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Hybrid µCT-FMT imaging and image analysis

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MCTP system model based on linear programming optimization of apertures obtained from sequencing patient image data

A Ureba¹, F J Salguero², A R Barbeiro³

¹Dpto. Fisiología Médica y Biofísica. Facultad de Medicina, Universidad de Sevilla, E-41009 Sevilla, Spain.

Medical Physics

|August 4, 2014

Summary

This study introduces a novel Monte Carlo treatment planning system (MC-TPS) using patient imaging data for direct multileaf collimator (MLC) aperture optimization. The system enables efficient and accurate simulation of complex radiotherapy treatments.

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Area of Science:

Medical Physics
Radiation Oncology
Computational Biology

Background:

Advanced radiotherapy requires precise dose delivery, often necessitating complex treatment planning.
Monte Carlo simulations offer high accuracy in radiation transport but can be computationally intensive.
Optimizing treatment plans efficiently is crucial for clinical implementation.

Purpose of the Study:

To present a hybrid direct multileaf collimator (MLC) aperture optimization model based on patient imaging data.
To implement this model on a Monte Carlo treatment planning system (MC-TPS) for advanced radiotherapy.
To achieve optimal results in efficient times for clinical practice.

Main Methods:

Developed the CARMEN system, a full MC-TPS controlled via MATLAB.
Utilized a novel 'biophysical' map generated from patient imaging data, replacing conventional fluence maps.
Employed a ray-casting algorithm and a custom sequencer for MLC interactions, with dose calculations using BEAMDOSE.
Simulated phase space files using EGSnrc/BEAMnrc for various linac and energy beam configurations.

Main Results:

Successfully treated three complex cases: head-and-neck, partial breast irradiation (IMRT + MERT), and prostate bed (VMAT).
Achieved required target doses and organ-at-risk constraints within clinically acceptable computation times.
Quality assurance showed high agreement between CARMEN system results and experimental measurements.

Conclusions:

Presented a Monte Carlo treatment planning model utilizing patient imaging data for aperture optimization.
The model generates deliverable apertures weighted via linear programming for modulation.
Demonstrated the model's capability to accurately and efficiently solve complex radiotherapy treatments.