Efficient 3D TOF PET Reconstruction Using View-Grouped Histo-Images DIRECT - Direct Image Reconstruction for TOF

• Published in: IEEE transactions on medical imaging Vol. 28; no. 5; pp. 739 - 751
• Main Authors: Matej, Samuel, Surti, Suleman, Jayanthi, Shridhar, Daube-Witherspoon, Margaret E., Lewitt, Robert M., Karp, Joel S.
• Format: Journal Article
• Language: English
• Published: 13.01.2009
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2008.2012034

For modern Time-Of-Flight PET systems, in which the number of possible lines of response and TOF bins is much larger than the number of acquired events, the most appropriate reconstruction approaches are considered to be list-mode methods. However, their shortcomings are relatively high computational costs for reconstruction and for sensitivity matrix calculation. Efficient treatment of TOF data within the proposed DIRECT approach is obtained by 1) angular (azimuthal and co-polar) grouping of TOF events to a set of views as given by the angular sampling requirements for the TOF resolution, and 2) deposition (weighted-histogramming) of these grouped events, and correction data, into a set of “histo-images”, one histo-image per view. The histo-images have the same geometry (voxel grid, size and orientation) as the reconstructed image. The concept is similar to the approach involving binning of the TOF data into angularly sub-sampled histo-projections -projections expanded in the TOF directions. However, unlike binning into histo-projections, the deposition of TOF events directly into the image voxels eliminates the need for tracing and/or interpolation operations during the reconstruction. Together with the performance of reconstruction operations directly in image space, this leads to a very efficient implementation of TOF reconstruction algorithms. Furthermore, the resolution properties are not compromised either, since events are placed into the image elements of the desired size from the beginning. Concepts and efficiency of the proposed data partitioning scheme are demonstrated in this work by using the DIRECT approach in conjunction with the Row-Action Maximum-Likelihood (RAMLA) algorithm.