) of renal uptake. CONCLUSIONS: Our proposed method is capable of producing images that are comparable to the original full‐acquisition‐time images, allowing for a reduction of acquisition time/injected dose in pediatric (99m)Tc‐DMSA planar imaging. John Wiley and Sons Inc. 2023-04-05 /pmc/articles/PMC10243333/ /pubmed/37021382 http://dx.doi.org/10.1002/acm2.13978 Text en © 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Medical Imaging Ichikawa, Shota Sugimori, Hiroyuki Ichijiri, Koki Yoshimura, Takaaki Nagaki, Akio Acquisition time reduction in pediatric (99m)Tc‐DMSA planar imaging using deep learning |
title | Acquisition time reduction in pediatric (99m)Tc‐DMSA planar imaging using deep learning |
title_full | Acquisition time reduction in pediatric (99m)Tc‐DMSA planar imaging using deep learning |
title_fullStr | Acquisition time reduction in pediatric (99m)Tc‐DMSA planar imaging using deep learning |
title_full_unstemmed | Acquisition time reduction in pediatric (99m)Tc‐DMSA planar imaging using deep learning |
title_short | Acquisition time reduction in pediatric (99m)Tc‐DMSA planar imaging using deep learning |
title_sort | acquisition time reduction in pediatric (99m)tc‐dmsa planar imaging using deep learning |
topic | Medical Imaging |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10243333/ https://www.ncbi.nlm.nih.gov/pubmed/37021382 http://dx.doi.org/10.1002/acm2.13978 |
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