Skip to main content

Advertisement

SpringerLink
Log in

A new approach for improving diagnostic accuracy in Alzheimer’s disease and frontal lobe dementia utilising the intrinsic properties of the SPET dataset

  • Original Article
  • Published:
European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

An Erratum to this article was published on 19 December 2003

Abstract

Alzheimer’s disease (AD) and frontal lobe dementia (FLD) show characteristic patterns of regional cerebral blood flow (rCBF). However, these patterns may overlap with those observed in the aging brain in elderly normal individuals. The aim of this study was to develop a new method for better classification and recognition of AD and FLD cases as compared with normal controls. Forty-six patients with AD, 7 patients with FLD and 34 normal controls (CTR) were included in the study. rCBF was assessed by technetium-99m hexamethylpropylene amine oxime and a three-headed single-photon emission tomography (SPET) camera. A brain atlas was used to define volumes of interest (VOIs) corresponding to the brain lobes. In addition to conventional image processing methods, based on count density/voxel, the new approach also analysed other intrinsic properties of the data by means of gradient computation steps. Hereby, five factors were assessed and tested separately: the mean count density/voxel and its histogram, the mean gradient and its histogram, and the gradient angle co-occurrence matrix. A feature vector concatenating single features was also created and tested. Preliminary feature discrimination was performed using a two-sided t-test and a K-means clustering was then used to classify the image sets into categories. Finally, five-dimensional co-occurrence matrices combining the different intrinsic properties were computed for each VOI, and their ability to recognise the group to which each individual scan belonged was investigated. For correct classification of the AD-CTR groups, the gradient histogram in the parieto-temporal lobes was the most useful single feature (accuracy 91%). FLD and CTR were better classified by the count density/voxel histogram (frontal and occipital lobes) and by the mean gradient (frontal, temporal and parietal lobes, accuracy 98%). For AD and FLD the count density/voxel histogram in the frontal, parietal and occipital lobes classified the groups with the highest accuracy (85%). The concatenated joint feature correctly classified 96% of the AD-CTR, 98% of the FLD-CTR and 94% of the AD-FLD cases. 5D co-occurrence matrices correctly recognised 98% of the AD-CTR cases, 100% of the FLD-CTR cases and 98% of the AD-FLD cases. The proposed approach classified and diagnosed AD and FLD patients with higher accuracy than conventional analytical methods used for rCBF-SPET. This was achieved by extracting from the SPET data the intrinsic information content in each of the selected VOIs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1a–c.

Similar content being viewed by others

References

  1. Wallin A, Brun A, Gustafson L and the Swedish Consensus Group. Swedish Consensus on dementia disease. Acta Neurol Scand 1994; 90 (Suppl):S1–S31.

    Google Scholar 

  2. Neary D, Snowden JS, Shields RA, et al. Single photon emission tomography using99mTc-HM-PAO in the investigation of dementia. J Neurol Neurosurg Psychiatry 1987; 50:1101–1109.

    CAS  PubMed  Google Scholar 

  3. Smith FW, Gemmell HG, Sharp PF. The use of99Tcm-HM-PAO for the diagnosis of dementia. Nucl Med Commun 1987; 8:525–533.

    CAS  PubMed  Google Scholar 

  4. Hunter R, McLuskie R, Wyper D, Patterson J, Christie JE, Brooks DN, McCulloch J, Fink G, Goodwin GM. The pattern of function-related regional cerebral blood flow investigated by single photon emission tomography with99mTc-HMPAO in patients with presenile Alzheimer’s disease and Korsakoff’s psychosis. Psychol Med 1989; 19:847–855.

    CAS  PubMed  Google Scholar 

  5. Jagust WJ, Eberling JL, Reed BR, Mathis CA, Budinger TF. Clinical studies of cerebral blood flow in Alzheimer’s disease. Ann N Y Acad Sci 1997; 826:254–262.

    CAS  PubMed  Google Scholar 

  6. Neary D, Snowden JS, Gustafson L, et al. Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. Neurology 1998; 51:1546–1554.

    CAS  PubMed  Google Scholar 

  7. Jagust WJ, Reed BR, Seab JP, Kramer JH, Budinger TF. Clinical-physiologic correlates of Alzheimer’s disease and frontal lobe dementia. Am J Physiol Imaging 1989; 4:89–96.

