Spatial Denoising Methods for Low Count Functional Images

Portable functional imaging devices can be used in oncological surgeries to locate residual tumors for better patient recovery and survival. Taking the patient dose and the limited time of surgery into account, the count in such images could be very low. In this study, we investigate effectiveness of different spatial denoising methods, such as Gaussian filtering, bilateral filtering, Rudin-Osher and Fatemin (ROF) denoising, and nonlocal means filtering, on low count functional images. We also propose a new denoising method based on maximum a posteriori (MAP) criterion. The simulation study shows that the simple methods, such as Gaussian and bilateral filtering, may be as effective as the advanced searching or iterative methods as measured by the relative root mean square error when the count is low. Further investigations using more realistic simulations or real functional images and tumor detection performance are needed to evaluate these methods at high noise levels.