锥形束CT生成伪CT的深度学习方法题录

目的针对前列腺癌放疗, 研究锥形束CT(CBCT)生成伪CT的深度学习方法, 以满足自适应放疗的需要。方法纳入瓦里安On-Board Imager采集的74例前列腺癌患者的CBCT图像及其模拟定位CT图像, 并使用MIM软件进行形变配准。将数据按简单随机法分为训练集(59例)和测试集(15例)。使用U-net、Pix2PixGAN和CycleGAN学习CBCT到模拟定位CT的映射。以形变配准后CT作为参考图像, 评价平均绝对误差(MAE)、结构相似指数(SSIM)和峰值信噪比(PSNR)。另外单独分析了图像质量, 包括软组织分辨率、图像噪声和伪影等。结果使用U-net、Pix2PixGAN和CycleGAN生成图像的MAE分别为(29.4±16.1)、(37.1±14.4)、(34.3±17.3)HU。在图像质量方面, U-net和Pix2PixGAN生成的图像存在过度模糊的问题, 导致了图像失真;而CycleGAN生成的图像保留了CBCT图像结构且改善了图像质量。结论 CycleGAN能有效地提高CBCT图像质量, 有更大的潜力应用于自适应放疗中。

A deep learning method for generating pseudo-CT by cone beam CT in radiotherapy

Objective To investigate the pseudo-CT generation from cone beam CT (CBCT) by a deep learning method for the clinical need of adaptive radiotherapy. Methods CBCT data from 74 prostate cancer patients collected by Varian On-Board Imager and their simulated positioning CT images were used for this study. The deformable registration was implemented by MIM software. And the data were randomly divided into the training set (n=59) and test set (n=15). U-net, Pix2PixGAN and CycleGAN were employed to learn the mapping from CBCT to simulated positioning CT. The evaluation indexes included mean absolute error (MAE), structural similarity index (SSIM) and peak signal to noise ratio (PSNR), with the deformed CT chosen as the reference. In addition, the quality of image was analyzed separately, including soft tissue resolution, image noise and artifacts, etc. Results The MAE of images generated by U-net, Pix2PixGAN and CycleGAN were (29.4±16.1) HU, (37.1±14.4) HU and (34.3±17.3) HU, respectively. In terms of image quality, the images generated by U-net and Pix2PixGAN had excessive blur, resulting in image distortion; while the images generated by CycleGAN retained the CBCT image structure and improved the image quality. Conclusion CycleGAN is able to effectively improve the quality of CBCT images, and has potential to be used in adaptive radiotherapy.