Oyama Asuka, Kumagai Shinobu, Arai Norikazu, Takata Takeshi, Saikawa Yusuke, Shiraishi Kenshiro, Kobayashi Takenori, Kotoku Jun'ichi
Graduate School of Medical Care and Technology, Teikyo University, 2-11-1 Kaga, Itabashi-ku, Tokyo, Japan.
Central Radiology Division, Teikyo University Hospital, 2-11-1 Kaga, Itabashi-ku, Tokyo, Japan.
J Radiat Res. 2018 Jul 1;59(4):501-510. doi: 10.1093/jrr/rry019.
This study was conducted to improve cone-beam computed tomography (CBCT) image quality using the super-resolution technique, a method of inferring a high-resolution image from a low-resolution image. This technique is used with two matrices, so-called dictionaries, constructed respectively from high-resolution and low-resolution image bases. For this study, a CBCT image, as a low-resolution image, is represented as a linear combination of atoms, the image bases in the low-resolution dictionary. The corresponding super-resolution image was inferred by multiplying the coefficients and the high-resolution dictionary atoms extracted from planning CT images. To evaluate the proposed method, we computed the root mean square error (RMSE) and structural similarity (SSIM). The resulting RMSE and SSIM between the super-resolution images and the planning CT images were, respectively, as much as 0.81 and 1.29 times better than those obtained without using the super-resolution technique. We used super-resolution technique to improve the CBCT image quality.
本研究旨在利用超分辨率技术提高锥束计算机断层扫描（CBCT）图像质量，超分辨率技术是一种从低分辨率图像推断高分辨率图像的方法。该技术与两个分别由高分辨率和低分辨率图像库构建的矩阵（即所谓的字典）一起使用。在本研究中，作为低分辨率图像的CBCT图像表示为低分辨率字典中的图像库原子的线性组合。通过将从计划CT图像中提取的系数与高分辨率字典原子相乘，推断出相应的超分辨率图像。为了评估所提出的方法，我们计算了均方根误差（RMSE）和结构相似性（SSIM）。超分辨率图像与计划CT图像之间的RMSE和SSIM结果分别比不使用超分辨率技术时获得的结果好0.81倍和1.29倍。我们使用超分辨率技术来提高CBCT图像质量。
