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

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

Methods: Brain F-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 F-FDG PET data, yielding diagnostic performances that seem promising with respect to those of a commercial post-processing software based on SUVr SD analysis.