Skip to main content

Advertisement

Log in

The importance of appropriate partial volume correction for PET quantification in Alzheimer’s disease

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

Abstract

Purpose

Alzheimer’s disease (AD) is the most common form of dementia. Clinically, it is characterized by progressive cognitive and functional impairment with structural hallmarks of cortical atrophy and ventricular expansion. Amyloid plaque aggregation is also known to occur in AD subjects. In-vivo imaging of amyloid plaques is now possible with positron emission tomography (PET) radioligands. PET imaging suffers from a degrading phenomenon known as the partial volume effect (PVE). The quantitative accuracy of PET images is reduced by PVEs primarily due to the limited spatial resolution of the scanner. The degree of PVE is influenced by structure size, with smaller structures tending to suffer from more severe PVEs such as atrophied grey matter regions. The aims of this paper were to investigate the effect of partial volume correction (PVC) on the quantification of amyloid PET and to highlight the importance of selecting an appropriate PVC technique.

Methods

An improved PVC technique, region-based voxel-wise (RBV) correction, was compared against existing Van-Cittert (VC) and Müller-Gärtner (MG) methods using amyloid PET imaging data. Digital phantom data were produced using segmented MRI scans from a control subject and an AD subject. Typical tracer distributions were generated for each of the phantom anatomies. Also examined were 70 clinical PET scans acquired using [18F]flutemetamol. Volume of interest (VOI) analysis was performed for corrected and uncorrected images.

Results

PVC was shown to improve the quantitative accuracy of regional analysis performed on amyloid PET images. Of the corrections applied, VC deconvolution demonstrated the worst recovery of grey matter values. MG PVC was shown to induce biases in some grey matter regions due to grey matter variability. In addition, white matter variability was shown to influence the accuracy of MG PVC in cortical grey matter and also cerebellar grey matter, a typical reference region for amyloid PET normalization in sporadic AD. RBV was shown to be more accurate than MG in terms of grey matter and white matter uptake. An increase in within-group variability after PVC was observed and is believed to be a genuine, more accurate representation of the data rather than a correction-induced error. The standardized uptake value ratio (SUVR) threshold for classifying subjects as either amyloid-positive or amyloid-negative was found to be 1.64 in the uncorrected dataset, rising to 2.25 after PVC.

Conclusion

Care should be taken when applying PVC to amyloid PET images. Assumptions made in existing PVC strategies can induce biases that could lead to erroneous inferences about uptake in certain regions. The proposed RBV PVC technique accounts for within-compartment variability, with the potential to reduce errors of this kind.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Sloane PD, Zimmerman S, Suchindran C, Reed P, Wang L, Boustani M, et al. The public health impact of Alzheimer’s disease, 2000–2050: potential implication of treatment advances. Annu Rev Public Health. 2002;23:213–31.

    Article  PubMed  Google Scholar 

  2. Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP, et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol. 2004;55:306–19.

    Article  PubMed  CAS  Google Scholar 

  3. Wong DF, Rosenberg PB, Zhou Y, Kumar A, Raymont V, Ravert HT, et al. In vivo imaging of amyloid deposition in Alzheimer disease using the radioligand 18F-AV-45 (Flobetapir F 18). J Nucl Med. 2010;51:913–20.

    Article  PubMed  CAS  Google Scholar 

  4. Rowe CC, Ackerman U, Browne W, Mulligan R, Pike KL, O’Keefe G, et al. Imaging of amyloid β in Alzheimer’s disease with 18F-BAY94-9172, a novel PET tracer: proof of mechanism. Lancet Neurol. 2008;7:129–35.

    Article  PubMed  CAS  Google Scholar 

  5. Vandenberghe R, Van Laere K, Ivanoiu A, Salmon E, Bastin C, Triau E, et al. 18F-flutemetamol amyloid imaging in Alzheimer disease and mild cognitive impairment: a phase 2 trial. Ann Neurol. 2010;68:319–29

    Article  PubMed  Google Scholar 

  6. Soret M, Bacharach SL, Buvat I. Partial-volume effect in PET tumor imaging. J Nucl Med. 2007;48:932–45.

    Article  PubMed  Google Scholar 

  7. Frisoni G, Laakso M, Beltramello A, Geroldi C, Bianchetti A, Soininen H, et al. Hippocampal and entorhinal cortex atrophy in frontotemporal dementia and Alzheimer’s disease. Neurology. 1999;52:91–100.

