Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT

Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT

Due to major artifacts and uncalibrated Hounsfield units (HU), cone-beam computed tomography (CBCT) cannot be used readily for diagnostics and therapy planning purposes. This study addresses image-to-image translation by convolutional neural networks (CNNs) to convert CBCT to CT-like scans, comparin...

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Bibliographic Details
Main Authors: Matteo Rossi, Pietro Cerveri
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Diagnostics
Subjects:
image-to-image translation
synthetic images
supervised training
unsupervised training
U-Net
cycleGAN
Online Access:https://www.mdpi.com/2075-4418/11/8/1435

Internet

https://www.mdpi.com/2075-4418/11/8/1435

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