Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Semarang, Central Java, Indonesia.
Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan.
J Appl Clin Med Phys. 2022 Sep;23(9):e13719. doi: 10.1002/acm2.13719. Epub 2022 Jul 9.
We have developed a software to automatically find the contrast-detail (C-D) curve based on the statistical low-contrast detectability (LCD) in images of computed tomography (CT) phantoms at multiple cell sizes and to generate minimum detectable contrast (MDC) characteristics.
A simple graphical user interface was developed to set the initial parameters needed to create multiple grid region of interest of various cell sizes with a 2-pixel increment. For each cell in the grid, the average CT number was calculated to obtain the standard deviation (SD). Detectability was then calculated by multiplying the SD of the mean CT numbers by 3.29. This process was automatically repeated as many times as the cell size was set at initialization. Based on the obtained LCD, the C-D curve was obtained and the target size at an MDC of 0.6% (i.e., 6-HU difference) was determined. We subsequently investigated the consistency of the target sizes for a 0.6% MDC at four locations within the homogeneous image. We applied the software to images with six noise levels, images of two modules of the American College of Radiology CT phantom, images of four different phantoms, and images of four different CT scanners. We compared the target sizes at a 0.6% MDC based on the statistical LCD and the results from a human observer.
The developed system was able to measure C-D curves from different phantoms and scanners. We found that the C-D curves follow a power-law fit. We found that higher noise levels resulted in a higher MDC for a target of the same size. The low-contrast module image had a slightly higher MDC than the distance module image. The minimum size of an object detected by visual observation was slightly larger than the size using statistical LCD.
The statistical LCD measurement method can generate a C-D curve automatically, quickly, and objectively.
我们开发了一种软件，能够根据 CT 体模图像中多个细胞大小的统计低对比度检测（LCD）自动找到对比度-细节（C-D）曲线，并生成最小可检测对比度（MDC）特性。
开发了一个简单的图形用户界面，用于设置创建具有不同细胞大小的多个网格感兴趣区域的初始参数，步长为 2 像素。对于网格中的每个细胞，计算平均 CT 数以获得标准差（SD）。然后通过将平均 CT 数的 SD 乘以 3.29 来计算检测率。初始化时设置的细胞大小重复此过程的次数越多。基于获得的 LCD，得到 C-D 曲线，并确定 MDC 为 0.6%（即 6-HU 差异）的目标大小。随后，我们在均匀图像的四个位置研究了 MDC 为 0.6%的目标大小的一致性。我们将该软件应用于具有六个噪声水平的图像、美国放射学院 CT 体模的两个模块的图像、四个不同体模的图像以及四个不同 CT 扫描仪的图像。我们比较了基于统计 LCD 和人类观察者结果的 MDC 为 0.6%的目标大小。
所开发的系统能够测量来自不同体模和扫描仪的 C-D 曲线。我们发现 C-D 曲线遵循幂律拟合。我们发现，对于相同大小的目标，较高的噪声水平会导致更高的 MDC。低对比度模块图像的 MDC 略高于距离模块图像。通过视觉观察检测到的最小物体尺寸略大于使用统计 LCD 的尺寸。
统计 LCD 测量方法可以快速、客观地自动生成 C-D 曲线。
