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Summary: Abstract 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 different 82
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.
I. INTRODUCTION
actor analysis techniques have been explored as a means to
improve cardiac function quantification. An image series is
decomposed into a finite number of temporal factors and their
corresponding spatial distribution (structures) which ideally
should correspond to the physiology of the imaged tissue [1]. The
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