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Brain PET in small structures is challenged by low resolution inducing bias in the activity measurements. Improved spatial resolution may be obtained by using dedicated tomographs and more comprehensive modeling of the acquisition system during reconstruction. In this study, we assess the impact of resolution modeling (RM) during reconstruction on image quality and on the estimates of biologic parameters in a clinical study performed on a high-resolution research tomograph. Methods: An accelerated list-mode ordinary Poisson ordered-subset expectation maximization (OP-OSEM) algorithm, including sinogram-based corrections and an experimental stationary model of resolution, has been designed. Experimental phantom studies are used to assess contrast and noise characteristics of the reconstructed images. The binding potential of a selective tracer of the dopamine transporter is also assessed in anatomic volumes of interest in a 5-patient study. Results: In the phantom experiment, a slower convergence and a higher contrast recovery are observed for RM-OP-OSEM than for OP-OSEM for the same level of statistical noise. RM-OP-OSEM yields contrast recovery levels that could not be reached without RM as well as better visual recovery of the smallest spheres and better delineation of the structures in the reconstructed images. Statistical noise has lower variance at the voxel level with RM than without at matched resolution. In a uniform activity region, RM induces higher positive and lower negative correlations with neighboring voxels, leading to lower spatial variance. Clinical images reconstructed with RM demonstrate better delineation of cortical and subcortical structures in both time-averaged and parametric images. The binding potential in the striatum is also increased, a result similar to the one observed in the phantom study. Conclusion: In high-resolution PET, RM during reconstruction improves quantitative accuracy by reducing the partial-volume effects.

PurposePositron emission tomography (PET) imaging of brain amyloid load has been suggested as a core biomarker for Alzheimer’s disease (AD). The aim of this study was to test the feasibility of using PET imaging with 18F-AV-45 (florbetapir) in a routine clinical environment to differentiate between patients with mild to moderate AD and mild cognitive impairment (MCI) from normal healthy controls (HC).MethodsIn this study, 46 subjects (20 men and 26 women, mean age of 69.0 ± 7.6 years), including 13 with AD, 12 with MCI and 21 HC subjects, were enrolled from three academic memory clinics. PET images were acquired over a 10-min period 50 min after injection of florbetapir (mean ± SD of radioactivity injected, 259 ± 57 MBq). PET images were assessed visually by two individuals blinded to any clinical information and quantitatively via the standard uptake value ratio (SUVr) in the specific regions of interest, which were defined in relation to the cerebellum as the reference region.ResultsThe mean values of SUVr were higher in AD patients (median 1.20, Q1-Q3 1.16-1.30) than in HC subjects (median 1.05, Q1-Q3 1.04-1.08; p = 0.0001) in the overall cortex and all cortical regions (precuneus, anterior and posterior cingulate, and frontal median, temporal, parietal and occipital cortex). The MCI subjects also showed a higher uptake of florbetapir in the posterior cingulate cortex (median 1.06, Q1-Q3 0.97-1.28) compared with HC subjects (median 0.95, Q1-Q3 0.82-1.02; p = 0.03). Qualitative visual assessment of the PET scans showed a sensitivity of 84.6% (95% CI 0.55–0.98) and a specificity of 38.1% (95% CI 0.18–0.62) for discriminating AD patients from HC subjects; however, the quantitative assessment of the global cortex SUVr showed a sensitivity of 92.3% and specificity of 90.5% with a cut-off value of 1.122 (area under the curve 0.894).ConclusionThese preliminary results suggest that PET with florbetapir is a safe and suitable biomarker for AD that can be used routinely in a clinical environment. However, the low specificity of the visual PET scan assessment could be improved by the use of specific training and automatic or semiautomatic quantification tools.