Highlights
- •Measurement of linear accelerator spectra from experimental PDDs processed by neural networks.
- •Neural networks are applied to solve a Fredholm integral equation.
- •Neural networks are used to solve an ill conditioned system of equations.
- •Resolution of the Fredholm integral equation applied to noisy experimental data.
- •Linear accelerator spectra obtained without requiring knowledge of the accelerator design.
Abstract
Purpose Linear accelerator (linac) spectra, used to improve the accuracy of dose calculation
and to produce a complete description of beam quality, among other aspects, are relevant
in radiotherapy and linear accelerator physics.
Methods In this work we apply neural networks in solving an ill-conditioned system of linear
equations, to indirectly measure the linear accelerator spectra via the percentage
depth dose curves. The photon beam spectra are related to radiation doses through
a Fredholm integral equation. To address our problem we measured the percentage depth
dose curve in water and solved a discretized Fredholm equation using artificial neural
network. After analysing the typology of our physical problem we selected a MultiLayer
Perceptron Neural Network and designed the most suitable neural network architecture.
Results The reconstructed spectra were compared with spectra from three linacs, two of them
obtained by us through simulations and the third produced by another author, achieving
a concordance between 92 % and 96 %.
Conclusions Therefore, the spectrum of any accelerator can be quickly and easily reconstructed
from measured percent depth dose curves, applying a trained artificial neural network.
Keywords
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Article info
Publication history
Published online: March 01, 2022
Accepted:
February 20,
2022
Received in revised form:
February 14,
2022
Received:
August 2,
2021
Identification
Copyright
© 2022 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.
