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A comprehensive comparative study of generative adversarial network architectures for synthetic computed tomography generation in the abdomen
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DOI：10.1002/mp.18038.png)
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Abstract
En 中文
Magnetic Resonance (MR)-based synthetic Computed Tomography (sCT) generation is an emerging promising technique, required for the transition from conventional planning workflows to MR-only radiotherapy planning. This shift aims to replace CT acquisition with a sCT improving both cost efficiency and burden to the patient. Generative Adversarial Networks (GANs) have shown some of the best performance in this area.
Keywords:
deep learning
generative adversarial networks
medical image analysis
MR-only radiotherapy
synthetic CT
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