Characterization and reduction of noise in dynamic PET data using masked volumewise principal component analysis
2011 (English)In: Journal of Nuclear Medicine Technology, ISSN 0091-4916, Vol. 39, no 1, 27-34 p.Article in journal (Refereed) Published
Masked volumewise principal component (PC) analysis (PCA) is used in PET to distinguish structures that display different kinetic behaviors after administration of a tracer. When masked volumewise PCA was introduced, one article proposed noise prenormalization because of temporal and spatial variations of the noise between slices. However, the noise prenormalization proposed in that article was applicable only to datasets reconstructed using filtered backprojection (FBP). The study presented in this article aimed at developing a new noise prenormalization that is applicable to datasets regardless of whether they were reconstructed with FBP or an iterative reconstruction algorithm, such as ordered-subset expectation maximization (OSEM).
Methods: A phantom study was performed to investigate differences in the expectation values and SDs of datasets reconstructed with FBP and OSEM. A novel method, higher-order PC noise prenormalization, was suggested and evaluated against other prenormalization methods on clinical datasets.
Results: Masked volumewise PCA of data reconstructed with FBP was much more dependent on an appropriate prenormalization than was analysis of data reconstructed with OSEM. Higher-order PC noise prenormalization showed an overall good performance with both FBP and OSEM reconstructions, whereas the other prenormalization methods performed well with only 1 of the 2 methods.
Conclusion: Higher-order PC noise prenormalization has potential for improving the results from masked volumewise PCA on dynamic PET datasets independent of the type of reconstruction algorithm.
Place, publisher, year, edition, pages
2011. Vol. 39, no 1, 27-34 p.
Medical Image Processing
IdentifiersURN: urn:nbn:se:uu:diva-162332DOI: 10.2967/jnmt.110.077347PubMedID: 21321248OAI: oai:DiVA.org:uu-162332DiVA: diva2:774832