Wavelet-based regularization strategies within the 3D list-mode ML-EM reconstruction process, for high resolution small animal PET data

Abstract

The measurements obtained from the acquiring PET system tend to be very noisy, since randoms and scatter contamination events as well as detector efficiency are strong sources of noise. In particular, for the small animal reconstructed images, this problem becomes severe corrupting areas of interest between organs, making the identification process even more difficult. For that reason, a regularization step is of crucial importance. In this paper, performance evaluations for two different strategies to include wavelet-based regularization within the list-mode Maximum Likelihood Expectation-Maximization (MLEM) reconstruction process are established. Results are compared against the standard noise reduction PSF methods (Gaussian smoothing) used for resolution recovery. For each reconstruction model proposed, investigations on the effects of image quality were addressed. Results show that reconstruction process given by the Model 2, significantly improves the quantity accuracy of the images, especially incrementing the image contrast values in comparison with the standard noise reduction method, which tends to blur the image data. Reconstruction models were tested using list-mode measured data from the high-resolution quad-HIDAC small animal PET scanner.