Gaussian Filter for Brain SPECT Imaging


  • Nikolay Nikolov Igor Sikorsky Kyiv Polytechnic Institute; Kundiiev Institute of Occupational Health, NAMS of Ukraine, Ukraine
  • Sergiy Makeyev Romodanov Neurosurgery Institute, NAMS of Ukraine, Ukraine
  • Olga Korostynska Oslo Metropolitan University, Norway
  • Tetyana Novikova Romodanov Neurosurgery Institute, NAMS of Ukraine, Ukraine
  • Yelizaveta Kriukova Igor Sikorsky Kyiv Polytechnic Institute, Ukraine



emission computed tomography, SPECT, cerebral blood flow, radioactive count, Gaussian filter, optimal filtering, speckle-noise, radiopharmaceutical, 99mTc-HMPAO


Background. The presence of a noise component on 3D images of single-photon emission computed tomo­graphy (SPECT) of a brain significantly distorts the probability distribution function (PD) of the radioactive count rate in the images. The presence of noise and further filtering of the data, based on a subjective assessment of image quality, have a significant impact on the calculation of volumetric cerebral blood flow and the values of the uptake asymmetry of the radiopharmaceutical in a brain.

Objective. We are aimed to develop a method for optimal SPECT filtering of brain images with lipophilic radiopharmaceuticals, based on a Gaussian filter (GF), for subsequent image segmentation by the threshold method. 

Methods. SPECT images of the water phantom and the brain of patients with 99mTc-HMPAO were used. We have developed a technique for artificial addition of speckle noise to conditionally flawless data in order to determine the optimal parameters for smoothing SPECT, based on a GF. The quantitative criterion for optimal smoothing was the standard deviation between the PD of radioactive count rate of the smoothed image and conditionally ideal one.

Results. It was shown that the maximum radioactive count rate of the SPECT image has an extremum by changing the standard deviation of the GF in the range of 0.3–0.4 pixels. The greater the noise component in the SPECT image, the more quasi-linearly the corresponding rate changes. This dependence allows determining the optimal smoothing parameters. The application of the developed smoothing technique allows restoring the probability distribution function of the radioactive count rate (distribution histogram) with an accuracy up to 5–10%. This provides the possibility to standardize SPECT images of brain.

Conclusions. The research results of work solve a specific applied problem: restoration of the histogram of a radiopharmaceuticals distribution in a brain for correct quantitative assessment of regional cerebral blood flow. In contrast to the well-known publications on the filtration of SPECT data, the work takes into account that the initial tomographic data are 3D, rather than 2D slices, and contain not only uniform random Gaussian noise, but also a pronounced speckle component.


Nikolov NA, Makeev SS, Yaroshenko OYu, Novikova TG, Globa MV. Quantitative evaluation of cerebral blood flow by scintigraphic studies with 99mTc-HMPAO. Meditsinskaya Fizika. 2016;72:72-9.

Nikolov N, Makeyev S, Yaroshenko O, Novikova T, Globa M. Quantitative evaluation of the absolute value of the cerebral blood flow according to the scintigraphic studies with 99MTC-HMPAO. Res Bull Nat Tech Univ Ukr Kyiv Polytech Inst. 2017;1:61-8. DOI: 10.20535/1810-0546.2017.1.91646

Lassen NA, Andersen AR, Friberg L, Paulson OB. The retention of [99mTc]-d,l-HM-PAO in the human brain after intracarotid bolus injection: A kinetic analysis. J Cereb Blood Flow Metab. 1988 Dec;8(1_suppl):S13-22. DOI: 10.1038/jcbfm.1988.28

Andersen AR, Friberg HH, Schmidt JF, Hasselbalch SG. Quantitative measurements of cerebral blood flow using SPECT and [99mTc]-d,l-HM-PAO compared to Xenon-133. J Cereb Blood Flow Metab. 1988 Dec;8(1_suppl):S69-81. DOI: 10.1038/jcbfm.1988.35

Nikolov NA, Makeev SS, Novikova TG, Chebotariova LL, Globa MV, Unevich OA, et al. Determination of absolute cerebral blood flow scintigraphy with lipophilic radiopharmaceutical. Meditsinskaya Fizika. 2018;79:36-45.

