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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Medical Physicsarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Medical Physics
Article . 2022 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
Medical Physics
Article . 2022
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Synthetic CT generation based on CBCT using respath‐cycleGAN

Authors: Liwei, Deng; Jie, Hu; Jing, Wang; Sijuan, Huang; Xin, Yang;

Synthetic CT generation based on CBCT using respath‐cycleGAN

Abstract

AbstractPurposeCone‐beam computed tomography (CBCT) plays an important role in radiotherapy, but the presence of a large number of artifacts limits its application. The purpose of this study was to use respath‐cycleGAN to synthesize CT (sCT) similar to planning CT (pCT) from CBCT for future clinical practice.MethodsThe method integrates the respath concept into the original cycleGAN, called respath‐cycleGAN, to map CBCT to pCT. Thirty patients were used for training and 15 for testing.ResultsThe mean absolute error (MAE), root mean square error (RMSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and spatial nonuniformity (SNU) were calculated to assess the quality of sCT generated from CBCT. Compared with CBCT images, the MAE improved from 197.72 to 140.7, RMSE from 339.17 to 266.51, and PSNR from 22.07 to 24.44, while SSIM increased from 0.948 to 0.964. Both visually and quantitatively, sCT with respath is superior to sCT without respath. We also performed a generalization test of the head‐and‐neck (H&N) model on a pelvic data set. The results again showed that our model was superior.ConclusionWe developed a respath‐cycleGAN method to synthesize CT with good quality from CBCT. In future clinical practice, this method may be used to develop radiotherapy plans.

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Keywords

Radiotherapy Planning, Computer-Assisted, Image Processing, Computer-Assisted, Humans, Radiotherapy Dosage, Spiral Cone-Beam Computed Tomography, Cone-Beam Computed Tomography, Signal-To-Noise Ratio, Artifacts

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
23
Top 10%
Top 10%
Top 10%
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