Non-uniform TOF PET reconstruction for uniform convergence

Time of fight (TOF) PET reconstruction statistically improves the image quality and the fast convergence speed, so-called the recovery rate. Although TOF PET can improve the overall signal to noise ratio (SNR) of the image compared to non-TOF PET, the SNR disparity between separate regions in the reconstructed image using TOF data becomes higher than that using non-TOF data. In particular, because of TOF bins having different photon statistics, the SNR for low activity or small regions is significantly lower than the SNR for high activity regions, which can degrade the overall image quality in practice when terminating TOF-PET reconstruction after a finite number of iterations because different SNRs of regions have different recovery rates. Achieving more uniform recovery rates across different SNR regions is crucial to improve the quality of the reconstructed image. In this project, we propose a TOF-PET reconstruction algorithm using the ordered subsets non-uniform separable quadratic surrogates (OS-NU-SQS) algorithm with Nesterov's momentum method and quadratic roughness regularization. In computer simulations, we demonstrate that the proposed method can improve the image quality and recovery rate uniformity compared to the TOF-based conventional ordered subset expectation maximization (OSEM) and conventional SQS algorithms with early stopping criterion. Furthermore, the proposed method is accelerated using our GPU implementation, making the algorithm more practical.

XCAT phantom simulation setup using (a) three different region of interests (ROIs) with high intensity components at (b) lung, (c) spine and (d) liver.

Recovery rate comparison by normalized root mean square difference (NRMSD) of (a) OS-SQS, (b) OSEM, (c) OS-NUSQS and (d) OS-NUSQS with momentum for three ROIs

K. Kim, J. C. Ye, L. Cheng, K. Ying, G. E. Fakhri and Q. Li, TOF-PET ordered subset reconstruction using non-uniform separable quadratic surrogates algorithm, IEEE International Symposium on Biomedical Imaging (ISBI), Beijing, China, April, 2014.