Effect of Sub-energy Windows’ Parameters on the Triple Energy Window Scatter Correction Method Accuracy in 99mTc SPECT Imaging
Scatter correction in SPECT quantification is of major importance to compensation for the scatter contribution under the photopeak. The triple energy windows method (TEW) is one of the suggested ways for scatter correction that are widely used in clinical routine. However, it can be a source of additional noise if the width or the number of sub-energy windows is not accurately chosen. To determine the precise scatter estimation windows settings under the 99mTc photopeak, scatter fraction was calculated for different sub-energy widths and numbers through GATE Monte Carlo simulation, for a main energy window of 15 %, centered at 140 keV. Four different acquisitions, with cold or hot inserts in a warm or a cold background, were studied. The estimation was done by two methods. The first method was the extraction of the number of detected Compton photons under the photopeak, therefore considered as the true scattered photons. The second method was the application of TEW method to the simulated energy spectra. The comparison of results corresponding to both methods shows a good agreement in two cases: simultaneous 7 % and 5 % sub-energy windows, respectively, positioned on the left and the right of the main energy window, and the second case is a 3 % left sub-energy window without a right sub-energy window. These sub-energy windows were then applied to experimental tomographic acquisitions to assess their impact on contrast, relative noise of the background (RNB), signal‑to‑noise ratio (SNR), integral uniformity (IU), and tomographic spatial resolution. Good results for these quantitative parameters were acquired with simultaneous 7 % and 5 % sub-energy windows. However, there was very little enhancement for tomographic spatial resolution.
Quantitative nuclear medicine imaging concepts, requirements and methods, IAEA Human Health Reports No. 9, © IAEA, Vienna (2014).
B. F. Hutton, I. Buvat and F. J. Beekman, Phys. Med. Biol. 56 (2011) R85.
K. F. Koral, X. Q. Wang, W. L. Rogers et al., J. Nucl. Med. 29 (1988) 195.
A. Kojima, A. Tsuji, Y. Takaki et al., Ann. Nucl. Med. 6 (1992) 153.
K. Ogawa, Y. Harata, T. Ichihara et al., IEEE Trans. Med. Imaging 10 (1991) 408.
K. Ogawa, Ann. Nucl. Med. 8 (1994) 277.
A. Kojima, M. Matsumoto and M. Takahashi, Ann. Nucl. Med. 5 (1991) 139.
C. E. Floyd, R. J. Jaszczak, C. C. Harris et al., Phys. Med. Biol. 29 (1984) 1217.
Y. Narita, S. Eberl, H. Iida et al., Phys. Med. Biol. 41 (1996) 2481.
V. Changizi, A. Takavar, A. Babakhani et al., J. Appl. Clin. Med. Phys. 9 (2008) 136.
Symbia T Series: System Specifications, Siemens AG (2013).
K. Assié, I. Gardin, P. Véra et al., Phys. Med. Biol. 50 (2005) 3113.
R. Brun and F. Rademakers, ROOT - An object oriented data analysis framework, Proceedings AIHENP'96 Workshop, Lausanne, Nucl. Inst. Meth. in Phys. Res. A 389(1997) 81.
C. El Amrani, O. Bouhali and R. Merrouch, IADIS IJCSIS. 4 (2009) 85.
E. Hawman, A. H. Vija, R. Daffach et al., Flash 3D Technology: Optimizing SPECT Quality and Accuracy, Whitepaper Flash 3D, Siemens Medical Solutions (2003) 1.
J. S. Fleming, Nucl. Med. Commun. 10 (1989) 83.
A. M. Loening and S. S. Gambhir, Mol. Imaging 2 (2003) 131.
L. S. Graham, F. H. Fahey, M. T. Madsen et al., Med. Phys. 22 (1995) 401.
A. Seret, D. Nguyen and C. Bernard, EJNMMI Res. 2 (2012) 1.
H. Zaidi, Monte Carlo Modeling in Nuclear Medicine Imaging, Quantitative Analysis in Nuclear Medicine Imaging, H. Zaidi (Ed.), Springer US, New York (2006) 358.
M. N. Asl, A. Sadremomtaz and A. Bitarafan-Rajabi, J. Med. Phys. 38 (2013) 189.
H. Saikouk and N. El Khayati, Monte Carlo Simulation of Scatter Effect for Clinical Gamma Camera, Proceedings of Middle East Conference on Biomedical Engineering, IEEE Computer Society 14 (2014) 30.
Copyright (c) 2022 Atom Indonesia
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.