Nabipour M, Deevband MR, Asgharzadeh Alvar A, Soleimani N, Sadeghi S. A New Method on Kerma Estimation in Mammography Screenings.
J Biomed Phys Eng 2021;
11:595-602. [PMID:
34722404 PMCID:
PMC8546156 DOI:
10.31661/jbpe.v0i0.1146]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 03/25/2019] [Indexed: 06/13/2023]
Abstract
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.
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