AbstractID: 7786 Title: 3-D anisotropic diffusion and wavelet filtering of Monte Carlo dose distribution The Monte Carlo transport is the most accurate method for radiotherapy dose calculations; however, within limited time, Monte Carlo calculation introduces statistical noise into the result. In our work, two- and three-dimensional post-processing denoising methods were studied to reduce the statistical fluctuation of the Monte Carlo dose distributions. Wavelet denoising method, anisotropic diffusion method and pixel-wise adaptive Wiener method were investigated and compared. The MCNP4C Monte Carlo code with a special lattice geometry patch was used to generate dose distributions with different level of statistical noise in homogeneous and inhomogeneous phantom. All the denoising results were compared with the results obtained with a large number of simulated histories (typically 10 9). The mean square error method was used to evaluate the difference between the distributions. Extensive studies were performed to investigate importance and sensitivity of the results to the input parameters of the investigated filters. It is shown in our work that both anisotropic diffusion and wavelet denoising method can reduce the statistical noise significantly while well preserving important gradients of the dose distribution. The adaptive Wiener method was found to be inferior to the other two methods. The anisotropic diffusion method worked better than the wavelet method in most of the investigated cases and is considered preferable method for noise reduction in Monte Carlo calculated dose distributions.