QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study

Abstract The purpose of this study was to directly and quantitatively measure BMD from Cone-beam CT (CBCT) images by enhancing the linearity and uniformity of the bone intensities based on a hybrid deep-learning model (QCBCT-NET) of combining the generative adversarial network (Cycle-GAN) and U-Net,...

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Autores principales:	Tae-Hoon Yong, Su Yang, Sang-Jeong Lee, Chansoo Park, Jo-Eun Kim, Kyung-Hoe Huh, Sam-Sun Lee, Min-Suk Heo, Won-Jin Yi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/aa130d6308e447d8b687bc06bba75fce
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spelling	oai:doaj.org-article:aa130d6308e447d8b687bc06bba75fce2021-12-02T17:55:03ZQCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study10.1038/s41598-021-94359-22045-2322https://doaj.org/article/aa130d6308e447d8b687bc06bba75fce2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94359-2https://doaj.org/toc/2045-2322Abstract The purpose of this study was to directly and quantitatively measure BMD from Cone-beam CT (CBCT) images by enhancing the linearity and uniformity of the bone intensities based on a hybrid deep-learning model (QCBCT-NET) of combining the generative adversarial network (Cycle-GAN) and U-Net, and to compare the bone images enhanced by the QCBCT-NET with those by Cycle-GAN and U-Net. We used two phantoms of human skulls encased in acrylic, one for the training and validation datasets, and the other for the test dataset. We proposed the QCBCT-NET consisting of Cycle-GAN with residual blocks and a multi-channel U-Net using paired training data of quantitative CT (QCT) and CBCT images. The BMD images produced by QCBCT-NET significantly outperformed the images produced by the Cycle-GAN or the U-Net in mean absolute difference (MAD), peak signal to noise ratio (PSNR), normalized cross-correlation (NCC), structural similarity (SSIM), and linearity when compared to the original QCT image. The QCBCT-NET improved the contrast of the bone images by reflecting the original BMD distribution of the QCT image locally using the Cycle-GAN, and also spatial uniformity of the bone images by globally suppressing image artifacts and noise using the two-channel U-Net. The QCBCT-NET substantially enhanced the linearity, uniformity, and contrast as well as the anatomical and quantitative accuracy of the bone images, and demonstrated more accuracy than the Cycle-GAN and the U-Net for quantitatively measuring BMD in CBCT.Tae-Hoon YongSu YangSang-Jeong LeeChansoo ParkJo-Eun KimKyung-Hoe HuhSam-Sun LeeMin-Suk HeoWon-Jin YiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tae-Hoon Yong Su Yang Sang-Jeong Lee Chansoo Park Jo-Eun Kim Kyung-Hoe Huh Sam-Sun Lee Min-Suk Heo Won-Jin Yi QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study
description	Abstract The purpose of this study was to directly and quantitatively measure BMD from Cone-beam CT (CBCT) images by enhancing the linearity and uniformity of the bone intensities based on a hybrid deep-learning model (QCBCT-NET) of combining the generative adversarial network (Cycle-GAN) and U-Net, and to compare the bone images enhanced by the QCBCT-NET with those by Cycle-GAN and U-Net. We used two phantoms of human skulls encased in acrylic, one for the training and validation datasets, and the other for the test dataset. We proposed the QCBCT-NET consisting of Cycle-GAN with residual blocks and a multi-channel U-Net using paired training data of quantitative CT (QCT) and CBCT images. The BMD images produced by QCBCT-NET significantly outperformed the images produced by the Cycle-GAN or the U-Net in mean absolute difference (MAD), peak signal to noise ratio (PSNR), normalized cross-correlation (NCC), structural similarity (SSIM), and linearity when compared to the original QCT image. The QCBCT-NET improved the contrast of the bone images by reflecting the original BMD distribution of the QCT image locally using the Cycle-GAN, and also spatial uniformity of the bone images by globally suppressing image artifacts and noise using the two-channel U-Net. The QCBCT-NET substantially enhanced the linearity, uniformity, and contrast as well as the anatomical and quantitative accuracy of the bone images, and demonstrated more accuracy than the Cycle-GAN and the U-Net for quantitatively measuring BMD in CBCT.
format	article
author	Tae-Hoon Yong Su Yang Sang-Jeong Lee Chansoo Park Jo-Eun Kim Kyung-Hoe Huh Sam-Sun Lee Min-Suk Heo Won-Jin Yi
author_facet	Tae-Hoon Yong Su Yang Sang-Jeong Lee Chansoo Park Jo-Eun Kim Kyung-Hoe Huh Sam-Sun Lee Min-Suk Heo Won-Jin Yi
author_sort	Tae-Hoon Yong
title	QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study
title_short	QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study
title_full	QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study
title_fullStr	QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study
title_full_unstemmed	QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study
title_sort	qcbct-net for direct measurement of bone mineral density from quantitative cone-beam ct: a human skull phantom study
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/aa130d6308e447d8b687bc06bba75fce
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QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study

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