A method for determining spatial resolution of phantom based on automatic contour delineation.
10.7507/1001-5515.202312002
- Author:
Ying LIU
1
;
Minghao SUN
1
;
Haowei ZHANG
1
;
Haikuan LIU
2
Author Information
1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China.
2. Institute of Radiation Medicine, Fudan University, Shanghai 200032, P. R. China.
- Publication Type:Journal Article
- Keywords:
Automatic contour delineation;
Modulation transfer function;
Self-made automatic tube current modulation phantom;
Spatial resolution
- MeSH:
Phantoms, Imaging;
Tomography, X-Ray Computed/instrumentation*;
Algorithms;
Neural Networks, Computer;
Image Processing, Computer-Assisted/methods*;
Humans;
Polymethyl Methacrylate
- From:
Journal of Biomedical Engineering
2025;42(2):263-271
- CountryChina
- Language:Chinese
-
Abstract:
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.
