Lung four dimensional computed tomography (4D-CT) can lead to accurate radiotherapy. However, for the safety of patients, the scan spacing of 4D-CT cannot be too small so that the inter-slice resolution of lung 4D-CT is low, and thus the coronal and sagittal images need to be interpolated to obtain high-resolution images. This paper presents a super-resolution reconstruction technique based on multi-model Gaussian process regression. We use the high-resolution transversal images and the corresponding low-resolution images as the training sets. The high-resolution pixels of the coronal and sagittal images can be predicted by constructing multiple Gaussian process regression models. The experimental results show that our method is superior to bicubic algorithm, projections onto convex sets, sparse coding, multi-phase similarity based method and Gaussian process regression method based on self-learning block in terms of the edge and detail recovery. The results demonstrate that the proposed method can effectively improve the quality of lung 4D-CT images, and potentially be applied to better image-guided radiation therapy of lung cancer.
肺部四维计算机断层扫描（4D-CT）能引导精确放疗，然而出于对患者安全性的考虑，4D-CT 扫描间距不能太小，以至于图像的上下层间分辨率过低，因此图像冠、矢状面需要插值才能得到高分辨率图像。本文提出了一种基于多模型高斯过程回归的超分辨率重建技术，该方法利用高分辨率的横截面及对应的低分辨率图像作为训练集，通过构造多个高斯过程回归模型，预测出冠、矢状面的高分辨率像素点。实验结果表明，本文方法在边缘及细节的恢复上都优于双三次插值、凸集投影算法、稀疏表达方法、多相位相似的方法和基于自学习分块的高斯过程回归方法。研究结果表明，本文方法能有效提高肺部 4D-CT 图像的质量，对实现肺部肿瘤精确的个体化放疗有积极意义。.
Keywords: Gaussian process regression; lung four dimensional computed tomography; super-resolution reconstruction.