Department of Radiation Oncology, Chung-Ang University Gwang Myeong Hospital, Gyeonggi-do, Republic of Korea.
Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
PLoS One. 2022 Jul 20;17(7):e0271724. doi: 10.1371/journal.pone.0271724. eCollection 2022.
While the recent advancements of computed tomography (CT) technology have contributed in reducing radiation dose and image noise, an objective evaluation of image quality in patient scans has not yet been established. In this study, we present a patient-specific CT image quality evaluation method that includes fully automated measurements of noise level, structure sharpness, and alteration of structure. This study used the CT images of 120 patients from four different CT scanners reconstructed with three types of algorithm: filtered back projection (FBP), vendor-specific iterative reconstruction (IR), and a vendor-agnostic deep learning model (DLM, ClariCT.AI, ClariPi Inc.). The structure coherence feature (SCF) was used to divide an image into the homogeneous (RH) and structure edge (RS) regions, which in turn were used to localize the regions of interests (ROIs) for subsequent analysis of image quality indices. The noise level was calculated by averaging the standard deviations from five randomly selected ROIs on RH, and the mean SCFs on RS was used to estimate the structure sharpness. The structure alteration was defined by the standard deviation ratio between RS and RH on the subtraction image between FBP and IR or DLM, in which lower structure alterations indicate successful noise reduction without degradation of structure details. The estimated structure sharpness showed a high correlation of 0.793 with manually measured edge slopes. Compared to FBP, IR and DLM showed 34.38% and 51.30% noise reduction, 2.87% and 0.59% lower structure sharpness, and 2.20% and -12.03% structure alteration, respectively, on an average. DLM showed statistically superior performance to IR in all three image quality metrics. This study is expected to contribute to enhance the CT protocol optimization process by allowing a high throughput and quantitative image quality evaluation during the introduction or adjustment of lower-dose CT protocol into routine practice.
虽然计算机断层扫描（CT）技术的最新进展有助于降低辐射剂量和图像噪声，但尚未建立患者扫描图像质量的客观评估方法。在这项研究中，我们提出了一种患者特异性 CT 图像质量评估方法，包括噪声水平、结构清晰度和结构变化的全自动测量。该研究使用了来自四个不同 CT 扫描仪的 120 名患者的 CT 图像，这些图像使用三种类型的算法重建：滤波反投影（FBP）、特定于供应商的迭代重建（IR）和供应商不可知的深度学习模型（DLM，ClariCT.AI，ClariPi Inc.）。结构相干特征（SCF）用于将图像分为均匀（RH）和结构边缘（RS）区域，然后用于定位感兴趣区域（ROI），以进一步分析图像质量指数。噪声水平通过在 RH 上随机选择五个 ROI 的标准偏差的平均值来计算，而 RS 上的平均 SCF 用于估计结构清晰度。结构变化定义为 FBP 和 IR 或 DLM 之间减法图像上 RS 和 RH 之间的标准偏差比，其中较低的结构变化表示在不降低结构细节的情况下成功降低噪声。估计的结构清晰度与手动测量的边缘斜率具有高度相关性（0.793）。与 FBP 相比，IR 和 DLM 分别显示 34.38%和 51.30%的噪声降低，2.87%和 0.59%的结构清晰度降低，2.20%和-12.03%的结构变化，平均而言。在所有三个图像质量指标中，DLM 与 IR 相比均显示出统计学上的优越性能。这项研究有望通过在常规实践中引入或调整低剂量 CT 方案时允许高通量和定量图像质量评估，从而促进 CT 方案优化过程。
