Linac photon spectra reconstruction using a depth dose gradient TSVD methodology based on Monte Carlo simulation

Megavoltage photon beams are widely used for radiation therapy treatments, and the precise knowledge of their spectral distribution is important for accurate dose calculations. There are several methods that can offer reasonable estimations of linac photon spectra based on measured depth dose distributions in a water tank. However, this reconstruction problem is an inverse radiation transport function which is poorly conditioned and its solution may become unstable due to small perturbations in the input data. We present here a novel and more stable method which can be used for photon spectral reconstruction without any prior knowledge of spectral distribution. This technique involves measuring the depth dose curve in a water phantom and applying an unfolding method using Monte Carlo simulated depth dose gradient curves for consecutives mono-energetic beams. It is shown that the relative errors in dose calculations, using the spectra reconstructed via this method, are significantly smaller than those obtained via the traditional reconstruction algorithms. These results suggest that this gradient algorithm could be useful in linac photon spectra routines calibration.