Table of Contents
International Journal of Molecular Imaging
Volume 2013 (2013), Article ID 435959, 12 pages
http://dx.doi.org/10.1155/2013/435959
Research Article

Region-Based Partial Volume Correction Techniques for PET Imaging: Sinogram Implementation and Robustness

1Department of Medical Biophysics, University of Toronto, Odette Cancer Centre at Sunnybrook Health Sciences Centre, Room TG-217, 2075 Bayview Avenue, Toronto, ON, Canada M4N 3M5
2Department of Electrical and Computer Engineering, University of McMaster, 1280 Main Street West, Hamilton, ON, Canada L8S 4K1
3L.C. Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON, Canada M4N 3M5
4Institute of Medical Science, 1 King’s College Circle, University of Toronto, Toronto, ON, Canada M5S 1A8
5Department of Medical Physics, Odette Cancer Centre at Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON, Canada M4N 3M5
6Department of Radiation Oncology, University of Toronto, Faculty of Medicine, 150 College Street, Room 106, Toronto, ON, Canada M5S 3E2
7Department of Radiation Oncology, Odette Cancer Centre at Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON, Canada M4N 3M5
8Department of Medical Imaging, University of Toronto, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON, Canada M4N 3M5

Received 2 June 2013; Revised 2 September 2013; Accepted 3 October 2013

Academic Editor: Habib Zaidi

Copyright © 2013 Mike Sattarivand et al. 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.

Abstract

Background/Purpose. Limited spatial resolution of positron emission tomography (PET) requires partial volume correction (PVC). Region-based PVC methods are based on geometric transfer matrix implemented either in image-space (GTM) or sinogram-space (GTMo), both with similar performance. Although GTMo is slower, it more closely simulates the 3D PET image acquisition, accounts for local variations of point spread function, and can be implemented for iterative reconstructions. A recent image-based symmetric GTM (sGTM) has shown improvement in noise characteristics and robustness to misregistration over GTM. This study implements the sGTM method in sinogram space (sGTMo), validates it, and evaluates its performance. Methods. Two 3D sphere and brain digital phantoms and a physical sphere phantom were used. All four region-based PVC methods (GTM, GTMo, sGTM, and sGTMo) were implemented and their performance was evaluated. Results. All four PVC methods had similar accuracies. Both noise propagation and robustness of the sGTMo method were similar to those of sGTM method while they were better than those of GTMo method especially for smaller objects. Conclusion. The sGTMo was implemented and validated. The performance of the sGTMo in terms of noise characteristics and robustness to misregistration is similar to that of the sGTM method and improved compared to the GTMo method.