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
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Rb infusion profiles. Biasing of factors due to spillover is reduced with MFO compared to MSO, while the robustness and reproducibility of MSO is maintained.