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

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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Bibliographic Details
Main Authors: Prabhakar Ramachandran, Darcie Anderson, Zachery Colbert, Daniel Arrington, Michael Huo, Mark B Pinkham, Matthew Foote, Andrew Fielding
Format: Article
Language:English
Published: Wolters Kluwer Medknow Publications 2025-01-01
Series:Journal of Medical Physics
Subjects:
computed tomography
cone-beam computed tomography
deep learning
gamma knife
pix2pix model
synthetic computed tomography
Online Access:https://journals.lww.com/10.4103/jmp.jmp_140_24
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