Tracer kinetic modeling of [11C]AFM, a new PET imaging agent for the serotonin transporter

Objectives: [11C]AFM is a new radioligand for the serotonin transporter which showed higher specific binding than [11C]DASB in nonhuman primate studies. The aim of this study was to determine an appropriate kinetic model to quantify [11C]AFM binding in humans.Methods: Four healthy subjects were involved in a test-retest protocol. Dynamic PET data were acquired for 120 min with an HRRT scanner. Arterial blood sampling and metabolite analysis were performed. A 1-exponential (1e), 2-exponential (2e), and Hill function were fitted to the parent fraction data. Regional time-activity curves (TACs) were obtained and kinetic analysis was performed using one-tissue (1T) or two-tissue (2T) models, and Logan graphical analysis (LGA).Results: Metabolism of AFM was rapid with % parent of 31 plusminus 6.0 and 8.1 plusminus 4.0 at 30 and 90 min, respectively. The 2T model resulted in nonconvergence or unstable estimates. For the 1e and Hill models, means of residuals were far from zero compared with the 2e model. The 1T model with 2e metabolite fitting described the data very well in ROIs with high VT values. Moderate lack of fit was seen in ROIs with low VT in 3/8 scans. VT ranged from 6.5 plusminus 1.0 (cerebellum) to 23.9 plusminus 3.7 (thalamus), with K1 values of 0.33 to 0.43 mL/min/mL. LGA estimates were in excellent agreement with 1T: VT(LGA) = 0.94 VT(1T) + 0.78 (r2=0.99).Conclusions: The 1T model well describes the kinetics of [11C]AFM in most cases. The lack of fit in the remaining cases may be due to uncertainties in the metabolite measurement, and suggest that the ultimate development of a reference region approach is desirable.Research Support: NIMH (MH066624) and Grants-in-Aid for Scientific Research of JSPS (18-6916)2008 SNM Annual Meetin

Quantification of serotonin tranporters with [11C]AFM: Evaluation of reference region methods

Introduction: [11C]AFM is a new, highly selective PET radioligand for the serotonin transporter (Huang et al., 2004). Metabolism of [11C]AFM is rapid with parent fraction of 30% and 8% at 30 and 90 min, respectively, and the accurate measurement of the metabolite or input function is difficult. The aim of this study is to investigate the suitability of reference region approaches for human [11C]AFM studies with ROI analysis.Methods: Nine healthy subjects were included in this study, and four subjects were involved in a test-retest protocol. The average dose was 555 plusminus 41 MBq, and dynamic PET scans were acquired for 2 h with an HRRT scanner. The automated anatomical labeling template was used to generate 7 regional time-activity curves (TACs): cerebellum (reference region), caudate, hippocampus, frontal lobe, temporal lobe, putamen, and thalamus. TACs were analyzed using one- and two-tissue compartment models (1T, 2T), Logan graphical analysis (LGA) and multilinear analysis (MA1) with a metabolite-corrected input function. TACs were also analyzed using three reference region approaches: SRTM, SRTM2, and reference-region based Logan graphical analysis (LGAR). The clearance rate constant of the reference region (k2\u27) for SRTM2 was calculated using SRTM, and k2\u27 for LGAR was fixed to be 0.05 [1/min], the mean value from the 1T model. For all graphical methods, tlow asterisk was set to be 60 min postinjection.Results: The use of 2T model was associated with unstable estimates. The ROIs with high VT were well described by 1T model. A lack of fit was observed in ROIs with low VT in 5/13 scans. The values of VT ranged from 7.0 plusminus 1.2 (cerebellum) to 24.9 plusminus 5.3 (putamen), with K1 values of 0.34 plusuminus 0.06 to 0.45 plusminus 0.07 [mL/min/cm3]. Compared with the estimates at t* = 60, the percent differences of BPND using LGA, MA1 and LGAR were 4.3 plusminus 5.1, 5.2 plusminus 5.3, and 4.1 plusminus 3.0 at t* = 20, respectively. The estimated BPND of LGA and MA1 matched well (BPND(MA1) = 1.02BPND(LGA) - 0.00 (r2 = 1.00)). This suggests that noise level in TACs is low. All three reference region approaches provided similar results. The linear regressions were BPND(SRTM) = 0.91BPND(LGA) + 0.06 (r2 = 0.99), BPND(SRTM2) = 0.87BPND(LGA) + 0.03 (r2 = 0.98), and BPND(LGAR) = 0.98BPND(LGA) + 0.00 (r2 = 1.00). The fit with 3-parameter SRTM was better than that of 2-parameter SRTM2. Fig. 1 shows the distributions of BPND estimated using different methods.Conclusion: BPND values obtained using reference region approaches were very similar to those from arterial input function methods, especially in regions with small BPND. Since all methods provide similar results for ROI curves, the choice of best method for ultimate pixel-by-pixel analysis should also consider the magnitude of noise-induced bias effects.Neuroreceptor Mapping 200

