Department of Radiology and BRIC, University of North Carolina, Chapel Hill, North Carolina 27599.
Med Phys. 2013 Dec;40(12):121717. doi: 10.1118/1.4829501.
4D-CT typically delivers more accurate information about anatomical structures in the lung, over 3D-CT, due to its ability to capture visual information of the lung motion across different respiratory phases. This helps to better determine the dose during radiation therapy for lung cancer. However, a critical concern with 4D-CT that substantially compromises this advantage is the low superior-inferior resolution due to less number of acquired slices, in order to control the CT radiation dose. To address this limitation, the authors propose an approach to reconstruct missing intermediate slices, so as to improve the superior-inferior resolution.
In this method the authors exploit the observation that sampling information across respiratory phases in 4D-CT can be complimentary due to lung motion. The authors' approach uses this locally complimentary information across phases in a patch-based sparse-representation framework. Moreover, unlike some recent approaches that treat local patches independently, the authors' approach employs the group-sparsity framework that imposes neighborhood and similarity constraints between patches. This helps in mitigating the trade-off between noise robustness and structure preservation, which is an important consideration in resolution enhancement. The authors discuss the regularizing ability of group-sparsity, which helps in reducing the effect of noise and enables better structural localization and enhancement.
The authors perform extensive experiments on the publicly available DIR-Lab Lung 4D-CT dataset [R. Castillo, E. Castillo, R. Guerra, V. Johnson, T. McPhail, A. Garg, and T. Guerrero, "A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets," Phys. Med. Biol. 54, 1849-1870 (2009)]. First, the authors carry out empirical parametric analysis of some important parameters in their approach. The authors then demonstrate, qualitatively as well as quantitatively, the ability of their approach to achieve more accurate and better localized results over bicubic interpolation as well as a related state-of-the-art approach. The authors also show results on some datasets with tumor, to further emphasize the clinical importance of their method.
The authors have proposed to improve the superior-inferior resolution of 4D-CT by estimating intermediate slices. The authors' approach exploits neighboring constraints in the group-sparsity framework, toward the goal of achieving better localization and noise robustness. The authors' results are encouraging, and positively demonstrate the role of group-sparsity for 4D-CT resolution enhancement.
4D-CT 通常比 3D-CT 能提供更准确的肺部解剖结构信息，因为它能够捕捉到不同呼吸相之间的肺部运动的视觉信息。这有助于更好地确定肺癌放射治疗期间的剂量。然而，4D-CT 的一个关键问题是，由于获取的切片数量较少，从而控制 CT 辐射剂量，导致其上下分辨率较低，这极大地削弱了这一优势。为了解决这一限制，作者提出了一种重建缺失中间切片的方法，以提高上下分辨率。
在该方法中，作者利用了这样一种观察结果，即在 4D-CT 中，由于肺部运动，跨呼吸相的采样信息可以互补。作者的方法使用基于补丁的稀疏表示框架，在跨相位的局部互补信息。此外，与一些最近的独立处理局部补丁的方法不同，作者的方法采用了群组稀疏性框架，该框架在补丁之间施加了邻域和相似性约束。这有助于缓解噪声稳健性和结构保持之间的权衡，这在分辨率增强中是一个重要的考虑因素。作者讨论了群组稀疏性的正则化能力，这有助于降低噪声的影响，并实现更好的结构定位和增强。
作者在公开的 DIR-Lab 肺部 4D-CT 数据集[R.Castillo、E.Castillo、R.Guerra、V.Johnson、T.McPhail、A.Garg 和 T.Guerrero，“使用大型地标点集评估变形图像配准空间准确性的框架”，物理医学与生物学，第 54 卷，第 1849-1870 页（2009 年）]上进行了广泛的实验。首先，作者对方法中的一些重要参数进行了经验参数分析。然后，作者从定性和定量两个方面，证明了他们的方法能够比双三次插值以及相关的最先进的方法实现更准确和更好的局部化结果。作者还展示了一些带有肿瘤的数据集的结果，以进一步强调他们的方法的临床重要性。
作者提出了通过估计中间切片来提高 4D-CT 的上下分辨率。作者的方法利用了群组稀疏性框架中的邻域约束，以实现更好的定位和噪声稳健性。作者的结果令人鼓舞，积极证明了群组稀疏性在 4D-CT 分辨率增强中的作用。
