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Brain 18F-FDG PET analysis via interval-valued reconstruction: proof of concept for Alzheimer’s disease diagnosis

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Abstract

Objective

We propose an innovative approach for 18F-FDG PET analysis based on an interval-valued reconstruction of 18F-FDG brain distribution. Its diagnostic performance for Alzheimer’s disease (AD) diagnosis with comparison to a validated post-processing software was assessed.

Methods

Brain 18F-FDG PET data from 26 subjects were acquired in a clinical routine setting. Raw data were reconstructed using an interval-valued version of the ML–EM algorithm called NIBEM that stands for Non-Additive interval-based expectation maximization. Subject classification was obtained via interval-based statistical comparison (intersection ratio, IR) between cortical regions of interest (ROI) including parietal, temporal, and temporo-mesial cortices and a reference region, the sub-cortical grey nuclei, known not to be affected by AD. In parallel, PET images were post-processed using a validated automated software based on the computation of ROI normalized uptake ratios standard deviation (SUVr SD) with reference to a healthy control database (Siemens Scenium). Clinical diagnosis made during follow-up was considered as the gold-standard for patient classification (16 healthy controls and 10 AD patients).

Results

Both methods provided cortical ROI indices that were significantly different between controls and AD patients. The area under the ROC curve for control/AD classification was statistically identical (0.96 for NIBEM IR and 0.95 for Scenium SUVr SD). At the optimal threshold, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were, respectively, 100%, 88%, 92%, 83%, and 100% for both Scenium SUVr SD and NIBEM IR methods.

Conclusion

This preliminary study shows that interval-valued reconstruction allows self-consistent analysis of brain 18F-FDG PET data, yielding diagnostic performances that seem promising with respect to those of a commercial post-processing software based on SUVr SD analysis.

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Notes

  1. Available at: https://www.gin.cnrs.fr/AAL2.

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Acknowledgements

The authors are grateful to the nuclear medicine staff of University Hospital Gui de Chauliac in Montpellier for their help regarding the constitution of the database used in this study. The first author was the recipient of a grant funded by the Siemens Healthineers company. There is no other potential conflict of interest relevant to this article.

Funding

Florentin Kucharczak was the recipient of a grant funded by the Siemens Healthineers company. There is no other potential conflict of interest relevant to this article.

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Authors and Affiliations

  1. Département de médecine nucléaire, Montpellier University Hospital, CHU Lapeyronie, 191 av. du Doyen Giraud, 34295, Montpellier cedex 5, France

    Florentin Kucharczak, Marie Suau, Fayçal Ben Bouallègue & Denis Mariano-Goulart

  2. LIRMM, CNRS, Univ. Montpellier, 860 rue de Saint Priest, 34095, Montpellier, France

    Florentin Kucharczak & Olivier Strauss

  3. PhyMedExp, INSERM, CNRS, Univ. Montpellier, CHU Arnaud de Villeneuve, 34295, Montpellier, France

    Fayçal Ben Bouallègue & Denis Mariano-Goulart

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  1. Florentin Kucharczak
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  2. Marie Suau
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Correspondence to Florentin Kucharczak.

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All procedures performed were in accordance with the ethical standards of the institutional and/or national research committee and with the principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The authors received a favorable opinion of the Institutional Review Board of University Hospital Center of Montpellier under Grant number 2018-IRB-MTP-09-08.

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Kucharczak, F., Suau, M., Strauss, O. et al. Brain 18F-FDG PET analysis via interval-valued reconstruction: proof of concept for Alzheimer’s disease diagnosis. Ann Nucl Med 34, 565–574 (2020). https://doi.org/10.1007/s12149-020-01490-7

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  • DOI: https://doi.org/10.1007/s12149-020-01490-7

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