Hierarchical Patch-Based Sparse Representation—A New Approach for Resolution Enhancement of 4D-CT Lung Data
Hierarchical Patch-Based Sparse Representation—A New Approach for Resolution Enhancement of 4D-CT Lung Data
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DOI：
10.1109/tmi.2012.2202245
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发表时间：
2012-06
影响因子：
10.6
通讯作者：
Yu Zhang;Guorong Wu;P. Yap;Qianjin Feng;Jun Lian;Wufan Chen;D. Shen
中科院分区：
文献类型：
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作者：
Yu Zhang;Guorong Wu;P. Yap;Qianjin Feng;Jun Lian;Wufan Chen;D. Shen
四维计算机断层扫描（4D-CT）在肺癌治疗中发挥着重要作用，因为它能够为高精度放射治疗提供呼吸运动的全面表征。然而，由于与CT相关联的固有高剂量曝光，沿上下方向的密集采样沿着通常是不实际的，因此导致切片间厚度比平面内体素分辨率大得多。因此，在4D-CT图像中经常观察到诸如肺血管不连续和部分容积效应的伪影，这可能误导放射治疗中的剂量管理。本文提出了一种新的基于分块的4D-CT图像沿着上下方向分辨率增强技术。我们的工作前提是，在一个特定阶段丢失的解剖信息可以从其他阶段恢复。基于这一假设，我们采用了一个分层的补丁为基础的稀疏表示机制，以提高4D-CT的上下分辨率重建额外的中间CT切片。具体来说，对于我们打算重建的中间CT切片上的每个空间位置，我们首先从4D-CT中的所有其他相位的图像中聚集补丁的字典。然后，我们采用稀疏组合的补丁从这个字典，从相邻的（上和下）切片的指导下，重建一系列的补丁，我们逐步完善的分层方式重建最终的中间切片显着增强的解剖细节。我们的方法使用公共数据集进行了广泛的评估。在所有的实验中，我们的方法优于传统的线性和三次样条插值方法在保留图像的细节，也在抑制误导性的伪影，表明我们提出的方法可以潜在地应用于更好的图像引导放射治疗肺癌在未来。
Four-dimensional computed tomography (4D-CT) plays an important role in lung cancer treatment because of its capability in providing a comprehensive characterization of respiratory motion for high-precision radiation therapy. However, due to the inherent high-dose exposure associated with CT, dense sampling along superior–inferior direction is often not practical, thus resulting in an inter-slice thickness that is much greater than in-plane voxel resolutions. As a consequence, artifacts such as lung vessel discontinuity and partial volume effects are often observed in 4D-CT images, which may mislead dose administration in radiation therapy. In this paper, we present a novel patch-based technique for resolution enhancement of 4D-CT images along the superior–inferior direction. Our working premise is that anatomical information that is missing in one particular phase can be recovered from other phases. Based on this assumption, we employ a hierarchical patch-based sparse representation mechanism to enhance the superior–inferior resolution of 4D-CT by reconstructing additional intermediate CT slices. Specifically, for each spatial location on an intermediate CT slice that we intend to reconstruct, we first agglomerate a dictionary of patches from images of all other phases in the 4D-CT. We then employ a sparse combination of patches from this dictionary, with guidance from neighboring (upper and lower) slices, to reconstruct a series of patches, which we progressively refine in a hierarchical fashion to reconstruct the final intermediate slices with significantly enhanced anatomical details. Our method was extensively evaluated using a public dataset. In all experiments, our method outperforms the conventional linear and cubic-spline interpolation methods in preserving image details and also in suppressing misleading artifacts, indicating that our proposed method can potentially be applied to better image-guided radiation therapy of lung cancer in the future.