一种基于变压器的双域网络，用于从放射治疗中截断的扇形束 CT 投影中重建视场扩展的锥束 CT 图像。
A transformer-based dual-domain network for reconstructing FOV extended cone-beam CT images from truncated sinograms in radiation therapy.
机构信息
School of Computer Science and Engineering, Southeast University, Nanjing, China; The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China.
The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China.
出版信息
Comput Methods Programs Biomed. 2023 Nov;241:107767. doi: 10.1016/j.cmpb.2023.107767. Epub 2023 Aug 16.
BACKGROUND AND OBJECTIVE
Cone-beam computed tomography (CBCT) is widely used in clinical radiotherapy, but its small field of view (sFOV) limits its application potential. In this study, a transformer-based dual-domain network (dual_swin), which combined image domain restoration and sinogram domain restoration, was proposed for the reconstruction of complete CBCT images with extended FOV from truncated sinograms.
METHODS
The planning CT images with large FOV (LFOV) of 330 patients who received radiation therapy were collected. The synthetic CBCT (sCBCT) images with LFOV were generated from CT images by the trained cycleGAN network, and CBCT images with sFOV were obtained through forward projection, projection truncation, and filtered back projection (FBP), comprising the training and test data. The proposed dual_swin includes sinogram domain restoration, image domain restoration, and FBP layer, and the swin transformer blocks were used as the basic feature extraction module in the network to improve the global feature extraction ability. The proposed dual_swin was compared with the image domain method, the sinogram domain method, the U-Net based dual domain network (dual_Unet), and the traditional iterative reconstruction method based on prior image and conjugate gradient least-squares (CGLS) in the test of sCBCT images and clinical CBCT images. The HU accuracy and body contour accuracy of the predicted images by each method were evaluated.
RESULTS
The images generated using the CGLS method were fuzzy and obtained the lowest structural similarity (SSIM) among all methods in the test of sCBCT and clinical CBCT images. The predicted images by the image domain methods are quite different from the ground truth and have low accuracy on HU value and body contour. In comparison with image domain methods, sinogram domain methods improved the accuracy of HU value and body contour but introduced secondary artifacts and distorted bone tissue. The proposed dual_swin achieved the highest HU and contour accuracy with mean absolute error (MAE) of 23.0 HU, SSIM of 95.7%, dice similarity coefficient (DSC) of 99.6%, and Hausdorff distance (HD) of 4.1 mm in the test of sCBCT images. In the test of clinical patients, images that were predicted by dual_swin yielded MAE, SSIM, DSC, and HD of 38.2 HU, 91.7%, 99.0%, and 5.4 mm, respectively. The predicted images by the proposed dual_swin has significantly higher accuracy than the other methods (P < 0.05).
CONCLUSIONS
The proposed dual_swin can accurately reconstruct FOV extended CBCT images from the truncated sinogram to improve the application potential of CBCT images in radiotherapy.
背景与目的
锥形束计算机断层扫描（CBCT）广泛应用于临床放射治疗，但小视野（sFOV）限制了其应用潜力。在这项研究中，我们提出了一种基于变压器的双域网络（dual_swin），该网络结合了图像域恢复和正弦图域恢复，用于从截断的正弦图重建具有扩展 FOV 的完整 CBCT 图像。
方法
收集了 330 名接受放射治疗的患者的大视野（LFOV）计划 CT 图像。通过训练好的CycleGAN 网络，从 CT 图像生成 LFOV 的合成 CBCT（sCBCT）图像，并通过正向投影、投影截断和滤波反投影（FBP）获得 sFOV 的 CBCT 图像，这些图像构成了训练和测试数据。所提出的 dual_swin 包括正弦图域恢复、图像域恢复和 FBP 层，并且 swin 变压器块被用作网络中的基本特征提取模块，以提高全局特征提取能力。在 sCBCT 图像和临床 CBCT 图像的测试中，将所提出的 dual_swin 与图像域方法、正弦图域方法、基于 U-Net 的双域网络（dual_Unet）和基于先验图像和共轭梯度最小二乘（CGLS）的传统迭代重建方法进行了比较。评估了每种方法预测图像的 HU 准确性和体轮廓准确性。
结果
CGLS 方法生成的图像模糊，在 sCBCT 和临床 CBCT 图像的测试中，所有方法中结构相似性（SSIM）最低。图像域方法生成的预测图像与真实图像有很大差异，HU 值和体轮廓的准确性较低。与图像域方法相比，正弦图域方法提高了 HU 值和体轮廓的准确性，但引入了二次伪影并扭曲了骨组织。所提出的 dual_swin 在 sCBCT 图像的测试中实现了最高的 HU 和轮廓准确性，平均绝对误差（MAE）为 23.0 HU，SSIM 为 95.7%，骰子相似系数（DSC）为 99.6%，Hausdorff 距离（HD）为 4.1 mm。在临床患者的测试中，dual_swin 预测的图像的 MAE、SSIM、DSC 和 HD 分别为 38.2 HU、91.7%、99.0%和 5.4 mm。与其他方法相比，所提出的 dual_swin 预测的图像具有更高的准确性（P<0.05）。
结论
所提出的 dual_swin 可以从截断的正弦图准确重建扩展 FOV 的 CBCT 图像，从而提高 CBCT 图像在放射治疗中的应用潜力。
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