|Table of Contents|

Generating synthetic CT in megavoltage CT image-guided adaptive radiotherapy(PDF)

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

Issue:
2024年第7期
Page:
813-820
Research Field:
医学放射物理
Publishing date:

Info

Title:
Generating synthetic CT in megavoltage CT image-guided adaptive radiotherapy
Author(s):
CHEN Yuting1;  2;  ZHOU Feiyu1;  2;  ZHANG Fuli2;  JIANG Huayong2;  CHEN Diandian2;  GAO Yanxiang2;  YU Yanjun2;  LE Xiaoyun1;  LU Na2
1. School of Physics, Beihang University, Beijing 100191, China 2. Department of Radiotherapy, the 7th Medical Center, Department of Oncology, Chinese PLA General Hospital, Beijing 100700, China
Keywords:
Keywords: cyclic generative adversarial network megavoltage computed tomography synthetic computed tomography image-guided radiotherapy image quality
PACS:
R737.33
DOI:
DOI:10.3969/j.issn.1005-202X.2024.07.005
Abstract:
Abstract: Objective To propose a deep learning neural network approach for transforming megavoltage computed tomography (MVCT) images of cervical cancer into pseudo kilovoltage computed tomography (kVCT) images with high signal-to-noise ratio and contrast-to-noise ratio, thus providing three-dimensional anatomical images and localization information required for adaptive radiotherapy of cervical cancer, and guiding the accelerator to achieve precise treatment. Methods The MVCT and kVCT images of 54 patients treated with cervical cancer radiotherapy were collected, with 44 cases randomly selected as the training set, and the remaining 10 cases as the test set. A cyclic generative adversarial network with gating mechanism and multi-channel data input was used to synthesize pseudo-kVCT images from MVCT images. The network training results were evaluated with imaging quality evaluation parameters, such as mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Results The MAE, PSNR, and SSIM of MVCT images vs pseudo-kVCT (5:5) images were (24.9±0.7) HU vs (17.8±0.3) HU, (29.8±0.2) dB vs (30.7±0.2) dB, and 0.841±0.007 vs 0.898±0.003, respectively. Conclusion The generated pseudo-kVCT images have advantages in noise reduction and contrast enhancement, and can reduce the need for additional MV-kVCT electron density calibration in dose calculations. The dose calculation ability of pseudo-kVCT is comparable to that of MVCT, providing a possibility for the application of pseudo-kVCT images in image-guided adaptive radiotherapy.

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Last Update: 2024-07-12