Optimization of serial arterial blood sampling for quantitative analysis for monkey PET data using independent component analysis-bsaed method

IEEE 2007 Nuclear Science Symposium and　Medical Imaging Conferenc

Evaluation of k2 imaging algorithm with 11C-verapamil using clustering-based kinetic approach

Introduction This study aims at evaluating the algorithm for k2 imaging using clustering-based kinetic approach [1] (CAKS) to 11C-verapamil (VER) which is a substrate of P-glycoprotein (P-gp) and is popular to measure P-gp functions. In CAKS,voxel-based tTACs are clustered based on such quantity that is related to the kinetics of VER in the brain. The behavior of VER can be described with one-tissue-two-compartment model [2]. Thus, the ratio of R = Integral t alpha C(t) dt / Integral C(t) dt has a direct relationship with k2 where the integral interval is from 0 to the last frame [1]. tTAC in a voxels are clustered based on each R, andthen an averaged tTAC in a cluster is utilized for compartment model estimation. This averaging improves a noise level in tTAC; the number of voxels in a cluster is 1000 in an implemented CAKS. Also, a computational time is reduced because estimates are not performed for each voxel but for each cluster. Note that K1 can be computed for every voxels although k2 is obtained for a cluster.Methods Five normal volunteers involved who were 57 to 76 yrs, and two women and three men. A set of dynamic scan was conducted using Headtome-V (Shimadzu Co., Kyoto, Japan) with arterial blood samplings and metabolite correction. ROIs were placed on the frontal cortex, cerebellum, choroid plexus and centrum semiovale as the white matter, and the averaged tTAC in the ROI was inputted to a usual nonlinear estimation algorithm to derive the estimates of K1 and k2 in ROI. Also CAKS was applied to the data for K1 and k2 imaging, and the ROI-averaged estimates in the ROI were compared with the ROI-based kinetic analysis.Results and Conclusion The comparison is shown in Fig. 1 in which the ROI-based usual kinetic analysis and CAKS are assigned on x- and y-axis, respectively. The regressions were y=1.07x+0.00 (r2=0.99), y=1.06x+0.00 (r2=0.94) and y=1.02x-0.04 (r2 = 0.99) for K1, k2 and total distribution volume (VT ), respectively. Therefore, the estimates using CAKS are almost identical with a usual ROI-based kinetics. The typical parametric images of VER are demonstrated in Fig. 2. In K1, a structure between gray and white matters could be seen. The choroid plexsus had a higher K1 than other regions, but the k2 was homogeneous. The clustering scheme based on kinetics could suppress the bad noise statistics in voxel-based tTAC so that reliable parametric images could be expected. The computational time was less than 5 minutes for the image of 125-by- 125 voxels and 40 slices, therfore, CAKS is practical for the parametric imaging on P-gp.Neuroreceptor Mapping 200

Distribution volumes as an alternative to binding potentials for sigma1 imaging

Brain\u2707 and BrainPET\u270

Elimination of arterial blood sampling in Logan plot using intersectional searching algorithm and clustering in [11C]TMSX PET

Brain\u2707 and BrainPET\u270

Ubiquitous tele-echography system: downsized wearable ultrasound probe with distributed processors and displays

In the ubiquitous multi-media computing environment, medical imaging hardware can share its components such as display or logic over the network. In this paper, a compact tele-echography system that works on the distributed network environment is presented. The system is a hand-held size ultrasound sensory probe connected to a wearable computer with wireless networking facility. To adapt various imaging media in the ubiquitous computing environment, raw echo data are transmitted instead of pre-rendered image