    PubMed  CAS  Google Scholar 

  8. Thompson PM, Hayashi KM, de Zubicaray G, Janke AL, Rose SE, Semple J, et al. Dynamics of gray matter loss in Alzheimer’s disease. J Neurosci. 2003;23:994–1005.

    PubMed  CAS  Google Scholar 

  9. Jack CR, Shiung MM, Weigand SD, O’Brien PC, Gunter JL, Boeve BF, et al. Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI. Neurology. 2005;65:1227–31.

    Article  PubMed  Google Scholar 

  10. Tohka J, Reilhac A. Deconvolution-based partial volume correction in Raclopride-PET and Monte Carlo comparison to MR-based method. Neuroimage. 2008;39:1570–84.

    Article  PubMed  Google Scholar 

  11. Mawlawi O, Podoloff DA, Kohlmyer S, Williams JJ, Stearns CW, Culp RF, et al. Performance characteristics of a newly developed PET/CT scanner using NEMA standards in 2D and 3D modes. J Nucl Med. 2004;45:1734–42.

    PubMed  Google Scholar 

  12. Teo BK, Seo Y, Bacharach SL, Carrasquillo JA, Libutti SK, Shukla H, et al. Partial-volume correction in PET: validation of an iterative postreconstruction method with phantom and patient data. J Nucl Med. 2007;48:802–10.

    PubMed  Google Scholar 

  13. Aston JA, Cunningham VJ, Asselin MC, Hammers A, Evans AC, Gunn RN. Positron emission tomography partial volume correction: estimation and algorithms. J Cereb Blood Flow Metab. 2002;22:1019–34.

    Article  PubMed  Google Scholar 

  14. Meltzer CC, Leal JP, Mayberg HS, Wagner HNJ, Frost JJ. Correction of PET data for partial volume effects in human cerebral cortex by MR imaging. J Comput Assist Tomogr. 1990;14:561–70.

    Article  PubMed  CAS  Google Scholar 

  15. Müller-Gärtner HW, Links JM, Prince JL, Bryan RN, McVeigh E, Leal JP, et al. Measurement of radiotracer concentration in brain gray matter using positron emission tomography: MRI-based correction for partial volume effects. J Cereb Blood Flow Metab. 1992;12:571–83.

    Article  PubMed  Google Scholar 

  16. Rousset OG, Ma Y, Evans AC. Correction for partial volume effects in PET: principle and validation. J Nucl Med. 1998;39:904–11.

    PubMed  CAS  Google Scholar 

  17. Lucy L. An iterative technique for the rectification of observed distributions. Astron J. 1974;79:745–54.

    Article  Google Scholar 

  18. Tohka J, Reilhac A. A Monte Carlo study of deconvolution algorithms for partial volume correction in quantitative PET. Nuclear Science Symposium Conference Record, 2006. IEEE, vol 6, p. 3339–3345. doi:10.1109/NSSMIC.2006.353719

  19. Boussion N, Hatt M, Lamare F, Bizais Y, Turzo A, Rest C, et al. A multiresolution image based approach for correction of partial volume effects in emission tomography. Phys Med Biol. 2006;51:1857–76.

    Article  PubMed  CAS  Google Scholar 

  20. Le Pogam A, Boussion N, Hatt M, Turkheimer F, Prunier-Aesch C, Guilloteau D, et al. A 3D multi resolution local analysis approach for correction of partial volume effects in emission tomography. Nuclear Science Symposium Conference Record, 2008. NSS' 08, IEEE, p. 5300–5303. doi:10.1109/NSSMIC.2008.4774429

  21. Shidahara M, Tsoumpas C, Hammers A, Boussion N, Visvikis D, Suhara T, et al. Functional and structural synergy for resolution recovery and partial volume correction in brain PET. Neuroimage. 2009;44:340–8.

    Article  PubMed  Google Scholar 

  22. Alessio AM, Kinahan PE. Improved quantitation for PET/CT image reconstruction with system modeling and anatomical priors. Med Phys. 2006;33:4095–103.

    Article  PubMed  Google Scholar 

  23. Rizzo G, Castiglioni I, Russo G, Tana MG, Dell’Acqua F, Gilardi MC, et al. Using deconvolution to improve PET spatial resolution in OSEM iterative reconstruction. Method Inform Med. 2007;46:231–5.