    CAS  PubMed  Google Scholar 

  8. Julin P, Wahlund LO, Basun H, Persson A, Måre K, Rudberg U. Clinical diagnosis of frontal lobe dementia and Alzheimer’s disease: relation to cerebral perfusion, brain atrophy and electroencephalography. Dement Geriatr Cogn Disord 1995; 6:142–147.

    CAS  Google Scholar 

  9. Risberg J, Gustafson L. Regional cerebral blood flow measurements in the clinical evaluation of demented patients. Dement Geriatr Cogn Disord 1997; 8:92–97.

    CAS  PubMed  Google Scholar 

  10. Holman BL, Johnson KA, Gerada B, Carvalho PA, Satlin A. The scintigraphic appearance of Alzheimer’s disease: a prospective study using technetium-99m-HMPAO SPECT. J Nucl Med 1992; 33:181–185.

    CAS  PubMed  Google Scholar 

  11. Waldemar G, Bruhn P, Kristensen M, Johnsen A, Paulson OB, Lassen NA. Heterogeneity of neocortical cerebral blood flow deficits in dementia of the Alzheimer type: a [99mTc]-d,l-HMPAO SPECT study. J Neurol Neurosurg Psychiatry 1994; 57:285–295.

    CAS  PubMed  Google Scholar 

  12. Frisoni GB, Pizzolato G, Bianchetti A, et al. Single photon emission computed tomography with [99Tc]-HM-PAO and [123I]-IBZM in Alzheimer’s disease and dementia of frontal type: preliminary results. Acta Neurol Scand 1994; 89:199–203.

    CAS  PubMed  Google Scholar 

  13. Ryding E. SPECT measurements of brain function in dementia; a review. Acta Neurol Scand 1996; 168 (Suppl):S54–S58.

    Google Scholar 

  14. Syed GMS, Eagger S, O’Brien J, Barrett JJ, Levy R. Patterns of regional cerebral blood flow in Alzheimer’s disease. Nucl Med Commun 1992; 13:656–663.

    CAS  PubMed  Google Scholar 

  15. O’Mahony, Coffey J, Murphy J, et al. The discriminant value of semiquantitative SPECT data in mild Alzheimer’s disease. J Nucl Med 1994; 35:1450–1455.

    CAS  PubMed  Google Scholar 

  16. Houston AS, Kemp PM, Macleod MA, Francis JR, Colohan HA, Matthews HP. Use of significance image to determine patterns of cortical blood flow abnormality in pathological and at-risk groups. J Nucl Med 1998; 39:425–430.

    CAS  PubMed  Google Scholar 

  17. Hamilton D, O’Mahony D, Coffey J, Murphy J, O’Hare N, Freyne P, Walsh B, Coakley D. Classification of mild Alzheimer’s disease by artificial neural network analysis of SPET data. Nucl Med Commun 1997; 18:805–810.

    CAS  PubMed  Google Scholar 

  18. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12:189–198.

    CAS  PubMed  Google Scholar 

  19. Pagani M, Salmaso D, Jonsson C, et al. Regional cerebral blood flow as assessed by principal component analysis and99mTc-HMPAO SPET in healthy subjects at rest: normal distribution and effect of age and gender. Eur J Nuc Med 2002; 29:67–75.

    Article  CAS  Google Scholar 

  20. Chang L-T. A method for attenuation correction in radionuclide computed tomography. IEEE Trans Nucl Sci 1978; 25:638–643.

    Google Scholar 

  21. Greitz T, Bohm C, Holte S, Eriksson L. A computerized brain atlas: construction, anatomical content, and some applications. J Comput Assist Tomogr 1991; 15:26–38.

    CAS  PubMed  Google Scholar 

  22. Thurfjell L, Bohm C, Bengtsson E. CBA—an atlas based software tool used to facilitate the interpretation of neuroimaging data. Comput Methods Programs Biomed 1995; 4:51–71.

    Article  Google Scholar 

  23. Andersson JLR, Thurfjell L. Implementation and validation of a fully automatic system for intra- and inter-individual registration of PET brain scans. J Comput Assist Tomogr 1997; 21:136–144.

    CAS  PubMed  Google Scholar 

  24. Thurfjell L, Bohm C, Greitz T, Eriksson L. Transformations and algorithms in a computerized brain atlas. IEEE Trans Nucl Sci 1993; 40:1187–1191.

    Article  CAS  Google Scholar 

  25. Gonzales RC, Woods RE. Digital image processing. Reading: Addison-Wesley; 1992:508–510.

  26. Kovalev VA, Kruggel F, Gertz H-J, von Cramon DY. Three dimensional texture analysis of MRI brain datasets. IEEE Trans Med Imaging 2001; 20:424–433.

