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07.01.2016 | Original Article | Ausgabe 6/2016

European Journal of Nuclear Medicine and Molecular Imaging 6/2016

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

Zeitschrift:
European Journal of Nuclear Medicine and Molecular Imaging > Ausgabe 6/2016
Autoren:
Tobias Tuszynski, Michael Rullmann, Julia Luthardt, Daniel Butzke, Solveig Tiepolt, Hermann-Josef Gertz, Swen Hesse, Anita Seese, Donald Lobsien, Osama Sabri, Henryk Barthel
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1007/​s00259-015-3300-6) contains supplementary material, which is available to authorized users.

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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Zusatzmaterial
ESM 1 (PDF 290 kb)
259_2015_3300_MOESM1_ESM.pdf
Literatur
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