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Direct attenuation correction for (99m)Tc-3PRGD(2) chest SPECT lung cancer images using deep learning

INTRODUCTION: The attenuation correction technique of single photon emission computed tomography (SPECT) images is essential for early diagnosis, therapeutic evaluation, and pharmacokinetic studies of lung cancer. (99m)Tc-3PRGD(2) is a novel radiotracer for the early diagnosis and evaluation of trea...

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Autores principales: Xing, Haiqun, Wang, Tong, Jin, Xiaona, Tian, Jian, Ba, Jiantao, Jing, Hongli, Li, Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10218122/
https://www.ncbi.nlm.nih.gov/pubmed/37251952
http://dx.doi.org/10.3389/fonc.2023.1165664
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author Xing, Haiqun
Wang, Tong
Jin, Xiaona
Tian, Jian
Ba, Jiantao
Jing, Hongli
Li, Fang
author_facet Xing, Haiqun
Wang, Tong
Jin, Xiaona
Tian, Jian
Ba, Jiantao
Jing, Hongli
Li, Fang
author_sort Xing, Haiqun
collection PubMed
description INTRODUCTION: The attenuation correction technique of single photon emission computed tomography (SPECT) images is essential for early diagnosis, therapeutic evaluation, and pharmacokinetic studies of lung cancer. (99m)Tc-3PRGD(2) is a novel radiotracer for the early diagnosis and evaluation of treatment effects of lung cancer. This study preliminary discusses the deep learning method to directly correct the attenuation of (99m)Tc-3PRGD(2) chest SPECT images. METHODS: Retrospective analysis was performed on 53 patients with pathological diagnosis of lung cancer who received (99m)Tc-3PRGD(2) chest SPECT/CT. All patients’ SPECT/CT images were reconstructed with CT attenuation correction (CT-AC) and without attenuation correction (NAC). The CT-AC image was used as the reference standard (Ground Truth) to train the attenuation correction (DL-AC) SPECT image model using deep learning. A total of 48 of 53 cases were divided randomly into the training set, the remaining 5 were divided into the testing set. Using 3D Unet neural network, the mean square error loss function (MSELoss) of 0.0001 was selected. A testing set is used to evaluate the model quality, using the SPECT image quality evaluation and quantitative analysis of lung lesions tumor-to-background (T/B). RESULTS: SPECT imaging quality metrics between DL-AC and CT-AC including mean absolute error (MAE), mean-square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), normalized root mean square error (NRMSE), and normalized Mutual Information (NMI) of the testing set are 2.62 ± 0.45, 58.5 ± 14.85, 45.67 ± 2.80, 0.82 ± 0.02, 0.07 ± 0.04, and 1.58 ± 0.06, respectively. These results indicate PSNR > 42, SSIM > 0.8, and NRMSE < 0.11. Lung lesions T/B (maximum) of CT-AC and DL-AC groups are 4.36 ± 3.52 and 4.33 ± 3.09, respectively (p = 0.81). There are no significant differences between two attenuation correction methods. CONCLUSION: Our preliminary research results indicate that using the DL-AC method to directly correct (99m)Tc-3PRGD(2) chest SPECT images is highly accurate and feasible for SPECT without configuration with CT or treatment effect evaluation using multiple SPECT/CT scans.
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spelling pubmed-102181222023-05-27 Direct attenuation correction for (99m)Tc-3PRGD(2) chest SPECT lung cancer images using deep learning Xing, Haiqun Wang, Tong Jin, Xiaona Tian, Jian Ba, Jiantao Jing, Hongli Li, Fang Front Oncol Oncology INTRODUCTION: The attenuation correction technique of single photon emission computed tomography (SPECT) images is essential for early diagnosis, therapeutic evaluation, and pharmacokinetic studies of lung cancer. (99m)Tc-3PRGD(2) is a novel radiotracer for the early diagnosis and evaluation of treatment effects of lung cancer. This study preliminary discusses the deep learning method to directly correct the attenuation of (99m)Tc-3PRGD(2) chest SPECT images. METHODS: Retrospective analysis was performed on 53 patients with pathological diagnosis of lung cancer who received (99m)Tc-3PRGD(2) chest SPECT/CT. All patients’ SPECT/CT images were reconstructed with CT attenuation correction (CT-AC) and without attenuation correction (NAC). The CT-AC image was used as the reference standard (Ground Truth) to train the attenuation correction (DL-AC) SPECT image model using deep learning. A total of 48 of 53 cases were divided randomly into the training set, the remaining 5 were divided into the testing set. Using 3D Unet neural network, the mean square error loss function (MSELoss) of 0.0001 was selected. A testing set is used to evaluate the model quality, using the SPECT image quality evaluation and quantitative analysis of lung lesions tumor-to-background (T/B). RESULTS: SPECT imaging quality metrics between DL-AC and CT-AC including mean absolute error (MAE), mean-square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), normalized root mean square error (NRMSE), and normalized Mutual Information (NMI) of the testing set are 2.62 ± 0.45, 58.5 ± 14.85, 45.67 ± 2.80, 0.82 ± 0.02, 0.07 ± 0.04, and 1.58 ± 0.06, respectively. These results indicate PSNR > 42, SSIM > 0.8, and NRMSE < 0.11. Lung lesions T/B (maximum) of CT-AC and DL-AC groups are 4.36 ± 3.52 and 4.33 ± 3.09, respectively (p = 0.81). There are no significant differences between two attenuation correction methods. CONCLUSION: Our preliminary research results indicate that using the DL-AC method to directly correct (99m)Tc-3PRGD(2) chest SPECT images is highly accurate and feasible for SPECT without configuration with CT or treatment effect evaluation using multiple SPECT/CT scans. Frontiers Media S.A. 2023-05-12 /pmc/articles/PMC10218122/ /pubmed/37251952 http://dx.doi.org/10.3389/fonc.2023.1165664 Text en Copyright © 2023 Xing, Wang, Jin, Tian, Ba, Jing and Li https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Xing, Haiqun
Wang, Tong
Jin, Xiaona
Tian, Jian
Ba, Jiantao
Jing, Hongli
Li, Fang
Direct attenuation correction for (99m)Tc-3PRGD(2) chest SPECT lung cancer images using deep learning
title Direct attenuation correction for (99m)Tc-3PRGD(2) chest SPECT lung cancer images using deep learning
title_full Direct attenuation correction for (99m)Tc-3PRGD(2) chest SPECT lung cancer images using deep learning
title_fullStr Direct attenuation correction for (99m)Tc-3PRGD(2) chest SPECT lung cancer images using deep learning
title_full_unstemmed Direct attenuation correction for (99m)Tc-3PRGD(2) chest SPECT lung cancer images using deep learning
title_short Direct attenuation correction for (99m)Tc-3PRGD(2) chest SPECT lung cancer images using deep learning
title_sort direct attenuation correction for (99m)tc-3prgd(2) chest spect lung cancer images using deep learning
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10218122/
https://www.ncbi.nlm.nih.gov/pubmed/37251952
http://dx.doi.org/10.3389/fonc.2023.1165664
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