    Article  CAS  PubMed  Google Scholar 

  27. Zucker SW, Hummel RA. A 3D edge operator. IEEE Trans PAMI 1981; 3:324–331.

    Google Scholar 

  28. Kovalev V, Thurfjell L, Lundqvist R, Pagani M. Classification of SPECT scans of Alzheimer’s disease and frontal lobe dementia based on intensity and gradient information. In: Medical image understanding and analysis. MIUA’99. Oxford, 19–20 July. 1999:69–72.

  29. Seber GAF. Multivariate analysis. New York: Wiley, 1984.

  30. Pagani M, Salmaso D, Ramström C, et al. Mapping pathological99mTc-HMPAO uptake in Alzheimer’s disease and frontal lobe dementia with SPECT. Dement Geriatr Cogn Disord 2001; 12:177–184.

    CAS  PubMed  Google Scholar 

  31. Starkstein SE, Migliorelli R, Teson A, Sabe L, Vazquez S, Turjanski M, Robinson RG, Leiguarda R. Specificity of changes in cerebral blood flow in patients with frontal lobe dementia. J Neurol Neurosurg Psychiatry 1994; 57:790–796.

    CAS  PubMed  Google Scholar 

  32. Minoshima S, Frey K, Koeppe R, Foster N, Kuhl D. A diagnostic approach in Alzheimer’s disease using three-dimensional stereotactic surface projections of fluorine-18-FDG PET. J Nucl Med 1995; 36:1238–1248.

    CAS  PubMed  Google Scholar 

  33. Bartenstein P, Minoshima S, Hirsch C, et al. Quantitative assessment of cerebral blood flow in patients with Alzheimer’s disease by SPECT. J Nucl Med 1997; 38:1095–1101.

    CAS  PubMed  Google Scholar 

  34. Elgh E, Sundstrom T, Nasman B, Ahlstrom R, Nyberg L. Memory functions and rCBF99mTc HMPAO SPET: developing diagnostics in Alzheimer’s disease. Eur J Nucl Med Mol Imaging 2002; 29:1140–1148.

    Article  CAS  PubMed  Google Scholar 

  35. Varrone A, Pappata S, Caraco C, Soricelli A, Milan G, Quarantelli M, Alfano B, Postiglione A, Salvatore M. Voxel-based comparison of rCBF SPET images in frontotemporal dementia and Alzheimer’s disease highlights the involvement of different cortical networks. Eur J Nucl Med Mol Imaging 2002; 29:1447–1454.

    Article  PubMed  Google Scholar 

  36. Borght T, Minoshima S, Giordani B, et al. Cerebral metabolic differences in Parkinson’s and Alzheimer’s disease matched for dementia severity. J Nucl Med 1997; 38:797–801.

    PubMed  Google Scholar 

  37. Imran MB, Kawashima R, Awata S, et al. Parametric mapping of cerebral blood flow deficits in Alzheimer’s disease: a SPECT study using HMPAO and image standardization technique. J Nucl Med 1999; 40:244–249.

    CAS  PubMed  Google Scholar 

  38. Van Laere K, Versijpt J, Audenaert K, Koole Goethals I, Achten E, Dierckx R.99mTc-ECD brain perfusion SPET: variability, asymmetry and effect of age and gender in healthy adults. Eur J Nucl Med 2001; 28:873–887.

    PubMed  Google Scholar 

  39. Van Gool WA, Walstra GJ, Teunisse S, Van der Zant FM, Weinstein HC, Van Royen EA. Diagnosing Alzheimer’s disease in elderly, mildly demented patients: the impact of routine single photon emission computed tomography. J Neurol 1995; 242:401–405.

    PubMed  Google Scholar 

  40. Read SL, Miller BL, Mena I, Kim R, Itabashi H, Darby A. SPECT in dementia: clinical and pathological correlation. J Am Geriatr Soc 1995; 43:1243–1247.

    CAS  PubMed  Google Scholar 

  41. van Dyck CH, Lin CH, Smith EO, Wisniewski G, Cellar J, Robinson R, Narayan M, Bennett A, Delaney RC, Bronen RA, Hoffer PB. Comparison of technetium-99m-HMPAO and technetium-99m-ECD cerebral SPECT images in Alzheimer’s disease. J Nucl Med 1996; 37:1749–1755.

