Wavelet-based regularization and edge preservation for submillimetre 3D list-mode reconstruction data from a high resolution small animal PET system

The data obtained from a PET system tend to be noisy because of the limitations of the current instrumentation and the detector efficiency. This problem is particularly severe in images of small animals as the noise contaminates areas of interest within small organs. Therefore, denoising becomes a challenging task. In this paper, a novel wavelet-based regularization and edge preservation method is proposed to reduce such noise. To demonstrate this method, image reconstruction using a small mouse 18F NEMA phantom and a 18F mouse was performed. Investigation on the effects of the image quality was addressed for each reconstruction case. Results show that the proposed method drastically reduces the noise and preserves the image details.