Anam Choirul, Naufal Ariij, Sutanto Heri, Fujibuchi Toshioh, Dougherty Geoff
Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia.
Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan.
Biomed Phys Eng Express. 2024 May 22;10(4). doi: 10.1088/2057-1976/ad4b20.
. To develop a method to extract statistical low-contrast detectability (LCD) and contrast-detail (C-D) curves from clinical patient images.. We used the region of air surrounding the patient as an alternative for a homogeneous region within a patient. A simple graphical user interface (GUI) was created to set the initial configuration for region of interest (ROI), ROI size, and minimum detectable contrast (MDC). The process was started by segmenting the air surrounding the patient with a threshold between -980 HU (Hounsfield units) and -1024 HU to get an air mask. The mask was trimmed using the patient center coordinates to avoid distortion from the patient table. It was used to automatically place square ROIs of a predetermined size. The mean pixel values in HU within each ROI were calculated, and the standard deviation (SD) from all the means was obtained. The MDC for a particular target size was generated by multiplying the SD by 3.29. A C-D curve was obtained by iterating this process for the other ROI sizes. This method was applied to the homogeneous area from the uniformity module of an ACR CT phantom to find the correlation between the parameters inside and outside the phantom, for 30 thoracic, 26 abdominal, and 23 head images.. The phantom images showed a significant linear correlation between the LCDs obtained from outside and inside the phantom, with Rvalues of 0.67 and 0.99 for variations in tube currents and tube voltages. This indicated that the air region outside the phantom can act as a surrogate for the homogenous region inside the phantom to obtain the LCD and C-D curves.. The C-D curves obtained from outside the ACR CT phantom show a strong linear correlation with those from inside the phantom. The proposed method can also be used to extract the LCD from patient images by using the region of air outside as a surrogate for a region inside the patient.
开发一种从临床患者图像中提取统计低对比度可探测性（LCD）和对比度细节（C-D）曲线的方法。我们使用患者周围的空气区域作为患者体内均匀区域的替代物。创建了一个简单的图形用户界面（GUI）来设置感兴趣区域（ROI）、ROI大小和最小可探测对比度（MDC）的初始配置。通过在-980 HU（亨氏单位）和-1024 HU之间设置阈值分割患者周围的空气以获得空气掩码来启动该过程。使用患者中心坐标修剪掩码以避免患者检查床造成的失真。它用于自动放置预定大小的方形ROI。计算每个ROI内以HU为单位的平均像素值，并获得所有平均值的标准差（SD）。通过将SD乘以3.29生成特定目标大小的MDC。通过对其他ROI大小重复此过程获得C-D曲线。将该方法应用于ACR CT体模均匀性模块的均匀区域，以寻找体模内外参数之间的相关性，用于30例胸部、26例腹部和23例头部图像。体模图像显示从体模外部和内部获得的LCD之间存在显著的线性相关性，管电流和管电压变化时的R值分别为0.67和0.99。这表明体模外部的空气区域可作为体模内部均匀区域的替代物来获得LCD和C-D曲线。从ACR CT体模外部获得的C-D曲线与从体模内部获得的曲线显示出很强的线性相关性。所提出的方法还可用于通过使用患者外部的空气区域作为患者内部区域的替代物从患者图像中提取LCD。