    CAS  PubMed  Google Scholar 

  42. Masterman DL, Mendez MF, Fairbanks LA, Cummings JL. Sensitivity, specificity, and positive predictive value of technetium 99-HMPAO SPECT in discriminating Alzheimer’s disease from other dementias. J Geriatr Psychiatry Neurol 1997; 10:15–21.

    CAS  PubMed  Google Scholar 

  43. Bonte FJ, Weiner MF, Bigio EH, White CL 3rd. Brain blood flow in the dementias: SPECT with histopathologic correlation in 54 patients. Radiology 1997; 202:793–797.

    CAS  PubMed  Google Scholar 

  44. Jobst KA, Barnetson LP, Shepstone BJ. Accurate prediction of histologically confirmed Alzheimer’s disease and the differential diagnosis of dementia: the use of NINCDS-ADRDA and DSM-III-R criteria, SPECT, X-ray CT, and Apo E4 in medial temporal lobe dementias. Oxford Project to Investigate Memory and Aging. Int Psychogeriatr 1998; 10:271–302.

    CAS  PubMed  Google Scholar 

  45. Obrien JT, Ames D, Desmond P, Lichtenstein M, Binns D, Schweitzer I, Davis S, Tress B. Combined magnetic resonance imaging and single-photon emission tomography scanning in the discrimination of Alzheimer’s disease from age-matched controls. Int Psychogeriatr 2001; 13:149–161.

    CAS  PubMed  Google Scholar 

  46. Varma AR, Adams W, Lloyd JJ, Carson KJ, Snowden JS, Testa HJ, Jackson A, Neary D. Diagnostic patterns of regional atrophy on MRI and regional cerebral blood flow change on SPECT in young onset patients with Alzheimer’s disease, frontotemporal dementia and vascular dementia. Acta Neurol Scand 2002; 105:261–269.

    Article  CAS  PubMed  Google Scholar 

  47. Jagust W, Thisted R, Devous MD Sr, Van Heertum R, Mayberg H, Jobst K, Smith AD, Borys N. SPECT perfusion imaging in the diagnosis of Alzheimer’s disease: a clinical-pathologic study. Neurology 2001; 56:950–956.

    PubMed  Google Scholar 

  48. Silverman DH, Gambhir SS, Huang HW, Schwimmer J, Kim S, Small GW, Chodosh J, Czernin J, Phelps ME. Evaluating early dementia with and without assessment of regional cerebral metabolism by PET: a comparison of predicted costs and benefits. J Nucl Med 2002; 43:253–266.

    PubMed  Google Scholar 

Download references

Acknowledgements

This study was supported by grants from the “Dipartimento per le Attivita’ Internazionali, Servizio I”, the Italian National Research Council (CNR), the Swedish Medical Research Council (MFR), The Swedish Institute, The Alzheimer Foundation and the Swedish Foundation for Strategic Research through the Visual Information Technology Program, Sweden.

Author information

Authors and Affiliations

  1. Institute of Cognitive Sciences and Technology, CNR, Viale Marx 15, 00137, Rome, Italy

    Marco Pagani

  2. Section of Nuclear Medicine, Karolinska Hospital/Institute, Stockholm, Sweden

    Marco Pagani, Hans Jacobsson & Stig A. Larsson

  3. Institute of Engineering Cybernetics, Belarus National Academy of Sciences, Minsk, Belarus

    Vassili A. Kovalev

  4. Max-Planck Institute of Cognitive Neuroscience, Leipzig, Germany

    Vassili A. Kovalev

  5. Applied Medical Imaging, Uppsala, Sweden

    Roger Lundqvist & Lennart Thurfjell

  6. Department of Radiology, Karolinska Hospital, Stockholm, Sweden

    Hans Jacobsson

Authors
  1. Marco Pagani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vassili A. Kovalev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roger Lundqvist
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hans Jacobsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stig A. Larsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lennart Thurfjell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marco Pagani.

Additional information

An erratum to this article can be found at http://dx.doi.org/10.1007/s00259-003-1434-4

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pagani, M., Kovalev, V.A., Lundqvist, R. et al. A new approach for improving diagnostic accuracy in Alzheimer’s disease and frontal lobe dementia utilising the intrinsic properties of the SPET dataset. Eur J Nucl Med Mol Imaging 30, 1481–1488 (2003). https://doi.org/10.1007/s00259-003-1196-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-003-1196-z

Keywords

Search

Navigation