Abstract
In this paper, we explore the usage of state space principles for the estimation of activity map in tomographic PET imaging. The proposed strategy formulates the dynamic changes of the organ activity distribution through state space evolution equations and the photon-counting measurements through observation equations, thus makes it possible to unify the dynamic reconstruction problem and static reconstruction problem into a general framework. Further, it coherently treats the uncertainties of the statistical model of the imaging system and the noisy nature of measurement data. The state-space reconstruction problem is solved by both the popular but suboptimal Kalman filter (KF) and the robust H ∞ estimator. Since the H ∞ filter seeks the minimum-maximum-error estimates without any assumptions on the system and data noise statistics, it is particular suited for PET imaging where the measurement data is known to be Poisson distributed. The proposed framework is evaluated using Shepp-Logan simulated phantom data and compared to standard methods with favorable results.
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Tian, Y., Liu, H., Shi, P. (2004). State Space Strategies for Estimation of Activity Map in PET Imaging. In: Yang, GZ., Jiang, TZ. (eds) Medical Imaging and Augmented Reality. MIAR 2004. Lecture Notes in Computer Science, vol 3150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28626-4_6
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DOI: https://doi.org/10.1007/978-3-540-28626-4_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22877-6
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