Sparse-view sinogram interpolation and tomography reconstruction using generative adversarial network

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 107 === Computed Tomography Scan (CT Scan) is one of the most important techniques in modern medical field, which can make projection views of an object in different angles by electromagnetic radiation without destructive test, then reconstruct inner structure of the...

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Bibliographic Details
Main Authors:	Lin, Yun-Kai, 林韵凱
Other Authors:	Ching, Yu-Tai
Format:	Others
Language:	en_US
Published:	2019
Online Access:	http://ndltd.ncl.edu.tw/handle/9m8fu9

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spelling	ndltd-TW-107NCTU53941112019-11-26T05:16:52Z http://ndltd.ncl.edu.tw/handle/9m8fu9 Sparse-view sinogram interpolation and tomography reconstruction using generative adversarial network 以生成對抗網路內插稀疏正弦圖及斷層影像重建 Lin, Yun-Kai 林韵凱碩士國立交通大學資訊科學與工程研究所 107 Computed Tomography Scan (CT Scan) is one of the most important techniques in modern medical field, which can make projection views of an object in different angles by electromagnetic radiation without destructive test, then reconstruct inner structure of the object through projection data. In sparse-view sinogram, the number of projection images taken is less than usual, but the quality of the reconstruction image is worse than full-view sinogram. In this study, we apply conditional Generative Adversarial Network (GAN) to synthesis full-view sinogram from sparse-view sinogram, which can generate high-quality reconstruction images. Ching, Yu-Tai 荊宇泰 2019 學位論文 ; thesis 20 en_US
collection	NDLTD
language	en_US
format	Others
sources	NDLTD
description	碩士 === 國立交通大學 === 資訊科學與工程研究所 === 107 === Computed Tomography Scan (CT Scan) is one of the most important techniques in modern medical field, which can make projection views of an object in different angles by electromagnetic radiation without destructive test, then reconstruct inner structure of the object through projection data. In sparse-view sinogram, the number of projection images taken is less than usual, but the quality of the reconstruction image is worse than full-view sinogram. In this study, we apply conditional Generative Adversarial Network (GAN) to synthesis full-view sinogram from sparse-view sinogram, which can generate high-quality reconstruction images.
author2	Ching, Yu-Tai
author_facet	Ching, Yu-Tai Lin, Yun-Kai 林韵凱
author	Lin, Yun-Kai 林韵凱
spellingShingle	Lin, Yun-Kai 林韵凱 Sparse-view sinogram interpolation and tomography reconstruction using generative adversarial network
author_sort	Lin, Yun-Kai
title	Sparse-view sinogram interpolation and tomography reconstruction using generative adversarial network
title_short	Sparse-view sinogram interpolation and tomography reconstruction using generative adversarial network
title_full	Sparse-view sinogram interpolation and tomography reconstruction using generative adversarial network
title_fullStr	Sparse-view sinogram interpolation and tomography reconstruction using generative adversarial network
title_full_unstemmed	Sparse-view sinogram interpolation and tomography reconstruction using generative adversarial network
title_sort	sparse-view sinogram interpolation and tomography reconstruction using generative adversarial network
publishDate	2019
url	http://ndltd.ncl.edu.tw/handle/9m8fu9
work_keys_str_mv	AT linyunkai sparseviewsinograminterpolationandtomographyreconstructionusinggenerativeadversarialnetwork AT línyùnkǎi sparseviewsinograminterpolationandtomographyreconstructionusinggenerativeadversarialnetwork AT linyunkai yǐshēngchéngduìkàngwǎnglùnèichāxīshūzhèngxiántújíduàncéngyǐngxiàngzhòngjiàn AT línyùnkǎi yǐshēngchéngduìkàngwǎnglùnèichāxīshūzhèngxiántújíduàncéngyǐngxiàngzhòngjiàn
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Sparse-view sinogram interpolation and tomography reconstruction using generative adversarial network

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