Region-Based Partial Volume Correction Techniques for PET Imaging: Sinogram Implementation and Robustness. (17th December 2013)

• Record Type: Journal Article
• Authors: Sattarivand, Mike; Armstrong, Jennifer; Szilagyi, Gregory M.; Kusano, Maggie; Poon, Ian; Caldwell, Curtis
• Other Names: Zaidi Habib Academic Editor.
• Is Part Of: International journal of molecular imaging. Volume 2013(2013)
• Publication Date: 2013-12-17
• Subjects: Molecular diagnosis -- Periodicals; Molecular probes -- Diagnostic use -- Periodicals; Diagnostic imaging -- Periodicals; Molecular Imaging; Diagnostic imaging; Molecular diagnosis; Electronic journals; Periodicals; Periodicals; 616.0757
• DOI: 10.1155/2013/435959
• Languages: English
• ISSNs: 2090-1712

Abstract

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.