Noise reduction in PET sinograms using non-local total variation regularization

Positron Emission Tomography is a technique of molecular imaging and provides information about biochemical process within the body of a patient, it is employed for diagnosis, staggering, and treatment planning. However, the resulting images have high noise levels that may cause difficulties for reading and interpreting the images by medical staff. For this reason, it is necessary to perform a denoising step to achieve better signal to noise ratio. In this paper, an approach is presented to denoise Positron Emission Tomography sinogram images using non-local total variation in the sinogram domain. The images are modeled in the sinogram domain using a Poisson noise model, it is proposed to adapt the SPIRAL algorithm to approximate the objective function to be minimized with separable quadratic functions to include the nonlocal total variation as a regularization term.