Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients

Abstract Cone-beam computed tomography (CBCT) integrated with a linear accelerator is widely used to increase the accuracy of radiotherapy and plays an important role in image-guided radiotherapy (IGRT). For comparison with fan-beam computed tomography (FBCT), the image quality of CBCT is indistinct...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hui-Ju Tien, Hsin-Chih Yang, Pei-Wei Shueng, Jyh-Cheng Chen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/718d6a340dc8497e9f1128a01ff09a5d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:718d6a340dc8497e9f1128a01ff09a5d
record_format	dspace
spelling	oai:doaj.org-article:718d6a340dc8497e9f1128a01ff09a5d2021-12-02T14:01:25ZCone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients10.1038/s41598-020-80803-22045-2322https://doaj.org/article/718d6a340dc8497e9f1128a01ff09a5d2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80803-2https://doaj.org/toc/2045-2322Abstract Cone-beam computed tomography (CBCT) integrated with a linear accelerator is widely used to increase the accuracy of radiotherapy and plays an important role in image-guided radiotherapy (IGRT). For comparison with fan-beam computed tomography (FBCT), the image quality of CBCT is indistinct due to X-ray scattering, noise, and artefacts. We proposed a deep learning model, “Cycle-Deblur GAN”, combined with CycleGAN and Deblur-GAN models to improve the image quality of chest CBCT images. The 8706 CBCT and FBCT image pairs were used for training, and 1150 image pairs were used for testing in deep learning. The generated CBCT images from the Cycle-Deblur GAN model demonstrated closer CT values to FBCT in the lung, breast, mediastinum, and sternum compared to the CycleGAN and RED-CNN models. The quantitative evaluations of MAE, PSNR, and SSIM for CBCT generated from the Cycle-Deblur GAN model demonstrated better results than the CycleGAN and RED-CNN models. The Cycle-Deblur GAN model improved image quality and CT-value accuracy and preserved structural details for chest CBCT images.Hui-Ju TienHsin-Chih YangPei-Wei ShuengJyh-Cheng ChenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hui-Ju Tien Hsin-Chih Yang Pei-Wei Shueng Jyh-Cheng Chen Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients
description	Abstract Cone-beam computed tomography (CBCT) integrated with a linear accelerator is widely used to increase the accuracy of radiotherapy and plays an important role in image-guided radiotherapy (IGRT). For comparison with fan-beam computed tomography (FBCT), the image quality of CBCT is indistinct due to X-ray scattering, noise, and artefacts. We proposed a deep learning model, “Cycle-Deblur GAN”, combined with CycleGAN and Deblur-GAN models to improve the image quality of chest CBCT images. The 8706 CBCT and FBCT image pairs were used for training, and 1150 image pairs were used for testing in deep learning. The generated CBCT images from the Cycle-Deblur GAN model demonstrated closer CT values to FBCT in the lung, breast, mediastinum, and sternum compared to the CycleGAN and RED-CNN models. The quantitative evaluations of MAE, PSNR, and SSIM for CBCT generated from the Cycle-Deblur GAN model demonstrated better results than the CycleGAN and RED-CNN models. The Cycle-Deblur GAN model improved image quality and CT-value accuracy and preserved structural details for chest CBCT images.
format	article
author	Hui-Ju Tien Hsin-Chih Yang Pei-Wei Shueng Jyh-Cheng Chen
author_facet	Hui-Ju Tien Hsin-Chih Yang Pei-Wei Shueng Jyh-Cheng Chen
author_sort	Hui-Ju Tien
title	Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients
title_short	Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients
title_full	Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients
title_fullStr	Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients
title_full_unstemmed	Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients
title_sort	cone-beam ct image quality improvement using cycle-deblur consistent adversarial networks (cycle-deblur gan) for chest ct imaging in breast cancer patients
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/718d6a340dc8497e9f1128a01ff09a5d
work_keys_str_mv	AT huijutien conebeamctimagequalityimprovementusingcycledeblurconsistentadversarialnetworkscycledeblurganforchestctimaginginbreastcancerpatients AT hsinchihyang conebeamctimagequalityimprovementusingcycledeblurconsistentadversarialnetworkscycledeblurganforchestctimaginginbreastcancerpatients AT peiweishueng conebeamctimagequalityimprovementusingcycledeblurconsistentadversarialnetworkscycledeblurganforchestctimaginginbreastcancerpatients AT jyhchengchen conebeamctimagequalityimprovementusingcycledeblurconsistentadversarialnetworkscycledeblurganforchestctimaginginbreastcancerpatients
_version_	1718392147344883712

Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients

Ejemplares similares