Abstract: Lung 4D-CT is of great value in the application of cancer treatment, because it provides comprehensive image guidance for radiation therapy. However, the high resolution lung 4D-CT data along the Z axis can’t be obtained due to the dose limitation. Concern this problem, a multi-phase similarity based non-local means super-resolution reconstruction for lung 4D-CT is proposed in this paper. The redundant information from multi-phases of 4D-CT data are used to recover some of this high frequency information by using a patch-based reconstruction in combination with a global constraint. A public dataset provided by DIR-lab is used to evaluate the proposed method. The dataset consists of 10 groups of lung 4D-CT data and each group contains 10 phases. All the evaluation results show that the approach outperforms traditional linear-interpolation and the convex set projection approach（POCS） in terms of quantitative measures and visual observation.

Key words: lung 4D-CT, non-local means, super-resolution reconstruction

摘要： 肺4D-CT为放射治疗提供全面的图像引导，在当今肺癌治疗中起着重要的作用。然而，由于采集时间和人体所能承受辐射剂量的限制，无法得到高分辨率（[Z]轴方向）CT图像，因此，通常采集到的肺4D-CT图像[Z]轴方向细节部分往往不够清晰。针对这一问题，提出了一个基于多相位相似性的非局部均值超分辨率重建方法，来提升肺4D-CT图像的质量。该方法利用多相位图像之间的互补信息，以非局部均值滤波为基础，来恢复图像的高分辨率细节结构；另外，由于呼吸运动，肺4D-CT图像中不同相位图像肺区域的灰度会有所差异，为保证重建高分辨率图像的灰度不变性，引入全局约束以修正重建图像的整体灰度。采用一套公共肺4D-CT数据集评估提出的方法，该数据集由10组肺4D-CT数据组成，每组数据包含10个相位。实验表明，在恢复图像的细节和增强分辨率方面，该方法要优于传统的线性插值和凸集投影（Projection Onto Convex Set，POCS）超分辨率重建算法。

关键词: 肺4D-CT, 非局部均值, 超分辨率重建