Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, Heilongjiang, China.
School of Automation, Harbin University of Science and Technology, Harbin, Heilongjiang, China.
Med Phys. 2022 Aug;49(8):5317-5329. doi: 10.1002/mp.15684. Epub 2022 May 9.
Cone-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.
The 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.
The 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.
We 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.
锥形束 CT（CBCT）在放射治疗中具有重要作用，但大量伪影限制了其应用。本研究旨在使用 respath-cycleGAN 从 CBCT 合成类似于计划 CT（pCT）的 CT（sCT），以用于未来的临床实践。
该方法将 respath 概念集成到原始 cycleGAN 中，称为 respath-cycleGAN，以将 CBCT 映射到 pCT。使用 30 名患者进行训练，15 名患者进行测试。
计算平均绝对误差（MAE）、均方根误差（RMSE）、峰值信噪比（PSNR）、结构相似性指数（SSIM）和空间非均匀性（SNU），以评估从 CBCT 生成的 sCT 的质量。与 CBCT 图像相比，MAE 从 197.72 降低到 140.7，RMSE 从 339.17 降低到 266.51，PSNR 从 22.07 增加到 24.44，而 SSIM 从 0.948 增加到 0.964。从视觉和定量两方面来看，带有 respath 的 sCT 优于不带 respath 的 sCT。我们还在骨盆数据集上对头颈部（H&N）模型进行了泛化测试，结果再次表明我们的模型更优。
我们开发了一种 respath-cycleGAN 方法，可从 CBCT 合成高质量的 CT。在未来的临床实践中，该方法可用于制定放射治疗计划。
