Nabipour, Mohammad, Deevband, Mohammad Reza, Asgharzadeh Alvar, Amin, Soleimani, Narges, Sadeghi, Sara. (1400). A New Method on Kerma Estimation in Mammography Screenings. سامانه مدیریت نشریات علمی, 11(5), 595-602. doi: 10.31661/jbpe.v0i0.1146
Mohammad Nabipour; Mohammad Reza Deevband; Amin Asgharzadeh Alvar; Narges Soleimani; Sara Sadeghi. "A New Method on Kerma Estimation in Mammography Screenings". سامانه مدیریت نشریات علمی, 11, 5, 1400, 595-602. doi: 10.31661/jbpe.v0i0.1146
Nabipour, Mohammad, Deevband, Mohammad Reza, Asgharzadeh Alvar, Amin, Soleimani, Narges, Sadeghi, Sara. (1400). 'A New Method on Kerma Estimation in Mammography Screenings', سامانه مدیریت نشریات علمی, 11(5), pp. 595-602. doi: 10.31661/jbpe.v0i0.1146
Nabipour, Mohammad, Deevband, Mohammad Reza, Asgharzadeh Alvar, Amin, Soleimani, Narges, Sadeghi, Sara. A New Method on Kerma Estimation in Mammography Screenings. سامانه مدیریت نشریات علمی, 1400; 11(5): 595-602. doi: 10.31661/jbpe.v0i0.1146
A New Method on Kerma Estimation in Mammography Screenings
1MSc, Department of Medical Physics and Biomedical Engineering, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2PhD, Department of Medical Physics and Biomedical Engineering, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3PhD candidate, Department of Medical Physics and Biomedical Engineering, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4MD, Faculty of Medicine, Golestan University of Medical Sciences, Gorgan, Iran
5MD, Faculty of Medicine, Islamic Azad University, Tehran Medical Branch, Tehran, Iran
چکیده
Background: Given the extensive use and preferred diagnostic method in common mammography tests for screening and diagnosis of breast cancer, there is concern about the increased dose absorbed by the patient due to the sensitivity of the breast tissue. Objective: This study aims to evaluate the entrance surface air kerma (ESAK) before irradiation to the patient through its estimation. Material and Methods: In this descriptive paper, firstly, a phantom was used to measure some data, including ESAK, Kvp, mAs, HVL, and type of filter/target. Secondly, the MultiLayer Perceptron (MLP) neural network model was trained with Levenberg-Marquardt (LM) backpropagation training algorithm and finally, ESAK was estimated. Results: Based on results obtained from the program in different neuron numbers, it was found that the number of 35 neurons is the most optimal value, offering a regression coefficient of 95.7%. The Mean Squared Error (MSE) for all data was 0.437 mGy and accounting for 4.8% of the output range changes, predicting 95.2% accuracy in the present research. Conclusion: Using neural networks in ESAK prediction, the method proposed in the present research leads to the possible ESAK estimation of patients before X-Ray. The results suggested that the regression coefficient represented 4.3% difference between the kerma measured by solid-state dosimeter in the radiation field and the value predicted in the research. In comparison with the Monte-Carlo simulation method, this method has better accuracy.
National Cancer Institutes 2018. [Accessed August 21, 2018]. Available from: https://www.cancer.gov/.
Tabar L, Vitak B, Chen TH, Yen AM, Cohen A, Tot T, et al. Swedish two-county trial: impact of mammographic screening on breast cancer mortality during 3 decades. Radiology. 2011;260:658-63. doi: 10.1148/radiol.11110469. PubMed PMID: 21712474.
NZ health statistics [Internet]. New Zealand National Screening Unit Website 2018. [Accessed August 21, 2018]. Available from: https://www.health.govt.nz/nz-health-statistics.
Dance DR. Monte Carlo calculation of conversion factors for the estimation of mean glandular breast dose. Phys Med Biol. 1990;35:1211-9.doi: 10.1088/0031-9155/35/9/002. PubMed PMID: 2236205.
Sobol WT, Wu X. Parametrization of mammography normalized average glandular dose tables. Med Phys. 1997;24:547-54. doi: 10.1118/1.597937. PubMed PMID: 9127307.
Nigapruke K, Puwanich P, Phaisangittisakul N, Youngdee W. Monte Carlo simulation of average glandular dose and an investigation of influencing factors. J Radiat Res. 2010;51:441-8. doi: 10.1269/jrr.10008 . PubMed PMID: 20523013.
Ko K, Park S, Lee J. Assessment of patient close in mammography using Monte Carlo simulation. J Nucl Sci Technol. 2004;41:215-8.
Mohammadi A, Faghihi R, Mehdizadeh S, Hadad K. Total absorbed dose of critical organs in mammography, assessment and comparison of Monte-Carlo method and TLD. Biomed Tech. 2005;50:393-4.
Ceke D, Kunosic S, Kopric M, Lincender L. Using neural network algorithms in prediction of mean glandular dose based on the measurable parameters in mammography. Acta Informatica Medica. 2009;17:194.
Mohammadyari P, Faghihi R, Mosleh-Shirazi MA, Lotfi M, Hematiyan MR, Koontz C, et al. Calculation of dose distribution in compressible breast tissues using finite element modeling, Monte Carlo simulation and thermoluminescence dosimeters. Phys Med Biol. 2015;60:9185-202. doi: 10.1088/0031-9155/60/23/9185.
Highnam R [Internet]. Patient-Specific Radiation Dose Estimation in Breast Cancer Screening Keeping Patients Safe and Informed 2018. [Accessed 21 August 2018]. Available from: https://www.volparasolutions.com/assets/Uploads/VolparaDose-White-Paper.pdf.
Ariga E, Ito S, Deji S, Saze T, Nishizawa K. Determination of half value layers of X-ray equipment using computed radiography imaging plates. Phys Med. 2012;28:71-5. doi: 10.1016/j.ejmp.2011.01.001.
Haykin S. Neural networks: a comprehensive foundation. United States: Prentice Hall PTR; 1994.
Alvar AA, Deevband MR, Ashtiyani M. Neutron spectrum unfolding using radial basis function neural networks. Appl Radiat Isot. 2017;129:35-41. doi: 10.1016/J.APRADISO.2017.07.048.
Anderson JA. An introduction to neural networks. Germany: MIT press; 1995.
Hagan MT, Menhaj MB. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks. 1994;5:989-93. doi: 10.1109/72.329697.
Hagan MT, Demuth HB, Beale MH, De Jess O. Neural network design (2nd Edition). Martin Hagan; 2014.
Iyer MS, Rhinehart RR. A method to determine the required number of neural-network training repetitions. IEEE Transactions on Neural Networks. 1999;10:427-32. doi: 10.1109/72.750573.
Fukumizu K, Amari S. Local minima and plateaus in multilayer neural networks. 1999 Ninth International Conference on Artificial Neural Networks ICANN 99 (Conf. Publ. No. 470), Edinburgh, UK: IET; 1999. doi: 10.1049/cp:19991175.
Hamm L, Brorsen BW, Hagan MT. Comparison of stochastic global optimization methods to estimate neural network weights. Neural Process Lett. 2007;26:145-58. doi: 10.1007/s11063-007-9048-7.