Synthetic CT Generation of the Pelvis in Patients With Cervical Cancer: A Single Input Approach Using Generative Adversarial Network

Synthetic CT Generation of the Pelvis in Patients With Cervical Cancer: A Single Input Approach Using Generative Adversarial Network

Multi-modality imaging constitutes a foundation of precision medicine, especially in oncology where reliable and rapid imaging techniques are needed in order to insure adequate diagnosis and treatment. In cervical cancer, precision oncology requires the acquisition of <sup>18</sup>F-labe...

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Bibliographic Details
Main Authors: Atallah Baydoun, Ke Xu, Jin Uk Heo, Huan Yang, Feifei Zhou, Latoya A. Bethell, Elisha T. Fredman, Rodney J. Ellis, Tarun K. Podder, Melanie S. Traughber, Raj M. Paspulati, Pengjiang Qian, Bryan J. Traughber, Raymond F. Muzic
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Cervical cancer
computed tomography
deep learning
generative adversarial network
magnetic resonance imaging
U-Net
Online Access:https://ieeexplore.ieee.org/document/9316666/

Internet

https://ieeexplore.ieee.org/document/9316666/

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