Abstract
Although of important medical implications, simultaneous dual–tracer positron emission tomography reconstruction remains a challenging problem, primarily because the photon measurements from dual tracers are overlapped. In this paper, we propose a simultaneous dynamic dual–tracer reconstruction of tissue activity maps based on guidance from tracer kinetics. The dual–tracer reconstruction problem is formulated in a state–space representation, where parallel compartment models serve as continuous–time system equation describing the tracer kinetic processes of dual tracers, and the imaging data is expressed as discrete sampling of the system states in measurement equation. The image reconstruction problem has therefore become a state estimation problem in a continuous–discrete hybrid paradigm, and H ∞ filtering is adopted as the estimation strategy. As H ∞ filtering makes no assumptions on the system and measurement statistics, robust reconstruction results can be obtained for the dual-tracer PET imaging system where the statistical properties of measurement data and system uncertainty are not available a priori, even when there are disturbances in the kinetic parameters. Experimental results on digital phantoms, Monte Carlo simulations and physical phantoms have demonstrated the superior performance.
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Keywords
- Positron Emission Tomography
- Activity Image
- Average Percentage Error
- Count Level
- State Estimation Problem
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Huang, S.C., Carson, R.E., Hoffman, E.J.: An investigation of a double-tracer technique for positron computerized tomography. Journal of Nuclear Medicine 23, 816–822 (1982)
Koeppe, R.A., Raffel, D.M., Snyder, S.E.: Dual-[11C] Tracer Single-Acquisition Positron Emission Tomography Studies. Journal of Cerebral Blood Flow & Metabolism 21, 1480–1492 (2001)
Kadrmas, D.J., Rust, T.C.: Feasibility of rapid multi-tracer PET tumor imaging. Nuclear Science Symposium Conference Record 4, 2664–2668 (2004)
Rust, T.C., Dibella, E.V., McGann, C.J.: Rapid dual-injection single-scan (13) N-ammonia PET for quantification of rest and stress myocardial blood flow. Physics in Medicine and Biology 51, 5347–5362 (2006)
Hayashi, T., Kudomi, N., Watabe, H.: A rapid CBF/CMRO2 measurement with a single PET scan with dual-tracer/integration technique in human. Journal of Cerebral Blood Flow & Metabolism 25, S609 (2005)
Black, N.F., McJames, S., Rust, T.C.: Evaluation of rapid dual-tracer 62Cu-PTSM + 62Cu-ATSM PET in dogs with spontaneously occurring tumors. Physics in Medicine and Biology 53, 217–232 (2008)
Liu, H., Tian, Y., Shi, P.: PET image reconstruction: A robust state space approach. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 197–209. Springer, Heidelberg (2005)
Tong, S., Shi, P.: Tracer kinetics guided dynamic PET reconstruction. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 421–433. Springer, Heidelberg (2007)
Gunn, R.N., Gunn, S.R., Turkheimer, F.E.: Positron Emission Tomography Compartmental Models: A Basis Pursuit Strategy for Kinetic Modeling. Journal of Cerebral Blood Flow & Metabolism 21, 635–652 (2001)
Cobelli, C., Foster, D., Toffolo, G.: Tracer Kinetics in Biomedical Research: From Data to Model. Kluwer Academic/Plenum Publishers, New York (2000)
Shen, X., Deng, L.: A dynamic system approach to speech enhancement using the H1 filtering algorithm. IEEE Transactions on Speech and Audio Processing 7(4), 391–399 (1997)
Schiff, J., Shnider, S.: A natural approach to the numerical integration of Riccati differential equations. SIAM Journal on Numerical Analysis 36, 1392–1413 (1996)
Jan, S.: GATE: a simulation toolkit for PET and SPECT. Physics in Medicine and Biology 49, 4543–4561 (2004)
Muzic, R.F., Cornelius, S.: COMKAT: Compartment Model Kinetic Analysis Tool. Journal of Nuclear Medcine 42, 636–645 (2001)
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Gao, F., Liu, H., Jian, Y., Shi, P. (2009). Dynamic Dual-Tracer PET Reconstruction. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds) Information Processing in Medical Imaging. IPMI 2009. Lecture Notes in Computer Science, vol 5636. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02498-6_4
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DOI: https://doi.org/10.1007/978-3-642-02498-6_4
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