Image similarity evaluation of the bulk-density-assigned synthetic CT derived from MRI of intracranial regions for radiation treatment.

Various methods for radiation-dose calculation have been investigated over previous decades, focusing on the use of magnetic resonance imaging (MRI) only. The bulk-density-assignment method based on manual segmentation has exhibited promising results compared to dose-calculation with computed tomogr...

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Main Authors: Shin-Wook Kim, Hun-Joo Shin, Jin-Ho Hwang, Jin-Sol Shin, Sung-Kwang Park, Jin-Young Kim, Ki-Jun Kim, Chul-Seung Kay, Young-Nam Kang
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2017-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC5605009?pdf=render
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author Shin-Wook Kim
Hun-Joo Shin
Jin-Ho Hwang
Jin-Sol Shin
Sung-Kwang Park
Jin-Young Kim
Ki-Jun Kim
Chul-Seung Kay
Young-Nam Kang
author_facet Shin-Wook Kim
Hun-Joo Shin
Jin-Ho Hwang
Jin-Sol Shin
Sung-Kwang Park
Jin-Young Kim
Ki-Jun Kim
Chul-Seung Kay
Young-Nam Kang
author_sort Shin-Wook Kim
collection DOAJ
description Various methods for radiation-dose calculation have been investigated over previous decades, focusing on the use of magnetic resonance imaging (MRI) only. The bulk-density-assignment method based on manual segmentation has exhibited promising results compared to dose-calculation with computed tomography (CT). However, this method cannot be easily implemented in clinical practice due to its time-consuming nature. Therefore, we investigated an automatic anatomy segmentation method with the intention of providing the proper methodology to evaluate synthetic CT images for a radiation-dose calculation based on MR images.CT images of 20 brain cancer patients were selected, and their MR images including T1-weighted, T2-weighted, and PETRA were retrospectively collected. Eight anatomies of the patients, such as the body, air, eyeball, lens, cavity, ventricle, brainstem, and bone, were segmented for bulk-density-assigned CT image (BCT) generation. In addition, water-equivalent CT images (WCT) with only two anatomies-body and air-were generated for a comparison with BCT. Histogram comparison and gamma analysis were performed by comparison with the original CT images, after the evaluation of automatic segmentation performance with the dice similarity coefficient (DSC), false negative dice (FND) coefficient, and false positive dice (FPD) coefficient.The highest DSC value was 99.34 for air segmentation, and the lowest DSC value was 73.50 for bone segmentation. For lens segmentation, relatively high FND and FPD values were measured. The cavity and bone were measured as over-segmented anatomies having higher FPD values than FND. The measured histogram comparison results of BCT were better than those of WCT in all cases. In gamma analysis, the averaged improvement of BCT compared to WCT was measured. All the measured results of BCT were better than those of WCT. Therefore, the results of this study show that the introduced methods, such as histogram comparison and gamma analysis, are valid for the evaluation of the synthetic CT generation from MR images.The image similarity results showed that BCT has superior results compared to WCT for all measurements performed in this study. Consequently, more accurate radiation treatment for the intracranial regions can be expected when the proper image similarity evaluation introduced in this study is performed.
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spelling doaj.art-6d7ab065e8104e038147fce4348aaa5d2022-12-22T00:11:56ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01129e018508210.1371/journal.pone.0185082Image similarity evaluation of the bulk-density-assigned synthetic CT derived from MRI of intracranial regions for radiation treatment.Shin-Wook KimHun-Joo ShinJin-Ho HwangJin-Sol ShinSung-Kwang ParkJin-Young KimKi-Jun KimChul-Seung KayYoung-Nam KangVarious methods for radiation-dose calculation have been investigated over previous decades, focusing on the use of magnetic resonance imaging (MRI) only. The bulk-density-assignment method based on manual segmentation has exhibited promising results compared to dose-calculation with computed tomography (CT). However, this method cannot be easily implemented in clinical practice due to its time-consuming nature. Therefore, we investigated an automatic anatomy segmentation method with the intention of providing the proper methodology to evaluate synthetic CT images for a radiation-dose calculation based on MR images.CT images of 20 brain cancer patients were selected, and their MR images including T1-weighted, T2-weighted, and PETRA were retrospectively collected. Eight anatomies of the patients, such as the body, air, eyeball, lens, cavity, ventricle, brainstem, and bone, were segmented for bulk-density-assigned CT image (BCT) generation. In addition, water-equivalent CT images (WCT) with only two anatomies-body and air-were generated for a comparison with BCT. Histogram comparison and gamma analysis were performed by comparison with the original CT images, after the evaluation of automatic segmentation performance with the dice similarity coefficient (DSC), false negative dice (FND) coefficient, and false positive dice (FPD) coefficient.The highest DSC value was 99.34 for air segmentation, and the lowest DSC value was 73.50 for bone segmentation. For lens segmentation, relatively high FND and FPD values were measured. The cavity and bone were measured as over-segmented anatomies having higher FPD values than FND. The measured histogram comparison results of BCT were better than those of WCT in all cases. In gamma analysis, the averaged improvement of BCT compared to WCT was measured. All the measured results of BCT were better than those of WCT. Therefore, the results of this study show that the introduced methods, such as histogram comparison and gamma analysis, are valid for the evaluation of the synthetic CT generation from MR images.The image similarity results showed that BCT has superior results compared to WCT for all measurements performed in this study. Consequently, more accurate radiation treatment for the intracranial regions can be expected when the proper image similarity evaluation introduced in this study is performed.http://europepmc.org/articles/PMC5605009?pdf=render
spellingShingle Shin-Wook Kim
Hun-Joo Shin
Jin-Ho Hwang
Jin-Sol Shin
Sung-Kwang Park
Jin-Young Kim
Ki-Jun Kim
Chul-Seung Kay
Young-Nam Kang
Image similarity evaluation of the bulk-density-assigned synthetic CT derived from MRI of intracranial regions for radiation treatment.
PLoS ONE
title Image similarity evaluation of the bulk-density-assigned synthetic CT derived from MRI of intracranial regions for radiation treatment.
title_full Image similarity evaluation of the bulk-density-assigned synthetic CT derived from MRI of intracranial regions for radiation treatment.
title_fullStr Image similarity evaluation of the bulk-density-assigned synthetic CT derived from MRI of intracranial regions for radiation treatment.
title_full_unstemmed Image similarity evaluation of the bulk-density-assigned synthetic CT derived from MRI of intracranial regions for radiation treatment.
title_short Image similarity evaluation of the bulk-density-assigned synthetic CT derived from MRI of intracranial regions for radiation treatment.
title_sort image similarity evaluation of the bulk density assigned synthetic ct derived from mri of intracranial regions for radiation treatment
url http://europepmc.org/articles/PMC5605009?pdf=render
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