Irreversible Two-tissue Compartment Model Research Articles

First demonstration of in vivo mapping for regional brain monoacylglycerol lipase using PET with [11C]SAR127303

Monoacylglycerol lipase (MAGL) is a main regulator of the endocannabinoid system within the central nervous system (CNS). Recently, [11C]SAR127303 was developed as a promising radioligand for MAGL imaging. In this study, we aimed to quantify regional MAGL concentrations in the rat brain using positron emission tomography (PET) with [11C]SAR127303. An irreversible two-tissue compartment model (2-TCMi, k4 = 0) analysis was conducted to estimate quantitative parameters (k3, Ki2-TCMi, and λk3). These parameters were successfully obtained with high identifiability (<10 %COV) for the following regions ranked in order from highest to lowest: cingulate cortex > striatum > hippocampus > thalamus > cerebellum > hypothalamus ≈ pons. In vitro autoradiographs using [11C]SAR127303 showed a heterogeneous distribution of radioactivity, as seen in the PET images. The Ki2-TCMi and λk3 values correlated relatively highly with in vitro binding (r > 0.4, P < 0.005). The Ki2-TCMi values showed high correlation and low underestimation (<10%) compared with the slope of a Patlak plot analysis with linear regression (KiPatlak). In conclusion, we successfully estimated regional net uptake value of [11C]SAR127303 reflecting MAGL concentrations in rat brain regions for the first time.

4D-PET reconstruction using a spline-residue model with spatial and temporal roughness penalties

4D reconstruction of dynamic positron emission tomography (dPET) data can improve the signal-to-noise ratio in reconstructed image sequences by fitting smooth temporal functions to the voxel time-activity-curves (TACs) during the reconstruction, though the optimal choice of function remains an open question. We propose a spline-residue model, which describes TACs as weighted sums of convolutions of the arterial input function with cubic B-spline basis functions. Convolution with the input function constrains the spline-residue model at early time-points, potentially enhancing noise suppression in early time-frames, while still allowing a wide range of TAC descriptions over the entire imaged time-course, thus limiting bias.Spline-residue based 4D-reconstruction is compared to that of a conventional (non-4D) maximum a posteriori (MAP) algorithm, and to 4D-reconstructions based on adaptive-knot cubic B-splines, the spectral model and an irreversible two-tissue compartment (‘2C3K’) model. 4D reconstructions were carried out using a nested-MAP algorithm including spatial and temporal roughness penalties. The algorithms were tested using Monte-Carlo simulated scanner data, generated for a digital thoracic phantom with uptake kinetics based on a dynamic [18F]-Fluromisonidazole scan of a non-small cell lung cancer patient. For every algorithm, parametric maps were calculated by fitting each voxel TAC within a sub-region of the reconstructed images with the 2C3K model.Compared to conventional MAP reconstruction, spline-residue-based 4D reconstruction achieved >50% improvements for five of the eight combinations of the four kinetics parameters for which parametric maps were created with the bias and noise measures used to analyse them, and produced better results for 5/8 combinations than any of the other reconstruction algorithms studied, while spectral model-based 4D reconstruction produced the best results for 2/8. 2C3K model-based 4D reconstruction generated the most biased parametric maps. Inclusion of a temporal roughness penalty function improved the performance of 4D reconstruction based on the cubic B-spline, spectral and spline-residue models.

Voxel level quantification of [11C]CURB, a radioligand for Fatty Acid Amide Hydrolase, using high resolution positron emission tomography.

[11C]CURB is a novel irreversible radioligand for imaging fatty acid amide hydrolase in the human brain. In the present work, we validate an algorithm for generating parametric map images of [11C]CURB acquired with a high resolution research tomograph (HRRT) scanner. This algorithm applies the basis function method on an irreversible two-tissue compartment model (k4 = 0) with arterial input function, i.e., BAFPIC. Monte Carlo simulations are employed to assess bias and variability of the binding macroparameters (Ki and λk3) as a function of the voxel noise level and the range of basis functions. The results show that for a [11C]CURB time activity curve with noise levels corresponding to a voxel of an image acquired with the HRRT and reconstructed with the filtered back projection algorithm, the implementation of BAFPIC requires the use of a constant vascular fraction of tissue (5%) and a cutoff for slow frequencies (0.06 min-1). With these settings, BAFPIC maintains the probabilistic distributions of the binding macroparameters with approximately Gaussian shape and minimizes the bias and variability for large physiological ranges of the rate constants of [11C]CURB. BAFPIC reduces the variability of Ki to a third of that given by Patlak plot, the standard graphical method for irreversible radioligands. Application to real data demonstrated an excellent correlation between region of interest and BAFPIC parametric data and agreed with the simulations results. Therefore, BAFPIC with a constant vascular fraction can be used to generate parametric maps of [11C]CURB images acquired with an HRRT provided that the limits of the basis functions are carefully selected.

