Abstract
In dynamic positron emission tomography (PET), where scan durations often exceed 1 hour, registration of motion-corrupted dynamic PET images is necessary in order to maintain the integrity of the physiological/pharmacological/biochemical information derived from the tracer kinetic analysis of the scan. A pharmacokinetic model, which is traditionally used to analyse PET data following any registration, was incorporated into the registration process itself in order to allow for a groupwise registration of the temporal time frames. The new method achieved smaller registration errors and improved kinetic parameter estimates on validation data sets as compared with the traditional image based similarity registration approach. When applied to measured clinical data from 10 healthy subjects scanned with [11C]-(+)-PHNO (a dopamine D3/D2 receptor tracer), it reduced the intra-class variability on the tracer kinetics, suggesting a successful registration. Our new method which incorporates a generic tracer kinetic model could be applied widely to dynamic PET data as part of an automated tool to remove motion artefacts and increase the integrity and statistical power of these data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Raghunath, N., Faber, T.L., Suryanarayanan, S., Votaw, J.R.: Motion correction of PET brain images through deconvolution: II. practical implementation and algorithm optimization. Physics in Medicine & Biology 54(3), 813–829 (2009)
Koshino, K., Watabe, H., Hasegawa, S., Hayashi, T., Hatazawa, J., Iida, H.: Development of motion correction technique for cardiac (15)O-water PET study using an optical motion tracking system. Annals of Nuclear Medicine 24(1), 1–11 (2010)
Searle, G., Beaver, J.D., Comley, R.A., Bani, M., Tziortzi, A., Slifstein, M., Mugnaini, M., Griffante, C., Wilson, A.A., Merlo-Pich, E., Houle, S., Gunn, R., Rabiner, E.A., Laruelle, M.: Imaging dopamine D3 receptors in the human brain with positron emission tomography, [11C]PHNO, and a selective D3 receptor antagonist. Biol. Psychiatry 68(4), 392–399 (2010)
Bhushan, M., Schnabel, J.A., Risser, L., Heinrich, M.P., Brady, J.M., Jenkinson, M.: Motion Correction and Parameter Estimation in dceMRI Sequences: Application to Colorectal Cancer. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 476–483. Springer, Heidelberg (2011)
Tofts, P.S., Brix, G., Buckley, D.L., Evelhoch, J.L., Henderson, E., Knopp, M.V., Larsson, H.B., Lee, T.Y., Mayr, N.A., Parker, G.J., Port, R.E., Taylor, J., Weisskoff, R.M.: Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J. Magn. Reson. Imaging. 10(3), 223–232 (1999)
Jiao, J., Salinas, C.A., Searle, G.E., Gunn, R.N., Schnabel, J.A.: Joint estimation of subject motion and tracer kinetic parameters of dynamic PET data in an EM framework. In: Proc. SPIE 83140A (2012)
Cunningham, V.J., Jones, T.: Spectral analysis of dynamic PET studies. Journal of Cerebral Blood Flow & Metabolism 13(1), 15–23 (1993)
Schmidt, K.: Which linear compartmental systems can be analyzed by spectral analysis of PET output data summed over all compartments? J. Cereb. Blood Flow Metab. 19(5), 560–569 (1999)
Lawson, C.L., Hanson, R.J.: Solving least squares problems, 3rd edn. (1995)
Gunn, R.N., Gunn, S.R., Cunningham, V.J.: Positron emission tomography compartmental models. J. Cereb. Blood Flow Metab. 21(6), 635–652 (2001)
Daube-Witherspoon, M.E., Muehllehner, G.: An iterative image space reconstruction algorithm suitable for volume ECT. IEEE Trans. Med. Imaging 5(2), 61–66 (1986)
Oikonen, V.: Noise model for PET time-radioactivity curves. Turku PET Centre Modelling report TPCMOD0008 (2003)
Tziortzi, A.C., Searle, G.E., Tzimopoulou, S., Salinas, C., Beaver, J.D., Jenkinson, M., Laruelle, M., Rabiner, E.A., Gunn, R.N.: Imaging dopamine receptors in humans with [11C]-(+)-PHNO: dissection of D3 signal and anatomy. Neuroimage 54(1), 264–277 (2011)
Searle, G.E., Beaver, J.D., Tziortzi, A., Comley, R.A., Bani, M., Ghibellini, G., Merlo-Pich, E., Rabiner, E.A., Laruelle, M., Gunn, R.N.: Mathematical modelling of [11C]-(+)-PHNO human competition studies (under review)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jiao, J., Searle, G.E., Tziortzi, A.C., Salinas, C.A., Gunn, R.N., Schnabel, J.A. (2012). Spatial-temporal Pharmacokinetic Model Based Registration of 4D Brain PET Data. In: Durrleman, S., Fletcher, T., Gerig, G., Niethammer, M. (eds) Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data. STIA 2012. Lecture Notes in Computer Science, vol 7570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33555-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-33555-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33554-9
Online ISBN: 978-3-642-33555-6
eBook Packages: Computer ScienceComputer Science (R0)