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Generation and Evaluation of Synthetic Computed Tomography (CT) from Cone-Beam CT (CBCT) by Incorporating Feature-Driven Loss into Intensity-Based Loss Functions in Deep Convolutional Neural Network

Authors
 Sang Kyun Yoo  ;  Hojin Kim  ;  Byoung Su Choi  ;  Inkyung Park  ;  Jin Sung Kim 
Citation
 CANCERS, Vol.14(18) : 4534, 2022-09 
Journal Title
CANCERS
Issue Date
2022-09
Keywords
SSIM loss ; cone-beam computed tomography (CBCT) ; convolutional neural network (CNN) ; feature mapping ratio (FMR) ; perceptual loss ; synthetic computed tomography (CT).
Abstract
Deep convolutional neural network (CNN) helped enhance image quality of cone-beam computed tomography (CBCT) by generating synthetic CT. Most of the previous works, however, trained network by intensity-based loss functions, possibly undermining to promote image feature similarity. The verifications were not sufficient to demonstrate clinical applicability, either. This work investigated the effect of variable loss functions combining feature- and intensity-driven losses in synthetic CT generation, followed by strengthening the verification of generated images in both image similarity and dosimetry accuracy. The proposed strategy highlighted the feature-driven quantification in (1) training the network by perceptual loss, besides L1 and structural similarity (SSIM) losses regarding anatomical similarity, and (2) evaluating image similarity by feature mapping ratio (FMR), besides conventional metrics. In addition, the synthetic CT images were assessed in terms of dose calculating accuracy by a commercial Monte-Carlo algorithm. The network was trained with 50 paired CBCT-CT scans acquired at the same CT simulator and treatment unit to constrain environmental factors any other than loss functions. For 10 independent cases, incorporating perceptual loss into L1 and SSIM losses outperformed the other combinations, which enhanced FMR of image similarity by 10%, and the dose calculating accuracy by 1-2% of gamma passing rate in 1%/1mm criterion.
Files in This Item:
T202205314.pdf Download
DOI
10.3390/cancers14184534
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiation Oncology (방사선종양학교실) > 1. Journal Papers
Yonsei Authors
Kim, Jinsung(김진성) ORCID logo https://orcid.org/0000-0003-1415-6471
Kim, Hojin(김호진) ORCID logo https://orcid.org/0000-0002-4652-8682
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/191991
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