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

  • Benjamin A. ThomasEmail author
  • Kjell Erlandsson
  • Marc Modat
  • Lennart Thurfjell
  • Rik Vandenberghe
  • Sebastien Ourselin
  • Brian F. Hutton
Original Article

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.

Keywords

Partial volume correction Amyloid Alzheimer’s disease Positron emission tomography Multimodality imaging 

Notes

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.

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Benjamin A. Thomas
    • 1
    Email author
  • Kjell Erlandsson
    • 1
  • Marc Modat
    • 2
  • Lennart Thurfjell
    • 3
  • Rik Vandenberghe
    • 4
    • 5
  • Sebastien Ourselin
    • 2
    • 6
  • Brian F. Hutton
    • 1
  1. 1.Institute of Nuclear MedicineUniversity College LondonLondonUK
  2. 2.Centre for Medical Image ComputingUniversity College LondonLondonUK
  3. 3.GE HealthcareAmershamUK
  4. 4.Laboratory for Cognitive NeurologyCatholic University LeuvenLeuvenBelgium
  5. 5.Neurology DepartmentUniversity Hospitals LeuvenLeuvenBelgium
  6. 6.Dementia Research CentreUniversity College LondonLondonUK

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