RT Journal Article
SR Electronic
T1 Characterization and Reduction of Noise in Dynamic PET Data Using Masked Volumewise Principal Component Analysis
JF Journal of Nuclear Medicine Technology
JO J. Nucl. Med. Technol.
FD Society of Nuclear Medicine
SP 27
OP 34
DO 10.2967/jnmt.110.077347
VO 39
IS 1
A1 Svensson, Per-Edvin
A1 Olsson, Johan
A1 Engbrant, Fredrik
A1 Bengtsson, Ewert
A1 Razifar, Pasha
YR 2011
UL http://tech.snmjournals.org/content/39/1/27.abstract
AB 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.