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

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.

Development of an ex vivo respiratory pediatric model of bronchopulmonary dysplasia for aerosol deposition studies.

Ethical restrictions are limitations of in vivo inhalation studies, on humans and animal models. Thus, in vitro or ex vivo anatomical models offer an interesting alternative if limitations are clearly identified and if extrapolation to human is made with caution. This work aimed to develop an ex vivo infant-like respiratory model of bronchopulmonary dysplasia easy to use, reliable and relevant compared to in vivo infant data. This model is composed of a 3D-printed head connected to a sealed enclosure containing a leporine thorax. Physiological data and pleural-mimicking depressions were measured for chosen respiratory rates. Homogeneity of ventilation was assessed by 81mkrypton scintigraphies. Regional radioaerosol deposition was quantified with 99mtechnetium-diethylene triamine pentaacetic acid after jet nebulization. Tidal volumes values are ranged from 33.16 ± 7.37 to 37.44 ± 7.43 mL and compliance values from 1.78 ± 0.65 to 1.85 ± 0.99 mL/cmH2O. Ventilation scintigraphies showed a homogenous ventilation with asymmetric repartition: 56.94% ± 9.4% in right lung and 42.83% ± 9.36 in left lung. Regional aerosol deposition in lungs exerted 2.60% ± 2.24% of initial load of radioactivity. To conclude the anatomical model satisfactorily mimic a 3-months old BPD-suffering bronchopulmonary dysplasia and can be an interesting tool for aerosol regional deposition studies.