Enhancing Gamma Knife Cone-beam Computed Tomography Image Quality Using Pix2pix Generative Adversarial Networks: A Deep Learning Approach

Aims: The study aims to develop a modified Pix2Pix convolutional neural network framework to enhance the quality of cone-beam computed tomography (CBCT) images. It also seeks to reduce the Hounsfield unit (HU) variations, making CBCT images closely resemble the internal anatomy as depicted in comput...

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主要な著者:	Prabhakar Ramachandran, Darcie Anderson, Zachery Colbert, Daniel Arrington, Michael Huo, Mark B Pinkham, Matthew Foote, Andrew Fielding
フォーマット:	Artigo
言語:	Inglês
出版事項:	Wolters Kluwer Medknow Publications 2025-01-01
シリーズ:	Journal of Medical Physics
主題:	computed tomography cone-beam computed tomography deep learning gamma knife pix2pix model synthetic computed tomography
オンライン･アクセス:	https://journals.lww.com/10.4103/jmp.jmp_140_24
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