Saxena P, Pavel DG, Quintana JC, Horwitz B. An automatic threshold-based scaling method for enhancing the usefulness of Tc-HMPAO SPECT in the diagnosis of Alzheimer’s disease. In: Wells WM, Colchester A, Delp S, editors. Medical image computing and computer-assisted intervention – MICCAI'98. MICCAI 1998. Lecture notes in computer science, vol. 1496. Berlin Heidelberg: Springer; 1998. p. 623-30. DOI: 10.1007/BFb0056248

Fahey FH, Bom HH, Chiti A, Choi YY, Huang G, Lassmann M, et al. Standardization of administered activities in pediatric nuclear medicine: A report of the first nuclear medicine global initiative project, Part 2—Current standards and the path toward global standardization. J Nucl Med. 2016 Jul;57(7):1148-57. DOI: 10.2967/jnumed.115.169714

Kapucu ÖL, Nobili F, Varrone A, Booij J, Vander Borght T, Någren K, et al. EANM procedure guideline for brain perfusion SPECT using 99mTc-labelled radiopharmaceuticals, version 2. Eur J Nucl Med Mol Imaging. 2009 Dec;36(12):2093-102. DOI: 10.1007/s00259-009-1266-y

Díaz MP, Rizo OD, Díaz AL, Aparicio EE, Díaz RR. Activity optimization method in SPECT: A comparison with ROC analysis. J Zhejiang Univ Sci B. 2006 Dec;7(12):947-56. DOI:

Jang MY, Park CR, Kang S, Lee Y. Experimental study of the fast non-local means noise reduction algorithm using the Hoffman 3D brain phantom in nuclear medicine SPECT image. Optik. 2020 Dec;224:165440. DOI: 10.1016/j.ijleo.2020.165440

Modzelewski R, Janvresse E, de la Rue T, Vera P. Comparison of heterogeneity quantification algorithms for brain SPECT perfusion images. EJNMMI Res. 2012;2(1):40. DOI: 10.1186/2191-219X-2-40

Lyra M, Ploussi A. Filtering in SPECT image reconstruction. Int J Biomed Imaging. 2011;2011:1-14. DOI: 10.1155/2011/693795

Bruyant PP. Analytic and iterative reconstruction algorithms in SPECT. J Nucl Med. 2002 Oct;43(10):1343-58.

Minoshima S, Maruno H, Yui N, Togawa T, Kinoshita F, Kubota M, et al. Optimization of Butterworth filter for brain SPECT imaging. Ann Nucl Med. 1993 Jun;7(2):71-7. DOI: 10.1007/BF03164571

Van Laere K, Koole M, Lemahieu I, Dierckx R. Image filtering in single-photon emission computed tomography: principles and applications. Comput Med Imaging Graph. 2001 Mar;25(2):127-33. DOI: 10.1016/S0895-6111(00)00063-X

Liu H-G, Harris JM, Inampudi CS, Mountz JM. Optimal reconstruction filter parameters for multi-headed brain SPECT: Dependence on count activity. J Nucl Med Technol. 1995;23:251-7.

Dong X, Saripan M, Mahmud R, Mashohor S, Wang A. Determination of the optimum filter for 99mTc SPECT breast imaging using a wire mesh collimator. Pak J Nucl Med. 2017;7(1):9-15. DOI: 10.24911/PJNMed.7.2

Onishi H, Matsutake S, Amijima H. Validation of optimal cut-off frequency using a Butterworth filter in single photon emission computed tomography reconstruction for the target organ: Spatial domain and frequency domain. J Faculty of Health and Welfre Prefectural University of Hiroshima. 2010;10(1): 27-36.

