Image Quality Improvement for Chest Four-Dimensional Cone-Beam Computed Tomography by Cycle-Generative Adversarial Network
Image Quality Improvement for Chest Four-Dimensional Cone-Beam Computed Tomography by Cycle-Generative Adversarial Network
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通过循环生成对抗网络提高胸部四维锥形束计算机断层扫描的图像质量
DOI：
10.11409/mit.40.37
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Hiroyuki DAIDA
中科院分区：
文献类型：
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作者：
Keisuke USUI;Koichi OGAWA;Masami GOTO;Yasuaki SAKANO;Shinsuke KYOGOKU;Hiroyuki DAIDA
四维锥束计算机断层扫描（4D-CBCT）可以可视化肿瘤的移动，因此基于4D-CBCT的适应性放射治疗（ART）可以提高放射治疗的质量。本研究的目的是利用循环生成对抗网络（Cycle-GAN）提高4D-CBCT图像的质量，并通过定量指标评估这些图像。本研究使用20例患者未配对的胸部4D-CBCT图像和4D-MSCT图像进行训练，并在另外10例患者中测试了质量改善的4D-CBCT （sCT）图像的合成。计算平均误差（ME）和平均绝对误差（MAE）来评估CT数偏差，使用峰值信噪比（PSNR）和结构相似指数（SSIM）来评估图像相似度。我们的循环gan模型生成的sCT图像有效地减少了4D-CBCT图像上的伪影。肺区ME和MAE分别为46.5和61.9，而软组织和骨区CT计数恢复不足。sCT图像的SSIM和PSNR结果均有明显改善。所提出的Cycle-GAN方法生成的sCT图像质量接近4D-MSCT图像，特别是在肺部区域；然而，软组织和骨骼的解剖区域仍需要进一步改进。
Four-dimensional cone-beam computed tomography (4D-CBCT) can visualize moving tumors, thus the 4D-CBCT-based adaptive radiation therapy (ART) may improve the quality of radiation therapy. The aim of this study is to improve the quality of 4D-CBCT images using cycle-generative adversarial network (Cycle-GAN) and evaluate these images by a quantitative index. In this study, unpaired thoracic 4D-CBCT images and four-dimensional multislice computed tomography (4D-MSCT) images in 20 patients were used for training, and synthesis of 4D-CBCT (sCT) images with improved quality was tested in another 10 patients. The mean error (ME) and mean absolute errors (MAE) were calculated to assess CT number deviation, and peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) were used to evaluate image similarity. The sCT image generated by our Cycle-GAN model effectively reduced artifacts on 4D-CBCT image. The ME and MAE were 46.5 and 61.9 in lung regions, whereas soft tissue and bone regions insufficiently restored CT number. Results of the SSIM and PSNR were significantly improved in the sCT image. The proposed Cycle-GAN method generates sCT images with a quality close to 4D-MSCT image, particularly in the lung region; however, anatomical regions with soft tissue and bone still require further improvement.