Liu Ying, Sun Minghao, Zhang Haowei, Liu Haikuan
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China.
Institute of Radiation Medicine, Fudan University, Shanghai 200032, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2025 Apr 25;42(2):263-271. doi: 10.7507/1001-5515.202312002.
In this study, we propose an automatic contour outlining method to measure the spatial resolution of homemade automatic tube current modulation (ATCM) phantom by outlining the edge contour of the phantom image, selecting the region of interest (ROI), and measuring the spatial resolution characteristics of computer tomography (CT) phantom image. Specifically, the method obtains a binarized image of the phantom outlined by an automated fast region convolutional neural network (AFRCNN) model, measures the edge spread function (ESF) of the CT phantom with different tube currents and layer thicknesses, and differentiates the ESF to obtain the line spread function (LSF). Finally, the values passing through the zeros are normalized by the Fourier transform to obtain the CT spatial resolution index (RI) for the automatic measurement of the modulation transfer function (MTF). In this study, this algorithm is compared with the algorithm that uses polymethylmethacrylate (PMMA) to measure the MTF of the phantom edges to verify the feasibility of this method, and the results show that the AFRCNN model not only improves the efficiency and accuracy of the phantom contour outlining, but also is able to obtain a more accurate spatial resolution value through automated segmentation. In summary, the algorithm proposed in this study is accurate in spatial resolution measurement of phantom images and has the potential to be widely used in real clinical CT images.
在本研究中，我们提出了一种自动轮廓勾勒方法，通过勾勒体模图像的边缘轮廓、选择感兴趣区域（ROI）以及测量计算机断层扫描（CT）体模图像的空间分辨率特征，来测量自制自动管电流调制（ATCM）体模的空间分辨率。具体而言，该方法通过自动快速区域卷积神经网络（AFRCNN）模型获取勾勒出的体模二值化图像，测量不同管电流和层厚下CT体模的边缘扩展函数（ESF），并对ESF进行微分以获得线扩展函数（LSF）。最后，通过傅里叶变换对过零点的值进行归一化，以获得用于自动测量调制传递函数（MTF）的CT空间分辨率指数（RI）。在本研究中，将该算法与使用聚甲基丙烯酸甲酯（PMMA）测量体模边缘MTF的算法进行比较，以验证该方法的可行性，结果表明AFRCNN模型不仅提高了体模轮廓勾勒的效率和准确性，还能够通过自动分割获得更准确的空间分辨率值。总之，本研究提出的算法在体模图像的空间分辨率测量中是准确的，并且有潜力广泛应用于实际临床CT图像。
