A minimal factor overlap method for resolving ambiguity in factor analysis of dynamic cardiac PET
Factor analysis has been pursued as a means to decompose dynamic cardiac PET images into different tissue types based on their unique physiology. Each tissue is represented by a time-activity profile (factor) and an associated spatial distribution (structure). Decomposition is based on non-negative constraints of both the factors and structures; however, additional constraints are required to achieve a unique solution. In this work we present a novel method (minimal factor overlap - MFO) and compare its performance to a previously published constraint (minimal spatial overlap - MSO). We compared both methods using simulated data and on a canine model with variable 82Rb infusion profiles. Biasing of factors due to spillover is reduced with MFO compared to MSO, while the robustness and reproducibility of MSO is maintained.
|2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC|
|Organisation||Department of Systems and Computer Engineering|
Klein, R. (Ran), Bentourkia, M. (M'hamed), Beanlands, R.S. (Rob S.), Adler, A, & DeKemp, R.A. (Robert A.). (2007). A minimal factor overlap method for resolving ambiguity in factor analysis of dynamic cardiac PET. Presented at the 2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC. doi:10.1109/NSSMIC.2007.4436835