基于图割的肺4D-CT图像超分辨率重建

肺4D-CT在当今的肺癌放射治疗中起着重要的作用。本文提出了一种基于全局图割方法的肺4D-CT图像超分辨率重建 方法，来提升肺4D-CT图像的质量。该方法首先在最大后验马尔科夫随机场框架下建立一个肺4D-CT各相位高分辨率图像重 建的全局能量函数，然后，将该能量函数转化成图的表达方式，最后用图割方法和α-β swap算法优化能量函数来恢复高分辨率 图像细节结构。实验结果表明，在恢复图像的细节方面，本文方法要优于传统的线性插值和凸集投影超分辨率重建算法。

Super-resolution reconstruction for 4-dimensional computed tomography of the lung using graph cuts

Four-dimensional computer tomography (4D-CT) has a great value in lung cancer radiotherapy for its capability in providing lung information with respiratory motion. We employed a global graph cuts super-resolution (SR) reconstruction method to reconstruct high-resolution lung 4D-CT images. First, the high-resolution images reconstruction energy function was built based on a Maximum a posteriori Markov Random Field (MAP-MRF) formulation. The energy function was then transformed to a graph formulation, which was solved using graph cut algorithm. All the evaluation results showed that this approach outperformed the line interpolation and projection onto convex sets (POCS) approach with an improved structural clarity.