Table of Contents
ISRN Biomedical Imaging
Volume 2013, Article ID 326847, 8 pages
Research Article

Evaluation of Image Quality Improvements When Adding Patient Outline Constraints into a Generalized Scatter PET Reconstruction Algorithm

1Department of Physics and Astronomy, University of Manitoba, Allen Building, Winnipeg, MB, Canada R3T 2N2
2CancerCare Manitoba, Winnipeg, MB, Canada R3E 0V9
3Department of Radiology, University of Manitoba, Winnipeg, MB, Canada R3E 0V9

Received 28 February 2013; Accepted 7 April 2013

Academic Editors: E. Anashkin and W. Han

Copyright © 2013 Hongyan Sun and Stephen Pistorius. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Scattered coincidences degrade image contrast and compromise quantitative accuracy in positron emission tomography (PET). A number of approaches to estimating and correcting scattered coincidences have been proposed, but most of them are based on estimating and subtracting a scatter sinogram from the measured data. We have previously shown that both true and scattered coincidences can be treated similarly by using Compton scattering kinematics to define a locus of scattering which may in turn be used to reconstruct the activity distribution using a generalized scatter maximum-likelihood expectation maximization (GS-MLEM) algorithm. The annihilation position can be further confined by taking advantage of the patient outline (or a geometrical shape that encompasses the patient outline). The proposed method was tested on a phantom generated using GATE. The results have shown that for scatter fractions of 10–60% this algorithm improves the contrast recovery coefficients (CRC) by 4 to 28.6% for a source and 5.1 to 40% for a cold source while the relative standard deviation (RSD) was reduced. Including scattered photons directly into the reconstruction eliminates the need for (often empirical) scatter corrections, and further improvements in the contrast and noise properties of the reconstructed images can be made by including the patient outline in the reconstruction algorithm as a constraint.