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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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X-ray dose profiles using artificial neural networks.

Fernando Patlán-Cardoso1, Oscar Ibáñez-Orozco1, Suemi Rodríguez-Romo1

  • 1Centro de Investigaciones Teóricas, Facultad de Estudios Superiores Cuautitlán, Universidad Nacional Autónoma de México, Mexico.

Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine
|December 16, 2022
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This study presents a new Artificial Neural Network (ANN) method for simulating radiation dose profiles, offering an accurate alternative to Monte Carlo methods for radiotherapy applications.

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Artificial neural networksPercent depth doseX-rays

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Area of Science:

  • Medical Physics
  • Computational Dosimetry
  • Radiotherapy Research

Background:

  • Accurate simulation of radiation dose profiles is crucial for effective radiotherapy planning and calibration of measurement instruments.
  • Traditional Monte Carlo methods, while accurate, are computationally intensive.
  • The International Atomic Energy Agency (IAEA) Code of Practice 398 sets specific error margins for radiation dosimetry.

Purpose of the Study:

  • To introduce a novel computational method using Artificial Neural Networks (ANNs) to simulate and predict radiation dose profiles.
  • To validate the ANN method against established data and IAEA standards.
  • To provide a faster and efficient alternative to Monte Carlo simulations in radiotherapy.

Main Methods:

  • Development of a deep-learning Artificial Neural Network model.
  • Training and validation of the ANN using X-ray (6 and 15 MV) data at various depths and field sizes.
  • Comparison of ANN-predicted dose profiles with data from the British Journal of Radiology and other sources.

Main Results:

  • The ANN method accurately reproduced radiation dose profiles for 6 and 15 MV X-rays within the IAEA Code of Practice 398 error margins.
  • The method demonstrated reliability by reproducing data from multiple sources with acceptable errors.
  • Simulations showed the potential for enhancing radiotherapy techniques and instrument calibration.

Conclusions:

  • Artificial Neural Networks offer a viable and efficient alternative to Monte Carlo methods for simulating radiation dose profiles.
  • The developed ANN method can improve the accuracy and efficiency of radiotherapy dose planning.
  • This approach supports advancements in radiation oncology and the calibration of essential measurement instruments.