2016 Volume 34 Issue 5 Pages 259-266
We have developed a guided image filtering (GIF) using a noise model (NM) of whole-body positron emission tomography (PET) images for cancer screening studies. The noise standard deviation in each voxel was derived from a noise image (NI). The NI was created by taking the difference of two reconstruction images created from two group data. The list-mode PET data were divided into odd and even group data. The NM was created by taking the mean of distribution expressed the relation between voxel values and noise standard deviations in the pair of the each PET image and NI for 185 samples. We compared the image quality between the Gaussian filtering (Gauss) and GIF with/without the NM using the whole-body PET image embedded an artificial hot spot into lung, kidney and bladder. We also tested the GIF with the NM using the whole-body PET image mismatching to the NM. The GIF without the NM produced lower contrast recovery coefficient than the Gauss in the case of a hot spot with 2:1 contrast inserted in the lung; however, this was prevented by GIF with the NM.
