Denoising portal images by minimizing the SURE estimator on a parameterized family of shrinkage functions

The number of verification portal images in radiotherapy has increased in the last years. On the other hand, radiation delivered during imaging is not confined to the treatment volumes, but also affects the surrounding organs and tissues. In order to reduce the overall radiation dose due to imaging, one approach would be to reduce the dose per image, but noise would increase and the quality of portal images would reduce. The limited quality of portal images makes it difficult to propose a reduction of dose if there is no way to effectively reduce noise. Denoising algorithms could be the solution if the quality of the restored image can match the image obtained with a standard dose. In this work the statistical properties of noise in a portal imaging system and the statistical properties of portal images are used to develop an efficient denoising method. The result is a method that minimizes the Stein's unbiased risk estimator (SURE) in the image domain over a parametric family of shrinkage functions operating in the wavelet domain. The presented denoising method shows a better performance than the adaptive Wiener estimator for different portal images and noise energies.