期刊论文

Bai, PG

英文

Scientific Reports

2045-2322

2023

13

1

6624

10.1038/s41598-023-33472-w

Joint Funds for the innovation of science and Technology, Fujian province [2021Y9190]; Science and Technology Project of Fujian Province [2021Y0052, 2022Y0056]; Startup Fund for scientific research, Fujian Medical University [2020QH1223]; Fujian Provincial Health Technology Project [2021CXB013]; Fujian Provincial Clinlical Research Center for Cancer Radiotherapy and Immunotherapy [2020Y2012]

本校为其他机构

This study aims to utilize a hybrid approach of phantom correction and deep learning for synthesized CT (sCT) images generation based on cone-beam CT (CBCT) images for nasopharyngeal carcinoma (NPC). 52 CBCT/CT paired images of NPC patients were used for model training (41), validation (11). Hounsfield Units (HU) of the CBCT images was calibrated by a commercially available CIRS phantom. Then the original CBCT and the corrected CBCT (CBCT_cor) were trained separately with the same cycle generative adversarial network (CycleGAN) to generate SCT1 and SCT2. The mean error and mean absolute error ...