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A Megavoltage CT Image Enhancement Method for Image-Guided and Adaptive Helical TomoTherapy

Purpose: To propose a novel method to improve the mega-voltage CT (MVCT) image quality for helical TomoTherapy while maintaining the stability on dose calculation. Materials and Methods: The Block-Matching 3D-transform (BM3D) and Discriminative Feature Representation (DFR) methods were combined into...

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Autores principales: Liu, Yaru, Yue, Chenxi, Zhu, Jian, Yu, Haining, Cheng, Yang, Yin, Yong, Li, Baosheng, Dong, Jiwen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6524307/
https://www.ncbi.nlm.nih.gov/pubmed/31134157
http://dx.doi.org/10.3389/fonc.2019.00362
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author Liu, Yaru
Yue, Chenxi
Zhu, Jian
Yu, Haining
Cheng, Yang
Yin, Yong
Li, Baosheng
Dong, Jiwen
author_facet Liu, Yaru
Yue, Chenxi
Zhu, Jian
Yu, Haining
Cheng, Yang
Yin, Yong
Li, Baosheng
Dong, Jiwen
author_sort Liu, Yaru
collection PubMed
description Purpose: To propose a novel method to improve the mega-voltage CT (MVCT) image quality for helical TomoTherapy while maintaining the stability on dose calculation. Materials and Methods: The Block-Matching 3D-transform (BM3D) and Discriminative Feature Representation (DFR) methods were combined into a novel BM3D + DFR method for their respective advantages. A phantom (Catphan504) and three serials of clinical (head & neck, chest, and pelvis) MVCT images from 30 patients were acquired using the helical TomoTherapy system. The contrast-to-noise ratio (CNR) and edge detection algorithm (canny) was employed for image quality comparisons between the original and BM3D + DFR enhanced MVCT. A simulated rectangular field of 6 MV X-ray beams were vertically delivered on the original and post-processed MVCT serials of the same CT density phantom, and the dose curves on both serials were compared to test the effects of image enhancement on dose calculation accuracy. Results: In total, 466 transversal MVCT slices were acquired and processed by both BM3D and the proposed BM3D + DFR methods. Compared to the original MVCT image, the BM3D + DFR method presented a remarkable improvement in terms of the soft tissue contrast and noise reduction. For the phantom image, the CNR of the region of interest (ROI) was improved from 1.70 to 4.03. The average CNR of ROIs for 10 patients from each anatomical group, were increased significantly from 1.45 ± 1.51 to 2.09 ± 1.68 for the head & neck (p < 0.001), from 0.92 ± 0.78 to 1.36 ± 0.85 for the chest (p < 0.001), and from 1.12 ± 1.22 to 1.76 ± 1.31 for the pelvis (p < 0.001), respectively. The canny edge detection operator showed that BM3D + DFR provided clearer organ boundaries with less chaos. The root-mean-square of the dosimetry difference on the iso-center passed horizontal dose profile curves and vertical percentage depth dose curves were only 0.09% and 0.06%, respectively. Conclusions: The proposed BM3D + DFR method is feasible to improve the soft tissue contrast for the original MVCT images with coincidence in dose calculation and without compromising resolution. After integration in clinical workflow, the post-processed MVCT may be better applied on image-guided and adaptive helical TomoTherapy.
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spelling pubmed-65243072019-05-27 A Megavoltage CT Image Enhancement Method for Image-Guided and Adaptive Helical TomoTherapy Liu, Yaru Yue, Chenxi Zhu, Jian Yu, Haining Cheng, Yang Yin, Yong Li, Baosheng Dong, Jiwen Front Oncol Oncology Purpose: To propose a novel method to improve the mega-voltage CT (MVCT) image quality for helical TomoTherapy while maintaining the stability on dose calculation. Materials and Methods: The Block-Matching 3D-transform (BM3D) and Discriminative Feature Representation (DFR) methods were combined into a novel BM3D + DFR method for their respective advantages. A phantom (Catphan504) and three serials of clinical (head & neck, chest, and pelvis) MVCT images from 30 patients were acquired using the helical TomoTherapy system. The contrast-to-noise ratio (CNR) and edge detection algorithm (canny) was employed for image quality comparisons between the original and BM3D + DFR enhanced MVCT. A simulated rectangular field of 6 MV X-ray beams were vertically delivered on the original and post-processed MVCT serials of the same CT density phantom, and the dose curves on both serials were compared to test the effects of image enhancement on dose calculation accuracy. Results: In total, 466 transversal MVCT slices were acquired and processed by both BM3D and the proposed BM3D + DFR methods. Compared to the original MVCT image, the BM3D + DFR method presented a remarkable improvement in terms of the soft tissue contrast and noise reduction. For the phantom image, the CNR of the region of interest (ROI) was improved from 1.70 to 4.03. The average CNR of ROIs for 10 patients from each anatomical group, were increased significantly from 1.45 ± 1.51 to 2.09 ± 1.68 for the head & neck (p < 0.001), from 0.92 ± 0.78 to 1.36 ± 0.85 for the chest (p < 0.001), and from 1.12 ± 1.22 to 1.76 ± 1.31 for the pelvis (p < 0.001), respectively. The canny edge detection operator showed that BM3D + DFR provided clearer organ boundaries with less chaos. The root-mean-square of the dosimetry difference on the iso-center passed horizontal dose profile curves and vertical percentage depth dose curves were only 0.09% and 0.06%, respectively. Conclusions: The proposed BM3D + DFR method is feasible to improve the soft tissue contrast for the original MVCT images with coincidence in dose calculation and without compromising resolution. After integration in clinical workflow, the post-processed MVCT may be better applied on image-guided and adaptive helical TomoTherapy. Frontiers Media S.A. 2019-05-10 /pmc/articles/PMC6524307/ /pubmed/31134157 http://dx.doi.org/10.3389/fonc.2019.00362 Text en Copyright © 2019 Liu, Yue, Zhu, Yu, Cheng, Yin, Li and Dong. http://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
Liu, Yaru
Yue, Chenxi
Zhu, Jian
Yu, Haining
Cheng, Yang
Yin, Yong
Li, Baosheng
Dong, Jiwen
A Megavoltage CT Image Enhancement Method for Image-Guided and Adaptive Helical TomoTherapy
title A Megavoltage CT Image Enhancement Method for Image-Guided and Adaptive Helical TomoTherapy
title_full A Megavoltage CT Image Enhancement Method for Image-Guided and Adaptive Helical TomoTherapy
title_fullStr A Megavoltage CT Image Enhancement Method for Image-Guided and Adaptive Helical TomoTherapy
title_full_unstemmed A Megavoltage CT Image Enhancement Method for Image-Guided and Adaptive Helical TomoTherapy
title_short A Megavoltage CT Image Enhancement Method for Image-Guided and Adaptive Helical TomoTherapy
title_sort megavoltage ct image enhancement method for image-guided and adaptive helical tomotherapy
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6524307/
https://www.ncbi.nlm.nih.gov/pubmed/31134157
http://dx.doi.org/10.3389/fonc.2019.00362
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