    CAS  Google Scholar 

  24. Kirov AS, Piao JZ, Schmidtlein CR. Partial volume effect correction in PET using regularized iterative deconvolution with variance control based on local topology. Phys Med Biol. 2008;53:2577–91.

    Article  PubMed  CAS  Google Scholar 

  25. Rousset OG, Ma Y, Wong DF, Evans AC. Pixel- versus region-based partial volume correction in PET. In: Carson RE, Daube-Witherspoon ME, Herscovitch P, editors. Quantitative functional brain imaging with positron emission tomography. San Diego: Academic Press; 1998. p. 67–75.

    Chapter  Google Scholar 

  26. Quarantelli M, Berkouk K, Prinster A, Landeau B, Svarer C, Balkay L, et al. Integrated software for the analysis of brain PET/SPECT studies with partial-volume-effect correction. J Nucl Med. 2004;45:192–201.

    PubMed  Google Scholar 

  27. Yang J, Huang S, Mega M, Lin K, Toga A, Small G, et al. Investigation of partial volume correction methods for brain FDG PET studies. IEEE Trans Nucl Sci. 1996;43:3322–7.

    Article  Google Scholar 

  28. Erlandsson K, Wong AT, van Heertum R, Mann JJ, Parsey RV. An improved method for voxel-based partial volume correction in PET and SPECT. Neuroimage. 2006;31:T84.

    Article  Google Scholar 

  29. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33:341–55.

    Article  PubMed  CAS  Google Scholar 

  30. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Ségonne F, Salat DH, et al. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004;14:11–22.

    Article  PubMed  Google Scholar 

  31. Braak H, Braak E. Staging of Alzheimer’s disease-related neurofibrillary changes. Neurobiol Aging. 1995;16:271–8.

    Article  PubMed  CAS  Google Scholar 

  32. Ourselin S, Roche A, Subsol G, Pennec X, Ayache N. Reconstructing a 3D structure from serial histological sections. Image Vision Comput. 2001;19:25–31.

    Article  Google Scholar 

  33. Li Y, Rinne JO, Mosconi L, Pirraglia E, Rusinek H, DeSanti S, et al. Regional analysis of FDG and PIB-PET images in normal aging, mild cognitive impairment, and Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2008;35:2169–81.

    Article  PubMed  Google Scholar 

  34. Boussion N, Cheze Le Rest C, Hatt M, Visvikis D. Incorporation of wavelet-based denoising in iterative deconvolution for partial volume correction in whole-body PET imaging. Eur J Nucl Med Mol Imaging. 2009;36:1064–75.

    Article  PubMed  CAS  Google Scholar 

  35. Ercoli LM, Siddarth P, Kepe V, Miller KJ, Huang SC, Cole GM, et al. Differential FDDNP PET patterns in nondemented middle-aged and older adults. Am J Geriatr Psychiatry. 2009;17:397–406.

    Article  PubMed  Google Scholar 

  36. Shin J, Lee SY, Kim SJ, Kim SH, Cho SJ, Kim YB. Voxel-based analysis of Alzheimer’s disease PET imaging using a triplet of radiotracers: PIB, FDDNP, and FDG. Neuroimage. 2010;52:488–96.

    Article  PubMed  Google Scholar 

  37. Frouin V, Comtat C, Reilhac A, Gregoire MC. Correction of partial-volume effect for PET striatal imaging: fast implementation and study of robustness. J Nucl Med. 2002;43:1715–26.

    PubMed  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Dr. Matt Clarkson (Dementia Research Centre, UCL) for his help with the FreeSurfer segmentations and Christopher Buckley (GE Healthcare, Amersham, UK) for proofreading data collection related information. R.V. is a senior clinical investigator of the Research Fund Flanders (FWO). B.T. is supported by a CASE studentship with the Engineering and Physical Sciences Research Council (EPSRC) and GE Healthcare. The authors also wish to acknowledge that UCLH/UCL receives a proportion of its funding from the Department of Health’s NIHR Biomedical Research Centre’s funding scheme.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Benjamin A. Thomas.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Thomas, B.A., Erlandsson, K., Modat, M. et al. The importance of appropriate partial volume correction for PET quantification in Alzheimer’s disease. Eur J Nucl Med Mol Imaging 38, 1104–1119 (2011). https://doi.org/10.1007/s00259-011-1745-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-011-1745-9

Keywords

Navigation