Comparison of Count Normalization Methods for Statistical Parametric Mapping Analysis Using a Digital Brain Phantom Obtained from Fluorodeoxyglucose-positron Emission Tomography

Document Type : Original Article

Authors

1 Department of Radiological Sciences, International University of Health and Welfare, School of Health Sciences

2 Department of Radiological Sciences, International Univercity of Health and Welfare

3 Department of Diagnostic Image Analysis, Course of Radiological Technology, Tohoku University Graduate School of Medicine

4 Department of Diagnostic Image Analysis, Tohoku University Graduate School of Medicine

Abstract

Objective(s): Alternative normalization methods were proposed to solve the biased information of SPM in the study of neurodegenerative disease. The objective of this study was to determine the most suitable count normalization method for SPM analysis of a neurodegenerative disease based on the results of different count normalization methods applied on a prepared digital phantom similar to one obtained using fluorodeoxyglucose-positron emission tomography (FDG-PET) data of a brain with a known neurodegenerative condition.
Methods: Digital brain phantoms, mimicking mild and intermediate neurodegenerative disease conditions, were prepared from the FDG-PET data of 11 healthy subjects. SPM analysis was performed on these simulations using different count normalization methods. 
Results: In the slight-decrease phantom simulation, the Yakushev method correctly visualized wider areas of slightly decreased metabolism with the smallest artifacts of increased metabolism. Other count normalization methods were unable to identify this slightly decreases and produced more artifacts. The intermediate-decreased areas were well visualized by all methods. The areas surrounding the grey matter with the slight decreases were not visualized with
the GM and VOI count normalization methods but with the Andersson. The Yakushev method well visualized these areas. Artifacts were present in all methods. When the number of reference area extraction was increased, the Andersson method better-captured the areas with decreased metabolism and reduced the artifacts of increased metabolism. In the Yakushev method, increasing the threshold for the reference area extraction reduced such artifacts.
Conclusion: The Yakushev method is the most suitable count normalization method for the SPM analysis of neurodegenerative disease.

Keywords

Main Subjects

References

1. Eckert T, Barnes A, Dhawan V, Frucht S, Gordon MF, Feigin AS, et al. FDG PET in the differential diagnosis of parkinsonian disorders. Neuroimage. 2005;26(3):912-21.
2. Eidelberg D, Moeller JR, Dhawan V, Spetsieris P, Takikawa S, Ishikawa T, et al. The metabolic topography of parkinsonism. J Cereb Blood Flow and Metab. 1994;14(5):783-801.
3. Hosey LA, Thompson JL, Metman LV, van den Munckhof P, Braun AR. Temporal dynamics of cortical and subcortical responses to apomorphine in Parkinson disease: an H2(15)O PET study. Clin Neuropharmacol. 2005;28(1):18-27.
4. Huang C, Tang C, Feigin A, Lesser M, Ma Y, Pourfar M, et al. Changes in network activity with the progression of Parkinson’s disease. Brain. 2007;130(Pt 7):1834-46.
5. Kawachi T, Ishii K, Sakamoto S, Sasaki M, Mori T, Yamashita F, et al. Comparison of the diagnostic performance of FDG-PET and VBM-MRI in very mild Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2006;33(7):801-9.
6. Hosokai Y, Nishio Y, Hirayama K, Takeda A, Ishioka T, Sawada Y, et al. Distinct patterns of regional cerebral glucose metabolism in Parkinson’s disease with and without mild cognitive impairment. Mov Disord. 2009;24(6):854-62.
7. Soonawala D, Amin T, Ebmeier KP, Steele JD, Dougall NJ, Best J, et al. Statistical parametric mapping of 99mTc HMPAO-SPECT images for the diagnosis of Alzheimer’s disease: normalizing to cerebellar tracer uptake. Neuroimage. 2002;17(3):1193-202.
8. Borghammer P, Østergaard K, Cumming P, Gjedde A, Rodell A, Hall N, et al. A deformation-based morphometry study of patients with early-stage Parkinson’s disease. Eur J Neurol. 2010;17(2):314-20.
9. Borghammer P. Perfusion and metabolism imaging studies in Parkinson’s disease. Eur J Neurol. 2012;17(2):314-20.
10. Adachi N, Watanabe T, Matsuda H, Onuma T. Hyperperfusion in the lateral temporal cortex the striatum and the thalamus during complex visual hallucination: single photon emission computed tomography findings in patients with Charles Bonnet syndrome. Psychiatry Clin Neurosci. 2000;54(2):157-62.
11. Borghammer P, Chakravarty M, Jonsdottir KY, Sato N, Matsuda H, Ito K, et al. Cortical hypometabolism and hypoperfusion in Parkinson’s disease is extensiveprobably even at early disease stages. Brain Struct Funct. 2010;214(4):303-17.
12. Scarmeas N, Habecka CG, Zarahna E, Anderson KE, Park A, Hilton J, et al. Covariance PET patterns in early Alzheimer’s disease and subjects with cognitive impairment but no dementia: utility in group discrimination and correlations with functional performance. Neuroimage. 2004;23(1):35-45.
13. Yakushev I, Hammers A, Fellgiebel A, Schmidtmann I, Scheurich A, Buchholz HG, et al. SPM-based count normalization provides excellent discrimination of mild Alzheimer’s disease and amnestic mild cognitive impairment from healthy aging. Neuroimage. 2009;44(1):43-50.
14. Borghammer P, Jonsdottir KY, Cumming P, Ostergaard K, Vang K, Ashkanian M, et al. Normalization in PET group comparison studies--the importance of a valid reference region. Neuroimage. 2008;40(2):529-40.
15. Andersson JL. How to estimate global activity independent of changes in local Activity. Neuroimage. 1997;6(4):237-44.
16. Buchsbaum MS, Buchsbaum BR, Hazlett EA, Haznedar MM, Newmark R, Tang CY, et al. Relative glucose metabolic rate higher in white matter in patients with schizophrenia. Am J Psychiatry. 2007;164(7):1072-81.
17. Videbech P, Ravnkilde B, Pedersen TH, Hartvig H, Egander A, Clemmensen K, et al. The Danish PET/ depression project: clinical symptoms and cerebral blood flow: a regions-of-interest analysis. Acta Psychiatr Scand. 2002;106(1):35-44.
18. Habeck C, Foster NL, Perneczky R, Kurz A, Alexopoulos P, Koeppe RA, et al. Multivariate and univariate neuroimaging biomarkers of Alzheimer’sdisease. Neuroimage. 2008;40(4):1503-15.
19. Borghammer P, Cumming P, Aanerud J, Gjedde A. Artefactual subcortical hyperperfusion in PET studies normalized to global mean: lessons from Parkinson’s disease. Neuroimage. 2009;45(2):249-57.
20. Borghammer P, Aanerud J, Gjedde A. Data-driven intensity normalization of PET group comparison studies is superior to global mean normalization. Neuroimage. 2009;46(4):981-8.
21. Maeda H, Yamaki N, Azuma M. Development of the software package of the nuclear medicine data processor for education and research. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2012;68(3):299-306.
22. Worsley KJ, Liao CH, Aston J, Petre V, Duncan GH, Morales F, et al. A general statistical analysis for fMRI data. Neuroimage. 2002;15(1):1-15.
23. Worsley KJ, Marrett S, Neelin P, Vandal AC, Friston KJ, Evans AC. A unified statistical approach for determining significant signals in mages of cerebral activation. Hum Brain Map. 1996;4(1):58-73.
Asia Oceania Journal of Nuclear Medicine and Biology
Volume 7, Issue 1
January 2019
Pages 58-70

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