Quantitative Analysis of [18F]FMISO PET for Tumor Hypoxia: Correlation of Modeling Results with Immunohistochemistry.

Quantitative evaluation of tumor hypoxia based on H-1-(3-[18F]fluoro-2-hydroxypropyl)-2-nitroimidazole ([18F]FMISO) positron emission tomography (PET) can deliver important information for treatment planning in radiotherapy. However, the merits and limitations of different analysis methods in revealing the underlying physiological feature are not clear. This study aimed to assess these quantitative analysis methods with the support of immunohistological data. Sixteen nude mice bearing xenografted human squamous cell carcinomas (FaDu or CAL-33) were scanned using 2-h dynamic [18F]FMISO PET. Tumors were resected and sliced, and the hypoxia marker pimonidazole was immunostained followed by H&E staining. The pimonidazole signal was segmented using a k-means clustering algorithm, and the hypoxic fraction (HF) was calculated as the hypoxic area/viable tumor-tissue-area ratio pooled over three tissue slices from the apical, center, and basal layers. PET images were analyzed using various methods including static analysis [standard uptake value (SUV), tumor-to-blood ratio (T/B), tumor-to-muscle ratio (T/M)] and kinetic modeling (Casciari αk A , irreversible and reversible two-tissue compartment k 3, Thorwarth w A k 3, Patlak K i , Logan V d , Cho K), and correlated with HF. No significant correlation was found for static analysis. A significant correlation between k 3 of the irreversible two-tissue compartment model and HF was observed (r = 0.61, p = 0.01). The correlation between HF and αk A of the Casciari model could be improved through reducing local minima by testing more sets of initial values (r = 0.59, p = 0.02) or by reducing the model complexity by fixing three parameters (r = 0.63, p = 0.0008). With support of immunohistochemistry data, this study shows that various analysis methods for [18F]FMISO PET perform differently for assessment of tumor hypoxia. A better fitting quality does not necessarily mean a higher physiological correlation. Hypoxia PET analysis needs to consider both the mathematical stability and physiological fidelity. Based on the results of this study, preference should be given to the irreversible two-tissue compartment model as well as the Casciari model with reduced parameters.

Blocking of fatty acid amide hydrolase activity with PF-04457845 in human brain: a positron emission tomography study with the novel radioligand [(11)C]CURB.

Positron emission tomography with [(11)C]CURB was recently developed to quantify fatty acid amide hydrolase (FAAH), the enzyme responsible for hydrolyzing the endocannabinoid anandamide. This study investigated the test-retest reliability of [(11)C]CURB as well as its in vivo specificity and the validity of the kinetic model by using the highly specific FAAH inhibitor, PF-04457845. Five healthy volunteers completed test-retest [(11)C]CURB scans 1 to 2 months apart and six subjects completed baseline and blocking scans on the same day after PF-04457845 (p.o.) administration (1, 4, or 20 mg; n=2 each). The composite parameter λk3 (an index of FAAH activity, λ=K1/k2) was estimated using an irreversible two-tissue compartment model with plasma input function. There were no clinically observable responses to oral PF-04457845 or [(11)C]CURB injection. Oral administration of PF-04457845 reduced [(11)C]CURB binding to a homogeneous level at all three doses, with λk3 values decreased by ⩾91%. Excellent reproducibility and good reliability (test-retest variability=9%; intraclass correlation coefficient=0.79) were observed across all regions of interest investigated. Our findings suggest that λk3/[(11)C]CURB is a reliable, highly sensitive, and selective tool to measure FAAH activity in human brain in vivo. Moreover, PF-04457845 is a highly potent FAAH inhibitor (>95% inhibition at 1 mg) in living human brain.

WE-AB-204-08: Uncertainties in Kinetic Modeling of Shortened Dynamic 18F-FMISO PET Due to Image Noise

