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2011 | 21 | 1 | 193-202
Tytuł artykułu

System matrix computation for iterative reconstruction algorithms in SPECT based on direct measurements

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A method for system matrix calculation in the case of iterative reconstruction algorithms in SPECT was implemented and tested. Due to a complex mathematical description of the geometry of the detector set-up, we developed a method for system matrix computation that is based on direct measurements of the detector response. In this approach, the influence of the acquisition equipment on the image formation is measured directly. The objective was to obtain the best quality of reconstructed images with respect to specified measures. This is indispensable in order to be able to perform reliable quantitative analysis of SPECT images. It is also especially important in non-hybrid gamma cameras, where not all physical processes that disturb image acquisition can be easily corrected. Two experiments with an 131 I point source placed at different distances from the detector plane were performed allowing the detector response to be acquired as a function of the point source distance. An analytical Gaussian function was fitted to the acquired data in both the one- and the two-dimensional case. A cylindrical phantom filled with a water solution of 131 I containing a region of “cold” spheres as well as a uniform solution (without any spheres) was used to perform algorithm evaluation. The reconstructed images obtained by using four different of methods system matrix computation were compared with those achieved using reconstruction software implemented in the gamma camera. The contrast of the spheres and uniformity were compared for each reconstruction result and also with the ranges of those values formulated by the AAPM (American Association of Physicists in Medicine). The results show that the implementation of the OSEM (Ordered Subsets Expectation Maximization) algorithm with a one-dimensional fit to the Gaussian CDR (Collimator-Detector Response) function provides the best results in terms of adopted measures. However, the fit of the two-dimensional function also gives satisfactory results. Furthermore, the CDR function has the potential to be applied to a fully 3D OSEM implementation. The lack of the CDR in system matrix calculation results in a very noisy image that cannot be used for diagnostic purposes.
Rocznik
Tom
21
Numer
1
Strony
193-202
Opis fizyczny
Daty
wydano
2011
otrzymano
2010-03-24
poprawiono
2010-07-17
Twórcy
autor
  • Institute of Automatic Control, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
  • Nuclear Medicine and Endocrine Oncology Department, Comprehensive Cancer Centre, Maria Skłodowska-Curie Memorial Institute ul. Wybrzeże Armii Krajowej 15, 44-101 Gliwice, Poland
  • Nuclear Medicine and Endocrine Oncology Department, Comprehensive Cancer Centre, Maria Skłodowska-Curie Memorial Institute ul. Wybrzeże Armii Krajowej 15, 44-101 Gliwice, Poland
  • Nuclear Medicine and Endocrine Oncology Department, Comprehensive Cancer Centre, Maria Skłodowska-Curie Memorial Institute ul. Wybrzeże Armii Krajowej 15, 44-101 Gliwice, Poland
Bibliografia
  • Autret, D., Bitar, A., Ferrer, L., Lisbona, A. and Bardies, M. (2005). Monte Carlo modeling of gamma cameras for I131 imaging in targeted radiotherapy, Cancer Biotherapy and Radiopharmaceuticals 20(1): 77-84.
  • Borys, D., Panek, R., Gorczewski, K., d'Amico, A., Steinhof, K. and Psiuk-Maksymowicz, K. (2006). Evaluation of SPECT-CT image fusion quality control, Biocybernetics and Biomedical Engineering 26(4): 79-90.
  • Bruyant, P.P. (2002). Analytic and iterative reconstruction algorithms in SPECT, Journal of Nuclear Medicine 43(10): 1343-1358.
  • Chang, L.T. (1978). A method for attenuation correction in radionuclide computed tomography, IEEE Transactions on Nuclear Science 25(1): 638-643.
  • Cherry, S.R., Sorenson, J.A. and Phelps, M.A. (2003). Physics in Nuclear Medicine, Saunders/Elsevier Science, Philadelphia, PA.
  • Formiconi, A.R., Pupi, A. and Passeri, A. (1989). Compensation of spatial system response in SPECT with conjugate gradient reconstruction technique, Physics in Medicine and Biology 34(1): 69-84.
  • Gilland, D.R., Jaszczak, R.J., Wang, H., Turkington, T.G., Greer, K.L. and Coleman, R. E. (1994). A 3D model of nonuniform attenuation and detector response for efficient iterative reconstruction in SPECT, Physics in Medicine and Biology 39(3): 547-561.
  • Graham, L.S., Fahey, F.H., Madsen, M.T., van Aswegen, A. and Yester, M.V. (1995). Quantitation of SPECT performance: Report of Task Group 4, Nuclear Medicine Committee, Medical Physics 22(4): 401-409.
  • Hubbell, J.H. and Seltzer, S.M. (n.d.). Tables of X-ray mass attenuation coefficients and mass energy-absorption coefficients, http://www.nist.gov/physlab/data/xraycoef/.
  • Hudson, H.M. and Larkin, R.S. (1994). Accelerated image reconstruction using ordered subsets of projection data, IEEE Transactions on Medical Imaging 13(4): 601-609.
  • Ichihara, T., Ogawa, K., Motomura, N., Kubo, A. and Hashimoto, S. (1993). Compton scatter compensation using the triple-energy window method for single- and dual-isotope SPECT, Journal of Nuclear Medicine 34(12): 2216-2221.
  • Lazaro, D., El Bitar, Z., Breton, V., Hill, D. and Buvat, I. (2005). Fully 3D Monte Carlo reconstruction in SPECT: A feasibility study, Physics in Medicine and Biology 50(16): 3739-3754.
  • Liang, Z., Turkington, T.G., Gilland, D.R., Jaszczak, R.J. and Coleman, R.E. (1992). Simultaneous compensation for attenuation, scatter and detector response for SPECT reconstruction in three dimensions, Physics in Medicine and Biology 37(3): 587-603.
  • Loudos, G.K. (2008). An efficient analytical calculation of probability matrix in 2D SPECT, Computerized Medical Imaging and Graphics 32(2): 83-94.
  • Rafecas, M., Böning, G., Pichler, B.J., Lorenz, E., Schwaiger, M. and Ziegler, S.I. (2004). Effect of noise in the probability matrix used for statistical reconstruction of PET data, IEEE Transactions on Nuclear Science 51(1): 149-156.
  • Shepp, L.A. and Vardi, Y. (1982). Maximum likelihood reconstruction for emission tomography, IEEE Transactions on Medical Imaging 1(2): 113-122.
  • Vandenberghe, S., D'Asseler, Y., Van de Walle, R., Kauppinen, T., Koole, M., Bouwens, L., Van Laere, K., Lemahieu, I. and Dierckx, R.A. (2001). Iterative reconstruction algorithms in nuclear medicine, Computerized Medical Imaging and Graphics 25(2): 105-111.
  • Zaidi, H. (2006). Quantitative Analysis in Nuclear Medicine Imaging, Springer, New York, NY.
  • Zeng, G.L. (2001). Image reconstruction-A tutorial, Computerized Medical Imaging and Graphics 25(2): 97-103.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv21i1p193bwm
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