#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Bone SPECT image reconstruction using deconvolution and wavelet decomposition


Authors: Jaroslav Ptáček 1,2;  Lenka Henzlová 1;  Pavel Koranda 1
Authors‘ workplace: Klinika nukleární medicíny, LF UP a FN Olomouc 1;  Oddělení lékařské fyziky a radiační ochrany, LF UP a FN Olomouc 2
Published in: NuklMed 2015;4:42-50
Category: Original Article

Overview

Purpose:
The aim was to develop a new algorithm for bone SPECT image reconstruction and to compare its results with a standard OSEM and resolution recovery (RR) reconstructions.

Materials and methods:
The algorithm uses the Lucy-Richardson deconvolution and logarithmic image processing to enhance the projections. A modification of the wavelet decomposition coefficients was used to suppress the noise. The comparison with vendor software was done using a phantom study, utilizing Signal-to-Noise ratio (SNR), Signal-to-Background ratio (SBR) and spatial resolution. In clinical studies, visual assessment of changes in contrast, spatial resolution and lesion detectability were evaluated.

Results:
A change in the SNR (from − 4 to 40 %), an increase in the SBR (from 19 to 40 %), a minor improvement in spatial resolution and a similar noise level were observed in a phantom study compared to standard OSEM. A worse spatial resolution, a decrease in the SNR, but only a 3 to 13 % lower SBR were recorded in comparison with the vendor supplied resolution recovery (RR) algorithm. The proposed algorithm creates better contrast patient images leading to better lesion detectability compared to clinically used OSEM. More than half of obtained images showed better contrast and nearly half of them has better lesion detectability compared to RR.

Conclusion:
The proposed algorithm works well compared to the standard OSEM, the results of the comparison with RR and noise suppression algorithms were not so promising, but still it can be used with only a slight decrease in the SBR.

Key Words:
algorithm, SPECT reconstruction, OSEM, RR


Sources

1. Bombardieri E. Aktolun C, Baum RP et al. Bone Scintigraphy Procedures Guidelines for Tumour Imaging [online] EANM 2003. [cit. 2013-03-16]. Dostupné na: http://www.eanm.org/publications/guidelines/gl_onco_bone.pdf0

2. Knoll P, Kotalova D, Köchle G et al. Comparison of advanced iterative reconstruction methods for SPECT/CT, Z Med. Phys. 2012; 22:58-69

3. Zaidi H ed., Quantitative Analysis in Nuclear Medicine Imaging, Springer, 2006

4. Richardson WH., Bayesian-Based Iterative Method of Image Restoration, J Opt Soc Am 1972;62:55–59. doi:10.1364/JOSA.62.000055

5. Lucy LB. An iterative technique for the rectification of observed distributions. Astronomical Journal 1974;79:745–754. doi:10.1086/111605

6. Pinoli JC. The logarithmic image processing model: connections with human brightness perception and contrast estimators. J Math Imaging Vis. 1997;7:341-358

7. Chen GY, Bui TD, Krzyzak A. Image denoising with neighbour dependency and customized wavelet and threshold. Pattern Recognition 2005;38:115-124

8. Fernandes M, Gavet Y, Pinoli JC. Improving focus measurement using logarithmic image processing. Journal of Microscopy 2010;242:228-2419

9. Deng G, Pinoli JC. Differentiation-based edge detection using the logarithmic image processing model. J Math Imaging Vis. 1998;8:161-180

10. Deng G, Cahill W, Tobin GR. The study of logarithmic image processing model and its application to image enhancement. IEEE Trans Image Process. 1995;4:506-512

11. Navarro L, Courbebaisse G. Symmetric Logarithmic Image Processing Model Application to Laplacian Edge Detection. [online]. 2012. [cit. 2013-04-24]. Dostupné na: http://hal.archives-ouvertes.fr/docs/00/71/19/04/PDF/Article.pdf

12. Chang SG, Bin Y, Vetterli M. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans Image Proc 2000;9:1532-1546

13. Green GC. Wavelet-based denoising of Cardiac PET data (MASc Thesis). [online]. 2005. [cit. 2013-04-24]. Dostupné na: http://www.sce.carleton.ca/~geogreen/GeoffGreenMAScThesis.pdf

14. Fugal DL. Conceptual Wavelets In Digital Signal Processing – An In-Depth, Practical Approach for the Non-Mathematician, Space & Signal Technologies LLC, [online]. 2009. [cit. 2013-04-24]. Dostupné na: www.conceptualwavelets.com

15. Turkheimer FE, Brett M, Visvikis D et al. Multiresolution analysis of emission tomography images in the wavelet domain. J Cerebr Blood F Met 1999;19:1189-1208

16. Nowak RD, Baraniuk RG. Wavelet-domain filtering for photon imaging systems. IEEE Trans Image Proc 1999;8:666-678

17. Koren I, Laine A. A discrete dyadic wavelet transform for multidimensional feature analysis. In: M. Akay, editor. Time frequency and wavelets in biomedical signal processing. New Yor, IEEE Press, 1998, pp. 425-448

18. Cai TT, Silverman BW. Incorporating information on neighbouring coefficients into wavelet estimation. Sankhya Ser B 2001;63:127-148

19. Mohideen SK, Perumal SA, Sathik MM. Image de-noising using discrete wavelet transform. Int J Comput Sci Netw Sec 2008;8:213-216

20. Shih YY, Chen JC, Liu RS. Development of wavelet de-noising technique for PET images. Comput Med Imag Grap 2005;29:297-304

21. Lin JW, Laine AF. Improving PET-based physiological quantification through methods of wavelet denoising. IEEE Trans Biomed Eng 2001;48:202-212

22. Cohen J. A coefficient of agreement for nominal scales. Educational Psychological Meas 1960;20:37-46

23. Cohen J. Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psych Bull 1968;70:213-220

24. Gardner MJ, Altman DG. Statistics with confidence, BMJ; London, 1989

25. Teo BK, Seo Y, Bachatach SL et al. Partial-Volume Correction in PET: Validation of an Iterative Postreconstruction Method with Phantom and Patient Data, J Nucl Med 2007;802-810

26. Rizzo G, Castiglioni I, Russo G et al. Using Deconvolution to Improve PET spatial Resolution in OSEM Iterative Reconstruction. Methods Inf Med 2007;46:231-235

27. Boussion N, Cheze Le Rest C, Hatt M et al. Incorporation of wavelet-based denoising in iterative deconvolution for partial volume correction in whole-body PET imaging. Eur J Nucl Med Mol Imaging 2009;36:1064-1075

28. Turkheimer FE, Boussion N, Anderson AN et al. PET Image Denoising Using a Synergistic Multiresolution Analysis of Structural (MRI/CT) and Funtional Datasets, J Nucl Med 2008; 9:657-666

29. Boussion N, Hatt M, Lamare F et al. A multiresolution image based approach for correction of partial volume effects in emission tomography. Phys Med Biol 2006;51:1857-1876

30. Le Pogam A, Hatt M, Descourt P et al. Evaluation of a 3D local multiresolution algorithm for the correction of partial volume effects in positron emission tomography, Med Phys 2011;38:4920-4923

Labels
Nuclear medicine Radiodiagnostics Radiotherapy
Login
Forgotten password

Enter the email address that you registered with. We will send you instructions on how to set a new password.

Login

Don‘t have an account?  Create new account

#ADS_BOTTOM_SCRIPTS#