Sowa-Staszczak A, Lenda-Tracz W, Tomaszuk M, Głowa B, Hubalewska-Dydejczyk A. Optimization of image reconstruction method for SPECT studies performed using [99mTc-EDDA/HYNIC] octreotate in patients with neuroendocrine tumors. Nucl Med Rev. 2013 Feb 8;16(1):9-16. DOI: 10.5603/NMR.2013.0003

King MA, Glick SJ, Penney BC, Schwinger RB, Doherty PW. Interactive visual optimization of SPECT prereconstruction filtering. J Nucl Med. 1987;28:1192-8.

Huang C, Wu J, Cheng K, Pan L. Optimization of imaging parameters for SPECT scans of [99mTc]TRODAT-1 using Taguchi analysis. PLoS ONE. 2015 Mar 19;10(3):e0113817. DOI: 10.1371/journal.pone.0113817

Beekman FJ, Slijpen ETP, Niessen WJ. Supervised diffusion parameter selection for filtering SPECT brain images. In: ter Haar Romeny B, Florack L, Koenderink J, Viergever M, editors. Scale-space theory in computer vision. Scale-space 1997. Lecture notes in computer science, vol. 1252. Berlin, Heidelberg: Springer; 1997. p. 164-75. DOI: 10.1007/3-540-63167-4_48

Razifar P, Sandström M, Schnieder H, Långström B, Maripuu E, Bengtsson E, et al. Noise correlation in PET, CT, SPECT and PET/CT data evaluated using autocorrelation function: a phantom study on data, reconstructed using FBP and OSEM. BMC Med Imaging. 2005 Dec;5(1):5-23. DOI: 10.1186/1471-2342-5-5

Brambilla M, Cannillo B, Dominietto M, Leva L, Secco C, Inglese E. Characterization of ordered-subsets expectation maximization with 3d post-reconstruction gauss filtering and comparison with filtered backprojection in99mTc SPECT. Ann Nucl Med. 2005 Apr;19(2):75-82. DOI: 10.1007/BF03027384

Morano GN, Seibyl JP. Technical overview of brain SPECT imaging: improving acquisition and processing of data. J Nucl Med Technol. 2003 Dec;31(4):191-5; quiz 202-3.

Beekman FJ, Slijpen ETP, Niessen WJ. Selection of task-dependent diffusion filters for the post-processing of SPECT images. Phys Med Biol. 1998 Jun 1;43(6):1713-30. DOI: 10.1088/0031-9155/43/6/024

Vija AH, Cachovan M. Multi-modal reconstruction in brain perfusion SPECT. J Nucl Med. 2019;60 (supplement 1):1362.

Li T, Wang Y. Multiscaled combination of MR and SPECT images in neuroimaging: A simplex method based variable-weight fusion. Comput Methods Programs Biomed. 2012 Jan;105(1):31-9. DOI: 10.1016/j.cmpb.2010.07.012

Liu Z, Song Y, Sheng VS, Xu C, Maere C, Xue K, et al. MRI and PET image fusion using the nonparametric density model and the theory of variable-weight. Comput Methods Programs Biomed. 2019 Jul;175:73-82. DOI: 10.1016/j.cmpb.2019.04.010

Novikova T, Nikolov N, Makeev S, Steblyuk V. SPECT in the diagnosis of cerebral changes in patients in the intermediate and long-term periods of combat explosive mild traumatic brain injury. Eurasian J Oncol. 2020;8(3):260-70. Available from:

Zheng W, Li S, Krol A, Ross Schmidtlein C, Zeng X, Xu Y. Sparsity promoting regularization for effective noise suppression in SPECT image reconstruction. Inverse Problems. 2019 Nov 1;35(11):115011. DOI: 10.1088/1361-6420/ab23da




How to Cite

Nikolov N, Makeyev S, Korostynska O, Novikova T, Kriukova Y. Gaussian Filter for Brain SPECT Imaging. Innov Biosyst Bioeng [Internet]. 2022Feb.16 [cited 2022Jun.28];6(1):4-15. Available from: