一种使用变压器的更有效的CT合成器，用于锥形束CT引导的自适应放射治疗。

A more effective CT synthesizer using transformers for cone-beam CT-guided adaptive radiotherapy.

作者信息

Chen Xinyuan, Liu Yuxiang, Yang Bining, Zhu Ji, Yuan Siqi, Xie Xuejie, Liu Yueping, Dai Jianrong, Men Kuo

机构信息

National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences, Langfang, China.

出版信息

Front Oncol. 2022 Aug 25;12:988800. doi: 10.3389/fonc.2022.988800. eCollection 2022.

DOI:10.3389/fonc.2022.988800

PMID:36091131

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9454309/

Abstract

PURPOSE

The challenge of cone-beam computed tomography (CBCT) is its low image quality, which limits its application for adaptive radiotherapy (ART). Despite recent substantial improvement in CBCT imaging using the deep learning method, the image quality still needs to be improved for effective ART application. Spurred by the advantages of transformers, which employs multi-head attention mechanisms to capture long-range contextual relations between image pixels, we proposed a novel transformer-based network (called TransCBCT) to generate synthetic CT (sCT) from CBCT. This study aimed to further improve the accuracy and efficiency of ART.

MATERIALS AND METHODS

In this study, 91 patients diagnosed with prostate cancer were enrolled. We constructed a transformer-based hierarchical encoder-decoder structure with skip connection, called TransCBCT. The network also employed several convolutional layers to capture local context. The proposed TransCBCT was trained and validated on 6,144 paired CBCT/deformed CT images from 76 patients and tested on 1,026 paired images from 15 patients. The performance of the proposed TransCBCT was compared with a widely recognized style transferring deep learning method, the cycle-consistent adversarial network (CycleGAN). We evaluated the image quality and clinical value (application in auto-segmentation and dose calculation) for ART need.

RESULTS

TransCBCT had superior performance in generating sCT from CBCT. The mean absolute error of TransCBCT was 28.8 ± 16.7 HU, compared to 66.5 ± 13.2 for raw CBCT, and 34.3 ± 17.3 for CycleGAN. It can preserve the structure of raw CBCT and reduce artifacts. When applied in auto-segmentation, the Dice similarity coefficients of bladder and rectum between auto-segmentation and oncologist manual contours were 0.92 and 0.84 for TransCBCT, respectively, compared to 0.90 and 0.83 for CycleGAN. When applied in dose calculation, the gamma passing rate (1%/1 mm criterion) was 97.5% ± 1.1% for TransCBCT, compared to 96.9% ± 1.8% for CycleGAN.

CONCLUSIONS

The proposed TransCBCT can effectively generate sCT for CBCT. It has the potential to improve radiotherapy accuracy.

摘要

目的

锥束计算机断层扫描（CBCT）的挑战在于其图像质量较低，这限制了其在自适应放射治疗（ART）中的应用。尽管最近使用深度学习方法在CBCT成像方面有了显著改进，但为了有效地应用ART，图像质量仍需提高。受变压器优势的启发，其采用多头注意力机制来捕捉图像像素之间的长距离上下文关系，我们提出了一种基于变压器的新型网络（称为TransCBCT），用于从CBCT生成合成CT（sCT）。本研究旨在进一步提高ART的准确性和效率。

材料与方法

在本研究中，纳入了91例被诊断为前列腺癌的患者。我们构建了一种基于变压器的具有跳跃连接的分层编码器 - 解码器结构，称为TransCBCT。该网络还采用了几个卷积层来捕捉局部上下文。所提出的TransCBCT在来自76例患者的6144对CBCT/变形CT图像上进行训练和验证，并在来自15例患者的1026对图像上进行测试。将所提出的TransCBCT的性能与一种广泛认可的风格迁移深度学习方法——循环一致对抗网络（CycleGAN）进行比较。我们评估了用于ART需求的图像质量和临床价值（在自动分割和剂量计算中的应用）。

结果

TransCBCT在从CBCT生成sCT方面具有卓越性能。TransCBCT的平均绝对误差为28.8±16.7 HU，相比之下，原始CBCT为66.5±13.2 HU，CycleGAN为34.3±17.3 HU。它可以保留原始CBCT的结构并减少伪影。在自动分割中应用时，TransCBCT自动分割与肿瘤学家手动轮廓之间膀胱和直肠的骰子相似系数分别为0.92和0.84，而CycleGAN分别为0.90和0.83。在剂量计算中应用时，TransCBCT的伽马通过率（1%/1 mm标准）为97.5%±1.1%，而CycleGAN为96.9%±1.8%。