Validation of Parametric Methods for [11C]UCB-J PET Imaging Using Subcortical White Matter as Reference Tissue

PURPOSE

The aim of this study was to evaluate different non-invasive methods for generating (R)-1-((3-([¹¹C]methyl)pyridin-4-yl)methyl)-4-(3,4,5-trifluorophenyl)pyrrolidin-2-one) ([¹¹C]UCB-J) parametric maps using white matter (centrum semi-ovale-SO) as reference tissue.

PROCEDURES

Ten healthy volunteers (8 M/2F; age 27.6 ± 10.0 years) underwent a 90-min dynamic [¹¹C]UCB-J positron emission tomography (PET) scan with full arterial blood sampling and metabolite analysis before and after administration of a novel chemical entity with high affinity for presynaptic synaptic vesicle glycoprotein 2A (SV2A). A simplified reference tissue model (SRTM2), multilinear reference tissue model (MRTM2), and reference Logan graphical analysis (rLGA) were used to generate binding potential maps using SO as reference tissue (BP_SO). Shorter dynamic acquisitions down to 50 min were also considered. In addition, standard uptake value ratios (SUVR) relative to SO were evaluated for three post-injection intervals (SUVR_SO,40-70min, SUVR_SO,50-80min, and SUVR_SO,60-90min respectively). Regional parametric BP_SO + 1 and SUVR_SO were compared with regional distribution volume ratios of a 1-tissue compartment model (1TCM DVR_SO) using Spearman correlation and Bland-Altman analysis.

RESULTS

For all methods, highly significant correlations were found between regional, parametric BP_SO + 1 (r = [0.63;0.96]) or SUVR_SO (r = [0.90;0.91]) estimates and regional 1TCM DVR_SO. For a 90-min dynamic scan, parametric SRTM2 and MRTM2 values presented similar small bias and variability (- 3.0 ± 2.9 % for baseline SRTM2) and outperformed rLGA (- 10.0 ± 5.3 % for baseline rLGA). Reducing the dynamic acquisition to 60 min had limited impact on the bias and variability of parametric SRTM2 BP_SO estimates (- 1.0 ± 9.9 % for baseline SRTM2) while a higher variability (- 1.83 ± 10.8 %) for baseline MRTM2 was observed for shorter acquisition times. Both SUVR_SO,60-90min and SUVR_SO,50-80min showed similar small bias and variability (- 2.8 ± 4.6 % bias for baseline SUVR_SO,60-90min).

CONCLUSION

SRTM2 is the preferred method for a voxelwise analysis of dynamic [¹¹C]UCB-J PET using SO as reference tissue, while reducing the dynamic acquisition to 60 min has limited impact on [¹¹C]UCB-J BP_SO parametric maps. For a static PET protocol, both SUVR_SO,60-90min and SUVR_SO,50-80min images are an excellent proxy for [¹¹C]UCB-J BP_SO parametric maps.

Comparison of DCE-MRI of murine model cancers with a low dose and high dose of contrast agent

There are increasing concerns regarding intracellular accumulation of gadolinium (Gd) after multiple dynamic contrast enhanced (DCE) MRI scans. We investigated whether a low dose (LD) of Gd-based contrast agent is as effective as a high dose (HD) for quantitative analysis of DCE-MRI data, and evaluated the use of a split dose protocol to obtain new diagnostic parameters. Female C3H mice (n = 6) were injected with mammary carcinoma cells in the hind leg. MRI experiments were performed on 9.4 T scanner. DCE-MRI data were acquired with 1.5 s temporal resolution before and after a LD (0.04 mmol/kg), then again after 30 min followed by a HD (0.2 mmol/kg) bolus injection of Omniscan. The standard Tofts model was used to extract physiological parameters (K^trans and v_e) with the arterial input function derived from muscle reference tissue. In addition, an empirical mathematical model was used to characterize maximum contrast agent uptake (A), contrast agent uptake rate (α) and washout rate (β and γ). There were moderate to strong correlations (r = 0.69-0.97, p < 0001) for parameters K^trans, v_e, A, α and β from LD versus HD data. On average, tumor parameters obtained from LD data were significantly larger (p < 0.05) than those from HD data. The parameter ratios, K^trans, v_e, A and α calculated from the LD data divided by the HD data, were all significantly larger than 1.0 (p < 0.003) for tumor. T₂* changes following contrast agent injection affected parameters calculated from HD data, but this was not the case for LD data. The results suggest that quantitative analysis of LD data may be at least as effective for cancer characterization as quantitative analysis of HD data. In addition, the combination of parameters from two different doses may provide useful diagnostic information.

Contrasting functional imaging parametric maps: The mislocation problem and alternative solutions

In the field of neuroimaging, researchers often resort to contrasting parametric maps to identify differences between conditions or populations. Unfortunately, contrast patterns mix effects related to amplitude and location differences and tend to peak away from sources of genuine brain activity to an extent that scales with the smoothness of the maps. Here, we illustrate this mislocation problem on source maps reconstructed from magnetoencephalographic recordings and propose a novel, dedicated location-comparison method. In realistic simulations, contrast mislocation was on average ∼10 mm when genuine sources were placed at the same location, and was still above 5 mm when sources were 20 mm apart. The dedicated location-comparison method achieved a sensitivity of ∼90% when inter-source distance was 12 mm. Its benefit is also illustrated on real brain-speech entrainment data. In conclusion, contrasts of parametric maps provide precarious information for source location. To specifically address the question of location difference, one should turn to dedicated methods as the one proposed here.

Parametric mapping and quantitative analysis of the human calvarium

In this paper we report how thickness and density vary over the calvarium region of a collection of human skulls. Most previous reports involved a limited number of skulls, with a limited number of measurement sites per skull, so data in the literature are sparse. We collected computer tomography (CT) scans of 51 ex vivo human calvaria, and analyzed these in silico using over 2000 measurement sites per skull. Thickness and density were calculated at these sites, for the three skull layers separately and combined, and were mapped parametrically onto the skull surfaces to examine the spatial variations per skull. These were found to be highly variable, and unique descriptors of the individual skulls. Of the three skull layers, the thickness of the inner cortical layer was found to be the most variable, while the least variable was the outer cortical density.