A method for determining spatial resolution of phantom based on automatic contour delineation_Journal of Biomedical Engineering

Authors：

LIU Ying ¹ , SUN Minghao ¹ ,  ZHANG Haowei ¹ ,  LIU Haikuan ²

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;

Corresponding author：

ZHANG Haowei, Email: howiezh@aliyun.com; LIU Haikuan, Email: liuhk@fudan.edu.cn

Keywords：

Self-made automatic tube current modulation phantom; Automatic contour delineation; Spatial resolution; Modulation transfer function

DOI：

10.7507/1001-5515.202312002

Video：

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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.

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14.	Yang Y, Zhuo W, Chen B, et al. A new phantom developed to test the ATCM performance of chest CT scanners. Journal of Radiological Protection, 2021, 41: 349-359.
15.	王新怡. 基于深度特征的无监督图像检索. 北京: 北京邮电大学, 2020.
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17.	Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
18.	Li C, Kao C Y, Gore J C, et al. Minimization of Region-Scalable Fitting Energy for Image Segmentation. IEEE Transactions on Image Processing, 2008, 17(10): 1940-1949.
19.	井长兴. 基于深度学习的人脸检测与人脸关键点定位算法研究. 杭州: 中国计量大学, 2018.
20.	刘颖, 郭伊云, 陈静聪, 等. 基于Faster-RCNN和Level-Set的桥小脑角区肿瘤自动精准分割. 波谱学杂志, 2021, 38(3): 381-391.
21.	Fang Q, Boas D A. Tetrahedral mesh generation from volumetric binary and grayscale images. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009: 1142-1145.
22.	Sanders J W, Hurwitz L, Samei E. Patient-specific quantification of image quality: an automated method for measuring spatial resolution in clinical CT images. Medical Physics, 2016, 43(10): 5330.
23.	Samei E, Flynn M J, Reimann D A. A method for measuring the presampled MTF of digital radiographic systems using an edge test device. Medical Physics, 1998, 25(1): 102-113.
24.	Maidment A D, Albert M. Conditioning data for calculation of the modulation transfer function. Medical Physics, 2003, 30(2): 248-253.
25.	Anam C, Haryanto F, Widita R, et al. The impact of patient table on size-specific dose estimate (SSDE). Australasian Physical & Engineering Sciences in Medicine, 2017, 40(1): 153-158.
26.	Narvaez M, Graffigna J P, Gómez M E, et al. Application of oversampling to obtain the MTF of digital radiology equipment. Journal of Physics: Conference Series, 2016, 705(1): 012057.
27.	Li T, Feng H. Comparison of different analytical edge spread functionmodels for MTF calculation using curve-fitting//Proceedings of the International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, DOI: 10.1117/12.832793.

1. Christianson O, Winslow J, Frush D P, et al. Automated technique to measure noise in clinical CT examinations. American Journal of Roentgenology, 2015, 205(1): W93-W99.
2. Mccollough C H, Yu L, Kofler J M, et al. Degradation of CT low-contrast spatial resolution due to the use of iterative reconstruction and reduced dose levels. Radiology, 2015, 276(2): 499-506.
3. Newman D L, Dougherty G, Al obaid A, et al. Limitations of clinical CT in assessing cortical thickness and density. Phys Med Biol, 1998, 43(3): 619-626.
4. Hussain F A, Mail N, Shamy A M A, et al. A qualitative and quantitative analysis of radiation dose and image quality of computed tomography images using adaptive statistical iterative reconstruction. Journal of Applied Clinical Medical Physics, 2016, 17(3): 419-432.
5. Judy P F. The line spread function and modulation transfer function of a computed tomographic scanner. Medical Physics, 1976, 3(4): 233-236.
6. Kayugawa A, Ohkubo M, Wada S. Accurate determination of CT point-spread-function with high precision. Journal of Applied Clinical Medical Physics, 2013, 14(4): 216-226.
7. Padgett R, Kotre C J. Development and application of programs to measure modulation transfer function, noise power spectrum and detective quantum efficiency. Radiation Protection Dosimetry, 2005, 117(1-3): 283-287.
8. Droege R T, Morin R L. A practical method to measure the MTF of CT scanners. Medical Physics, 1982, 9(5): 758-760.
9. Garayoa J, Castro P. A study on image quality provided by a kilovoltage cone‐beam computed tomography. Journal of Applied Clinical Medical Physics, 2013, 14: 239-257.
10. Yin F F, Giger M L, Doi K. Measurement of the presampling modulation transfer function of film digitizers using a curve fitting technique. Medical Physics, 1990, 17(6): 962-966.
11. Boone J M, Seibert J A. An analytical edge spread function model for computer fitting and subsequent calculation of the LSF and MTF. Medical Physics, 1994, 21(10): 1541-1545.
12. Schneiders N J, Bushong S C. Computer assisted MTF determination in CT. Medical Physics, 1980, 7(1): 76-78.
13. Friedman S N, Fung G S K, Siewerdsen J H, et al. A simple approach to measure computed tomography (CT) modulation transfer function (MTF) and noise-power spectrum (NPS) using the American College of Radiology (ACR) accreditation phantom. Medical Physics, 2013, 40(5): 051907.
14. Yang Y, Zhuo W, Chen B, et al. A new phantom developed to test the ATCM performance of chest CT scanners. Journal of Radiological Protection, 2021, 41: 349-359.
15. 王新怡. 基于深度特征的无监督图像检索. 北京: 北京邮电大学, 2020.
16. 龙程. 基于对抗网络的图像数据集扩充研究与实现. 西安: 西安理工大学, 2020.
17. Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
18. Li C, Kao C Y, Gore J C, et al. Minimization of Region-Scalable Fitting Energy for Image Segmentation. IEEE Transactions on Image Processing, 2008, 17(10): 1940-1949.
19. 井长兴. 基于深度学习的人脸检测与人脸关键点定位算法研究. 杭州: 中国计量大学, 2018.
20. 刘颖, 郭伊云, 陈静聪, 等. 基于Faster-RCNN和Level-Set的桥小脑角区肿瘤自动精准分割. 波谱学杂志, 2021, 38(3): 381-391.
21. Fang Q, Boas D A. Tetrahedral mesh generation from volumetric binary and grayscale images. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009: 1142-1145.
22. Sanders J W, Hurwitz L, Samei E. Patient-specific quantification of image quality: an automated method for measuring spatial resolution in clinical CT images. Medical Physics, 2016, 43(10): 5330.
23. Samei E, Flynn M J, Reimann D A. A method for measuring the presampled MTF of digital radiographic systems using an edge test device. Medical Physics, 1998, 25(1): 102-113.
24. Maidment A D, Albert M. Conditioning data for calculation of the modulation transfer function. Medical Physics, 2003, 30(2): 248-253.
25. Anam C, Haryanto F, Widita R, et al. The impact of patient table on size-specific dose estimate (SSDE). Australasian Physical & Engineering Sciences in Medicine, 2017, 40(1): 153-158.
26. Narvaez M, Graffigna J P, Gómez M E, et al. Application of oversampling to obtain the MTF of digital radiology equipment. Journal of Physics: Conference Series, 2016, 705(1): 012057.
27. Li T, Feng H. Comparison of different analytical edge spread functionmodels for MTF calculation using curve-fitting//Proceedings of the International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, DOI: 10.1117/12.832793.

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