We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A New Method on Kerma Estimation in Mammography Screenings.
- Authors
Nabipour, Mohammad; Deevband, Mohammad Reza; Alvar, Amin Asgharzadeh; Soleimani, Narges; Sadeghi, Sara
- 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.
- Subjects
BREAST; MAMMOGRAMS; ARTIFICIAL neural networks; MONTE Carlo method; CANCER diagnosis; ALGORITHMS
- Publication
Journal of Biomedical Physics & Engineering, 2021, Vol 11, Issue 5, p595
- ISSN
2251-7200
- Publication type
Article
- DOI
10.31661/jbpe.v0i0.1146