Multileaf collimators are used on linear accelerators to provide conformal shaping of radiotherapy treatment beams, being an important tool for radiation therapy dose delivery. In this work, a multileaf collimator has been designed and implemented in the MCNP model of an Elekta Precise Linear Accelerator (LINAC) and introduced in PLUNC, a set of software tools for radiotherapy treatment planning (RTP) which was coupled in previous works with MCNP5 (Monte Carlo N-Particle transport code), with the purpose of comparing its effect on deterministic and Monte Carlo dose calculations. A 3D Shepp-Logan phantom was utilized as the patient model for validation purposes.Once the multileaf collimator model is implemented in the PLUNC LINAC model, a series of Matlab interfaces extract phantom and beam information created with PLUNC during the treatment plan and write it in MCNP5 input deck format. After the Monte Carlo simulation is performed, results are input back again in PLUNC in order to continue with the plan evaluation.The comparison is made via mapping of dose distribution inside the phantom with different field sizes, utilizing the MCNP5 tool FMESH, superimposed mesh tally, which allows registering the results over the problem geometry. This work follows a valid methodology for multileaf LINAC MC calculations during radiation treatment plans.

MC treatment planning techniques provide a very accurate dose calculation compared to 'conventional' deterministic treatment planning systems. In the present work, PLanUNC (PLUNC), a set of software tools for radiotherapy treatment planning (RTP), is compared with MCNP5 (Monte Carlo N-Particle transport code) by calculating dose maps inside the RANDO ® phantom, utilized as the patient model, irradiated with different field sizes with the MultiLeaf Collimated (MLC) Linear Accelerator (LinAc) Elekta Precise. PLUNC was initially coupled with MCNP5 and so exactly the same patient and plan parameters can be utilized in both dose calculation processes. A MLC Linear Accelerator was commissioned for PLUNC and a MCNP5 model used in the calculations. The coupling of MCNP5 with PLUNC has been achieved via a series of Matlab interfaces, which extract patient and beam information created with PLUNC during the treatment plan and write it in MCNP5 input deck format. A set of Computer Tomography images of the RANDO ® phantom was obtained and formatted. The CT slices are input in PLUNC, which performs the segmentation by defining anatomical structures. The Matlab algorithm developed by the authors, validated in previous works writes the phantom information in MCNP5 input deck format. Both calculations result in mapping of dose distribution inside the phantom. MCNP5 utilizes the FMESH tool, superimposed mesh tally, which allows registering the results over the problem geometry. Resulting dose maps are compared.

The main motivation that has led to the increasingly rapid development of techniques for prevention and treatment of cancer in the last decade has been its presence in the lists of main causes of death: more than 10 million diagnoses every year at a global scale and more than 160,000 in Spain. In this context, the clinical implementation of Radiotherapy Treatment Planning Systems (RTPS) has played a crucial role.