School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Med Phys. 2013 May;40(5):051916. doi: 10.1118/1.4802747.
Four-dimensional computer tomography (4D-CT) has been widely used in lung cancer radiotherapy due to its capability in providing important tumor motion information. However, the prolonged scanning duration required by 4D-CT causes considerable increase in radiation dose. To minimize the radiation-related health risk, radiation dose is often reduced at the expense of interslice spatial resolution. However, inadequate resolution in 4D-CT causes artifacts and increases uncertainty in tumor localization, which eventually results in extra damages of healthy tissues during radiotherapy. In this paper, the authors propose a novel postprocessing algorithm to enhance the resolution of lung 4D-CT data.
The authors' premise is that anatomical information missing in one phase can be recovered from the complementary information embedded in other phases. The authors employ a patch-based mechanism to propagate information across phases for the reconstruction of intermediate slices in the longitudinal direction, where resolution is normally the lowest. Specifically, the structurally matching and spatially nearby patches are combined for reconstruction of each patch. For greater sensitivity to anatomical details, the authors employ a quad-tree technique to adaptively partition the image for more fine-grained refinement. The authors further devise an iterative strategy for significant enhancement of anatomical details.
The authors evaluated their algorithm using a publicly available lung data that consist of 10 4D-CT cases. The authors' algorithm gives very promising results with significantly enhanced image structures and much less artifacts. Quantitative analysis shows that the authors' algorithm increases peak signal-to-noise ratio by 3-4 dB and the structural similarity index by 3%-5% when compared with the standard interpolation-based algorithms.
The authors have developed a new algorithm to improve the resolution of 4D-CT. It outperforms the conventional interpolation-based approaches by producing images with the markedly improved structural clarity and greatly reduced artifacts.
由于能够提供重要的肿瘤运动信息，四维计算机断层扫描（4D-CT）已广泛应用于肺癌放射治疗。然而，4D-CT 所需的长时间扫描会导致辐射剂量显著增加。为了尽量降低与辐射相关的健康风险，通常会以牺牲切片间空间分辨率为代价来降低辐射剂量。然而，4D-CT 分辨率不足会导致伪影，并增加肿瘤定位的不确定性，最终导致放射治疗过程中健康组织的额外损伤。在本文中，作者提出了一种新的后处理算法来提高肺部 4D-CT 数据的分辨率。
作者的前提是，一个相位中缺失的解剖学信息可以从其他相位中嵌入的互补信息中恢复。作者采用基于补丁的机制在纵向方向上传播信息，以重建中间切片，通常分辨率最低。具体来说，通过结构匹配和空间附近的补丁进行重建。为了提高对解剖细节的敏感性，作者采用四叉树技术自适应地分割图像，以进行更精细的细化。作者进一步设计了一种迭代策略，以显著增强解剖细节。
作者使用一个包含 10 个 4D-CT 病例的公共肺部数据集来评估他们的算法。作者的算法给出了非常有前景的结果，显著增强了图像结构，减少了伪影。定量分析表明，与基于标准插值的算法相比，作者的算法将峰值信噪比提高了 3-4dB，结构相似性指数提高了 3%-5%。
作者开发了一种新的算法来提高 4D-CT 的分辨率。与传统的基于插值的方法相比，它产生的图像具有明显改善的结构清晰度和大大减少的伪影，表现更优。
