Tsuji Takumasa, Yoshida Soichiro, Hommyo Mitsuki, Oyama Asuka, Kumagai Shinobu, Shiraishi Kenshiro, Kotoku Jun'ichi
Graduate School of Medical Care and Technology, Teikyo University, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605, Japan.
Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan.
J Imaging Inform Med. 2024 Dec 4. doi: 10.1007/s10278-024-01346-w.
Cone beam computed tomography (CBCT) images are convenient representations for obtaining information about patients' internal organs, but their lower image quality than those of treatment planning CT images constitutes an important shortcoming. Several proposed CBCT image-quality improvement methods based on deep learning require large amounts of training data. Our newly developed model using a super-resolution method, "one-shot" super-resolution (OSSR) based on the "zero-shot" super-resolution method, requires only small amounts of training data to improve CBCT image quality using only the target CBCT image and the paired treatment planning CT image. For this study, pelvic CBCT images and treatment planning CT images of 30 prostate cancer patients were used. We calculated the root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) to evaluate image-quality improvement and normalized mutual information (NMI) as a quantitative evaluation of positional accuracy. Our proposed method can improve CBCT image quality without requiring large amounts of training data. After applying our proposed method, the resulting RMSE, PSNR, SSIM, and NMI between the CBCT images and the treatment planning CT images were as much as 0.86, 1.05, 1.03, and 1.31 times better than those obtained without using our proposed method. By comparison, CycleGAN exhibited values of 0.91, 1.03, 1.02, and 1.16. The proposed method achieved performance equivalent to that of CycleGAN, which requires images from approximately 30 patients for training. Findings demonstrated improvement of CBCT image quality using only the target CBCT images and the paired treatment planning CT images.
锥束计算机断层扫描（CBCT）图像是获取患者内部器官信息的便捷表示方式，但其图像质量低于治疗计划CT图像，这是一个重要的缺点。几种基于深度学习提出的CBCT图像质量改进方法需要大量的训练数据。我们新开发的使用超分辨率方法的模型，即基于“零样本”超分辨率方法的“单样本”超分辨率（OSSR），仅需少量训练数据，仅使用目标CBCT图像和配对的治疗计划CT图像就能提高CBCT图像质量。在本研究中，使用了30例前列腺癌患者的盆腔CBCT图像和治疗计划CT图像。我们计算了均方根误差（RMSE）、峰值信噪比（PSNR）和结构相似性（SSIM）来评估图像质量的改进，并计算了归一化互信息（NMI）作为位置准确性的定量评估。我们提出的方法无需大量训练数据就能提高CBCT图像质量。应用我们提出的方法后，CBCT图像与治疗计划CT图像之间的RMSE、PSNR、SSIM和NMI分别比未使用我们提出的方法时提高了0.86倍、1.05倍、1.03倍和1.31倍。相比之下，循环生成对抗网络（CycleGAN）的相应值分别为0.91、1.03、1.02和1.16。所提出的方法取得了与CycleGAN相当的性能，而CycleGAN需要大约30例患者的图像进行训练。研究结果表明，仅使用目标CBCT图像和配对的治疗计划CT图像就能提高CBCT图像质量。
