Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, South Korea.
Med Phys. 2013 Nov;40(11):111915. doi: 10.1118/1.4825096.
Various scanning methods and image reconstruction algorithms are actively investigated for low-dose computed tomography (CT) that can potentially reduce a health-risk related to radiation dose. Particularly, compressive-sensing (CS) based algorithms have been successfully developed for reconstructing images from sparsely sampled data. Although these algorithms have shown promises in low-dose CT, it has not been studied how sparse sampling schemes affect image quality in CS-based image reconstruction. In this work, the authors present several sparse-sampling schemes for low-dose CT, quantitatively analyze their data property, and compare effects of the sampling schemes on the image quality.
Data properties of several sampling schemes are analyzed with respect to the CS-based image reconstruction using two measures: sampling density and data incoherence. The authors present five different sparse sampling schemes, and simulated those schemes to achieve a targeted dose reduction. Dose reduction factors of about 75% and 87.5%, compared to a conventional scan, were tested. A fully sampled circular cone-beam CT data set was used as a reference, and sparse sampling has been realized numerically based on the CBCT data.
It is found that both sampling density and data incoherence affect the image quality in the CS-based reconstruction. Among the sampling schemes the authors investigated, the sparse-view, many-view undersampling (MVUS)-fine, and MVUS-moving cases have shown promising results. These sampling schemes produced images with similar image quality compared to the reference image and their structure similarity index values were higher than 0.92 in the mouse head scan with 75% dose reduction.
The authors found that in CS-based image reconstructions both sampling density and data incoherence affect the image quality, and suggest that a sampling scheme should be devised and optimized by use of these indicators. With this strategic approach, one can acquire optimally sampled sparse data so that the CS-based algorithms can best perform in terms of image quality.
各种扫描方法和图像重建算法正在积极研究中，以实现潜在的低剂量计算机断层扫描（CT），从而降低与辐射剂量相关的健康风险。特别是，基于压缩感知（CS）的算法已成功用于从稀疏采样数据中重建图像。尽管这些算法在低剂量 CT 中表现出了潜力，但它们对 CS 图像重建中稀疏采样方案如何影响图像质量的研究还很少。在这项工作中，作者提出了几种用于低剂量 CT 的稀疏采样方案，定量分析了它们的数据特性，并比较了采样方案对图像质量的影响。
使用两种度量标准（采样密度和数据不相似度），分析了几种采样方案在基于 CS 的图像重建中的数据特性。作者提出了五种不同的稀疏采样方案，并模拟了这些方案以实现目标剂量降低。与常规扫描相比，剂量降低因子约为 75%和 87.5%。使用完全采样的锥形束 CT 数据集作为参考，并基于 CBCT 数据进行数值稀疏采样。
发现采样密度和数据不相似度都影响基于 CS 的重建中的图像质量。在所研究的采样方案中，稀疏视图、多视图欠采样（MVUS）-精细和 MVUS-移动方案显示出了有前途的结果。这些采样方案生成的图像与参考图像的图像质量相似，其结构相似度指数值在 75%剂量降低的小鼠头部扫描中高于 0.92。
作者发现，在基于 CS 的图像重建中，采样密度和数据不相似度都会影响图像质量，并建议通过使用这些指标来设计和优化采样方案。通过这种策略方法，可以获取最佳采样的稀疏数据，从而使 CS 算法能够在图像质量方面表现最佳。
