One-pass list-mode EM algorithm for high-resolution 3-D PET image reconstruction into large arrays

• Published in: IEEE transactions on nuclear science Vol. 49; no. 3; pp. 693 - 699
• Main Authors: Reader, A.J., Ally, S., Bakatselos, F., Manavaki, R., Walledge, R.J., Jeavons, A.P., Julyan, P.J., Sha Zhao, Hastings, D.L., Zweit, J.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-06-2002; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Animals; Arrays; Biomedical imaging; Cancer; Demand; Hospitals; Image reconstruction; Instruments; Iterative algorithms; Marketing; Mathematical analysis; Positron emission tomography; Reconstruction; Reconstruction algorithms; Studies
• ISSN: 0018-9499; 1558-1578
• DOI: 10.1109/TNS.2002.1039550

High-resolution three-dimensional (3-D) positron emission tomography (PET) scanners with high count rate performance, such as the quad-high density avalanche chamber (HIDAC), place new demands on image reconstruction algorithms due to the large quantities of high-precision list-mode data which are produced. Therefore, a reconstruction algorithm is required which can, in a practical time frame, reconstruct into very large image arrays (submillimeter voxels, which range over a large field of view) whilst preferably retaining the precision of the data. This work presents an algorithm which meets these demands: one-pass list-mode expectation maximization (OPL-EM) algorithm. The algorithm operates directly on list-mode data, passes through the data once only, accounts for finite resolution effects in the system model, and can also include regularization. The algorithm performs multiple image updates during its single pass through the list-mode data, corresponding to the number of subsets that the data have been split into. The algorithm has been assessed using list-mode data from a quad-HIDAC and is compared to the analytic reconstruction method 3-D reprojection (RP) with 3-D filtered backprojection.