Purpose: To assess uncertainties in voxel-wise kinetic modeling (KM) of dynamic 18F-FMISO PET (dPET) due to image noise for different lengths of dPET acquisitions. Methods: 12 tumor time activity curves (TACs) deduced from 6 head and neck cancer patient dPET datasets (45min dPET + 10min frames at ∼95min and ∼160min respectively) were modeled using an irreversible two-tissue compartment model to estimate kinetic rate constants (KRCs) k3, vB, K1, and K1/k2 (reference standard). For each modeled TAC, 1000 noisy TACs were simulated by adding Gaussian noise with standard deviation equivalent to that observed on a voxel level in GE DSTE PET-CT. KM was conducted for each set of noisy TACs using (i) full dataset (170min), and repeated using (ii) 105min and (iii) 45min shortened subsets, with an input function that was image-derived from the dPET dataset of matching length. Absolute value of percent difference between KRCs from noisy TACs as estimated from either of 3 datasets and reference standard KRCs was used to represent bias. Results: For all KRCs, bias was higher for shortened datasets. For k3, bias was lower when actual k3 was higher: from 7%±5% (170min), 9%±7% (105min), and 14%±11% (45min) (actual k3=0.0098), to 32%±25% (170min), 45%±34% (95min), and 79%±48% (45min) (actual k3=0.0008). Similar relationship was observed for vB, with bias being between ∼7% (actual vB=0.28) and ∼26–34% (actual vB=0.08). For K1 [K1/k2], bias was ∼6–10% [∼2–3%] (dependence on actual value of K1 [K1/k2] was not observed). Conclusion: Uncertainties in voxel-wise KM of shortened 18F-FMISO dPET due to image noisy only are larger for shorter acquisitions and, for k3 [vB], the bias was found to be inversely correlated with the actual values of k3 [vB]. Using 45min subset, k3, vB, K1, and K1/k2 could be estimated to within ∼15–80%, ∼10–35%, ∼10%, and ∼3%, respectively.

Pharmacokinetic Analysis of (64)Cu-ATSM Dynamic PET in Human Xenograft Tumors in Mice.

The aim of this study was to evaluate the feasibility to perform voxel-wise kinetic modeling on datasets obtained from tumor-bearing mice that underwent dynamic PET scans with 64Cu-ATSM and extract useful physiological parameters. Methods: Tumor-bearing mice underwent 90-min dynamic PET scans with 64Cu-ATSM and CT scans with contrast. Irreversible and reversible two-tissue compartment models were fitted to time activity curves (TACs) obtained from whole tumor volumes and compared using the Akaike information criterion (AIC). Based on voxel-wise pharmacokinetic analysis, parametric maps of model rate constants k1, k3 and Ki were generated and compared to 64Cu-ATSM uptake. Results: Based on the AIC, an irreversible two-tissue compartment model was selected for voxel-wise pharmacokinetic analysis. Of the extracted parameters, k1 (~perfusion) showed a strong correlation with early tracer uptake (mean spearman R = 0.88) 5 min post injection (pi). Moreover, positive relationships were found between late tracer uptake (90 min pi) and both k3 and the net influx rate constant, Ki (mean spearman R = 0.56 and R = 0.86; respectively). Conclusion: This study shows the feasibility to extract relevant parameters from voxel-wise pharmacokinetic analysis to be used for preclinical validation of 64Cu-ATSM as a hypoxia-specific PET tracer.

WE-C-217BCD-12: Irreversible Two-Tissue Compartment Model Fitting for Dynamic 18F- FDG PET: A Practical Comparison of Methods Using Simulated Time- Activity Data.

To compare techniques of fitting simulated Dynamic 18F-FDG PET time-activity curves to the irreversible two-tissue compartment model. The precision and accuracy of various algorithms were assessed in the presence of measurement error and with a practical focus on CPU time. Dynamic PET analysis is often applied to multiple individual voxels, leading to concerns regarding time efficiency, yet a reluctance to compromise on accuracy. This study evaluates selected fitting algorithms in terms of the precision/bias of fitted parameters and run-time. As a standard for comparison, biological parameters were fixed at typical values (K1=0.2, k2=0.25, k3=0.05, K&#7522;=0.3334). Tissue time-activity curves were generated using the model equation and by incorporating activity/time dependent Gaussian noise. The following algorithms were then applied: Genetic, Conjugate Gradient (CG), Gradient Descent (GD), Simulated Annealing (SA), Levenberg-Marquardt (LMQ), Gauss-Newton (GN), and Limited-BFGS (L-BFGS). Non-iterative, problem specific approaches were also considered: Patlak analysis (K? only), and Blomqvist linearization (BL). Parameter accuracy and precision were quantified with relative errors (REs). At typical noise levels, maximal REs were >60% (resulting in |Î'K?|>10%) for GD and SA, <20% for BL (|Î'K?|<6%) and Patlak (|Î'K?|<4%), and <15% (|Î'K?|<2%) with other methods. On average, the highest fidelity parameter estimates (rate-constant REs<2.5%, |Î'K?|<0.15%) were attained with L-BFGS, GN, and LMQ. In contrast, the non-iterative methods provided poorer estimates (RE between 1-7%), though run-time (<0.05 ms) was 100-1000x less. Using BL results as initial estimates for L- BFGS (BL+L-BFGS), GN (BL+GN) or LMQ (BL+LMQ) yielded rapid convergence (<0.1 ms), while maintaining superiority with respect to parameter bias. Poor choice of a fitting algorithm may lead to significant errors in estimated kinetic parameters and/or unpractically high computation times. Thus, our recommendation is the use of BL+L-BFGS, BL+GN or BL+LMQ, which is contrary to the gold standards, Patlak and LMQ (alone), commonly seen in the literature.

