New software solution for TPS quality control

Authors
P.E. Leni (1), R. Gschwind (1), L. Makovicka (1)
(1) UMR CNRS 6249 Chrono-Environnement, Montbéliard, France

Introduction: In radiation therapy, one of the most time consuming step is due to the Treatment Planning System (TPS) and quality control algorithms for the dose computations. Using Artificial Neural Network (ANN), it is possible to compute the doses inside a phantom resulting from ionizing radiations [1][2]. 

Material and methods: More precisely, once the ANN is trained, it can compute a dose for every voxels of the phantom according to the beam characteristics. The main advantage is the computation time: the time-consuming step is the training of the ANN, that has to be performed only once and offline. The execution time, i.e. the dose associated to a voxel, is very low. We present an evolution of the platform presented in [3], to take into account various field sizes and shapes, and also consider different orientation of the beam to the phantom.
To validate the ANN computations, we compare them with Monte Carlo simulations. Homogeneous phantoms are used to validate interpolations on various densities and depth. Then, realistic phantoms are created to validate the dose distribution computed for a square beam in complex areas (head and neck, lungs, prostatic area). Finally, IMRT treatment simulations are validated.

Results: We compare the results obtained using our approach, the Eclipse TPS (AAA), and Monte Carlo simulations for a 10cm x 10cm photon beam on phantoms extracted from clinical data. The same procedure is applied to validate IMRT treatments. To compare the dose distributions, 3D gamma indices are computed, as well as profile extractions. For IMRT treatments, the dose distribution is computed in less than one minute on a traditional computer.

Conclusions: We have demonstrated the performance of our dose distribution engine for IMRT. Our goal is to provide a fast and accurate solution for TPS quality control. Therefore, we are currently working on the development of arctherapy treatment simulations.

References:
[1]	R.MATHIEU, E. MARTIN, L.MAKOVICKA, R.GSCHWIND, S.CONTASSOT-VIVIER, J.BAHI. Calculations of dose distributions using a neural network model, Physics in Medecine and Biology 50, p.1019-1028 (2005)
[2]	A.VASSEUR, L.MAKOVICKA, E.MARTIN, M.SAUGET, S.CONTASSOT-VIVIER, J.BAHI. Dose   calculations using artificial neural networks: a feasibility study for photon beams;  NIM.  B, Vol.266, p.1085-1093 (2008)
[3] Sauget, M., Laurent, R., Henriet, J., Salomon, M., Gschwind, R., Contassot-Vivier, S., ... & Soussen, C. Efficient domain decomposition for a neural network learning algorithm, used for the dose evaluation in external radiotherapy. In Artificial Neural Networks–ICANN 2010 (pp. 261-266). Springer Berlin Heidelberg. (2010)