Abstract
Dynamic emission tomography is a technique used for quantifying the biochemical and physiological processes within the body. For certain neuroimaging applications, like kinetic modelling in positron emission tomography (PET), segmenting the measured data into a fewer number of regions-of-interest (ROI) is an important procedure needed for calculation of regional time-activity curves (TACs). Conventional estimation of regional activities in image domain suffers from substantial errors due to the reconstruction artifacts and segmentation inaccuracies. In this study, we present an approach for separating the dynamic tomographic data directly in the projection space using the least-squares method. Sinogram ROIs are the fractional parts of different tissue types measured at each voxel. Regional TACs can be estimated from the segmented sinogram ROIs, thereby avoiding the image reconstruction step. The introduced technique was validated with the two dynamic synthetic phantoms simulated based on 11C- and 18F-labelled tracer distributions. From the quantitative point of view, TAC estimation from the segmented sinograms yielded more accurate results compared to the image-based segmentation.
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Kamasak, M.E., Bouman, C.A., Morris, E.D., Sauer, K.: Direct reconstruction of kinetic parameter images from dynamic PET data. IEEE Tran. Med. Imag. 24(5), 636–650 (2005)
Passchier, J., Gee, A., Willemsen, A., Vaalburg, W., van Waarde, A.: Measuring drug-related receptor occupancy with positron emission tomography. Methods 27, 278–286 (2002)
Ashburner, J., Haslam, J., Taylor, C., Cunningham, V.J., Jones, T.: A cluster analysis approach for the characterization of dynamic PET data. In: Myers, R., Cunningham, V., Bailey, D., Jones, T. (eds.) Quantification of Brain Function using PET, pp. 301–306. Academic, San Diego (1996)
Chen, J.L., Gunn, S., Nixon, M.S., Gunn, R.N.: Markov random field models for segmentation of PET images. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, pp. 468–474. Springer, Heidelberg (2001)
Wong, K.-P., Feng, D., Meikle, S., Fulham, M.: Segmentation of dynamic PET images using cluster analysis. IEEE Trans. Nucl. Sci. 49(1), 200–207 (2002)
Brankov, J.G., Galatsanos, N.P., Yang, Y.Y., Wernick, M.N.: Segmentation of dynamic PET or fMRI images based on a similarity metric. IEEE Trans. Nucl. Sci. 50(5), 1410–1414 (2003)
Kim, J., Feng, D.D., Cai, T.W., Eberl, S.: Automatic 3D temporal kinetics segmentation of dynamic emission tomography image using adaptive region growing cluster analysis. In: Nucl. Sci. Symposium Conf. Record, vol. 3, pp. 1580–1583 (2002)
Parker, B., Feng, D.D.: Variational segmentation and PCA applied to dynamic PET analysis. In: Jin, J.S., Eades, P., Feng, D.D., Yan, H. (eds.) Proc. Pan-Sydney Workshop on Visual Information Processing (VIP2002), Adelaide Australia. Conferences in Research and Practice in Information Technology, vol. 22, pp. 89–92 (2002)
Koivistoinen, H., Tohka, J., Ruotsalainen, U.: Comparison of pattern classification methods in segmentation of dynamic PET brain images. In: Proc. of Sixth Nordic Signal Processing Symposium. NORSIG 2004, pp. 73–76 (2004)
Kak, A.C., Slaney, M.: Principles of Computerized Tomographic Imaging. Society of Industrial and Applied Mathematics (2001)
Formiconi, A.R.: Least squares algorithm for region-of-interest evaluation in emission tomography. IEEE Tran. Med. Imag. 12(1), 90–100 (1993)
Reutter, B.W., Gullberg, G.T., Huesman, R.H.: Direct least-squares estimation of spatiotemporal distributions from dynamic SPECT projections using a spatial segmentation and temporal B-splines. IEEE Tran. Med. Imag. 19(5), 434–450 (2000)
Trefethen, L.N., Bau III, D.: Numerical linear algebra. Society of Industrial and Applied Mathematics, Philadelphia (1997)
Zubal, I., Hurrell, C., Smith, E., Rattner, Z., Gindi, G., Hoffer, P.: Computerized three-dimensional segmented human anatomy. Med. Phys. 21(2), 299–302 (1994)
Reilhac, A., Lartizien, C., Costes, N., Sans, S., Comtat, C., Gunn., R.N., Evans, A.C.: PET-SORTEO: A Monte Carlo-based simulator with high count rate capabilities. IEEE Trans. Nuc. Sci. 51(1), 46–52 (2004)
Magadan-Mendez, M., Kivimäki, A., Ruotsalainen, U.: ICA separation of functional components from dynamic cardiac PET data. In: IEEE Nucl. Sci. Symposium Conference Record, vol. 4, pp. 2618–2622 (2003)
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Krestyannikov, E., Tohka, J., Ruotsalainen, U. (2006). Segmentation of Dynamic Emission Tomography Data in Projection Space. In: Beichel, R.R., Sonka, M. (eds) Computer Vision Approaches to Medical Image Analysis. CVAMIA 2006. Lecture Notes in Computer Science, vol 4241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11889762_10
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DOI: https://doi.org/10.1007/11889762_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46257-6
Online ISBN: 978-3-540-46258-3
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