Validity of Simplified 3′-Deoxy-3′-[18F]Fluorothymidine Uptake Measures for Monitoring Response to Chemotherapy in Locally Advanced Breast Cancer

PurposePositron emission tomography using 3′-deoxy-3′-[18F]fluorothymidine ([18F]FLT) has been suggested as a means for monitoring response to chemotherapy. The aim of this study was to evaluate the validity of simplified uptake measures for assessing response to chemotherapy using [18F]FLT in locally advanced breast cancer (LABC).ProceduresFifteen LABC patients underwent dynamic [18F]FLT scans both prior to and after the first cycle of chemotherapy with fluorouracil, epirubicin or doxorubicin, and cyclophosphamide. The net uptake rate constant of [18F]FLT, K i, determined by non-linear regression (NLR) of an irreversible two-tissue compartment model was used as the gold standard. In addition to Patlak graphical analysis, standardised uptake values (SUV) and tumour-to-whole blood ratio (TBR) were used for analysing [18F]FLT data. Correlations and relationships between simplified uptake measures and NLR before and after chemotherapy were assessed using regression analysis.ResultsNo significant differences in both pre- and post-chemotherapy relationships between any of the simplified uptake measures and NLR were found. However, changes in SUV between baseline and post-therapy scans showed a significant negative bias and slope less than one, while TBR did not.ConclusionsIn LABC, TBR instead of SUV may be preferred for monitoring response to chemotherapy with [18F]FLT.

Quantitative Assessment of Heterogeneity in Tumor Metabolism Using FDG-PET

[(18)F]-fluorodeoxyglucose-positron emission tomography (FDG-PET) images are usually quantitatively analyzed in "whole-tumor" volumes of interest. Also parameters determined with dynamic PET acquisitions, such as the Patlak glucose metabolic rate (MR(glc)) and pharmacokinetic rate constants of two-tissue compartment modeling, are most often derived per lesion. We propose segmentation of tumors to determine tumor heterogeneity, potentially useful for dose-painting in radiotherapy and elucidating mechanisms of FDG uptake. In 41 patients with 104 lesions, dynamic FDG-PET was performed. On MR(glc) images, tumors were segmented in quartiles of background subtracted maximum MR(glc) (0%-25%, 25%-50%, 50%-75%, and 75%-100%). Pharmacokinetic analysis was performed using an irreversible two-tissue compartment model in the three segments with highest MR(glc) to determine the rate constants of FDG metabolism. From the highest to the lowest quartile, significant decreases of uptake (K(1)), washout (k(2)), and phosphorylation (k(3)) rate constants were seen with significant increases in tissue blood volume fraction (V(b)). Tumor regions with highest MR(glc) are characterized by high cellular uptake and phosphorylation rate constants with relatively low blood volume fractions. In regions with less metabolic activity, the blood volume fraction increases and cellular uptake, washout, and phosphorylation rate constants decrease. These results support the hypothesis that regional tumor glucose phosphorylation rate is not dependent on the transport of nutrients (i.e., FDG) to the tumor.

Parametric Mapping of [18F]Fluoromisonidazole Positron Emission Tomography using Basis Functions

In this study, we show a basis function method (BAFPIC) for voxelwise calculation of kinetic parameters (K(1), k(2), k(3), K(i)) and blood volume using an irreversible two-tissue compartment model. BAFPIC was applied to rat ischaemic stroke micro-positron emission tomography data acquired with the hypoxia tracer [(18)F]fluoromisonidazole because irreversible two-tissue compartmental modelling provided good fits to data from both hypoxic and normoxic tissues. Simulated data show that BAFPIC produces kinetic parameters with significantly lower variability and bias than nonlinear least squares (NLLS) modelling in hypoxic tissue. The advantage of BAFPIC over NLLS is less pronounced in normoxic tissue. K(i) determined from BAFPIC has lower variability than that from the Patlak-Gjedde graphical analysis (PGA) by up to 40% and lower bias, except for normoxic tissue at mid-high noise levels. Consistent with the simulation results, BAFPIC parametric maps of real data suffer less noise-induced variability than do NLLS and PGA. Delineation of hypoxia on BAFPIC k(3) maps is aided by low variability in normoxic tissue, which matches that in K(i) maps. BAFPIC produces K(i) values that correlate well with those from PGA (r(2)=0.93 to 0.97; slope 0.99 to 1.05, absolute intercept <0.00002 mL/g per min). BAFPIC is a computationally efficient method of determining parametric maps with low bias and variance.