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Evaluation of software tools for automated identification of neuroanatomical structures in quantitative β-amyloid PET imaging to diagnose Alzheimer’s disease

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Abstract

Introduction

For regional quantification of nuclear brain imaging data, defining volumes of interest (VOIs) by hand is still the gold standard. As this procedure is time-consuming and operator-dependent, a variety of software tools for automated identification of neuroanatomical structures were developed. As the quality and performance of those tools are poorly investigated so far in analyzing amyloid PET data, we compared in this project four algorithms for automated VOI definition (HERMES Brass, two PMOD approaches, and FreeSurfer) against the conventional method. We systematically analyzed florbetaben brain PET and MRI data of ten patients with probable Alzheimer’s dementia (AD) and ten age-matched healthy controls (HCs) collected in a previous clinical study.

Methods

VOIs were manually defined on the data as well as through the four automated workflows. Standardized uptake value ratios (SUVRs) with the cerebellar cortex as a reference region were obtained for each VOI. SUVR comparisons between ADs and HCs were carried out using Mann-Whitney-U tests, and effect sizes (Cohen’s d) were calculated. SUVRs of automatically generated VOIs were correlated with SUVRs of conventionally derived VOIs (Pearson’s tests).

Results

The composite neocortex SUVRs obtained by manually defined VOIs were significantly higher for ADs vs. HCs (p=0.010, d=1.53). This was also the case for the four tested automated approaches which achieved effect sizes of d=1.38 to d=1.62. SUVRs of automatically generated VOIs correlated significantly with those of the hand-drawn VOIs in a number of brain regions, with regional differences in the degree of these correlations. Best overall correlation was observed in the lateral temporal VOI for all tested software tools (r=0.82 to r=0.95, p<0.001).

Conclusion

Automated VOI definition by the software tools tested has a great potential to substitute for the current standard procedure to manually define VOIs in β-amyloid PET data analysis.

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Acknowledgments

We would like to thank the statistical counselling service of IMISE, University of Leipzig for their support.

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Authors and Affiliations

  1. Department of Nuclear Medicine, Leipzig University Medical Centre, Liebigstraße 18, 04103, Leipzig, Germany

    Tobias Tuszynski, Michael Rullmann, Julia Luthardt, Daniel Butzke, Solveig Tiepolt, Swen Hesse, Anita Seese, Osama Sabri & Henryk Barthel

  2. Department of Psychiatry, Leipzig University Medical Centre, Leipzig, Germany

    Hermann-Josef Gertz

  3. Integrated Treatment and Research Centre (IFB) Adiposity Diseases, Leipzig University Medical Centre, Leipzig, Germany

    Michael Rullmann, Swen Hesse & Osama Sabri

  4. Department of Neuroradiology, Leipzig University Medical Centre, Leipzig, Germany

    Donald Lobsien

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Correspondence to Henryk Barthel.

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Funding

This project received research support by Piramal Imaging, HERMES Medical solutions, and PMOD Technologies Ltd.

Conflict of interest

OS and HB received speaker and consulting honoraria from Piramal Imaging and Siemens Healthcare. MR received travel expenses from Piramal Imaging. SH received travel grants and honoraria from General Electric (GE) Healthcare and Bayer Schering Pharma.

Ethical approval

All procedures performed involving human participants were in accordance with the ethical standards of the 1964 Helsinki declaration and its later amendments and with the standards of the institutional and national research committee. This article does not contain any studies with animals performed by any of the authors.

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Written informed consent was obtained from all individual participants included in the study.

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Tuszynski, T., Rullmann, M., Luthardt, J. et al. Evaluation of software tools for automated identification of neuroanatomical structures in quantitative β-amyloid PET imaging to diagnose Alzheimer’s disease. Eur J Nucl Med Mol Imaging 43, 1077–1087 (2016). https://doi.org/10.1007/s00259-015-3300-6

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  • DOI: https://doi.org/10.1007/s00259-015-3300-6

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