An analysis of whole body tracer kinetics in dynamic PET studies with application to image-based blood input function extraction

Quantitation of dynamic total-body PET imaging: recent developments and future perspectives

BACKGROUND

Positron emission tomography (PET) scanning is an important diagnostic imaging technique used in disease diagnosis, therapy planning, treatment monitoring, and medical research. The standardized uptake value (SUV) obtained at a single time frame has been widely employed in clinical practice. Well beyond this simple static measure, more detailed metabolic information can be recovered from dynamic PET scans, followed by the recovery of arterial input function and application of appropriate tracer kinetic models. Many efforts have been devoted to the development of quantitative techniques over the last couple of decades.

CHALLENGES

The advent of new-generation total-body PET scanners characterized by ultra-high sensitivity and long axial field of view, i.e., uEXPLORER (United Imaging Healthcare), PennPET Explorer (University of Pennsylvania), and Biograph Vision Quadra (Siemens Healthineers), further stimulates valuable inspiration to derive kinetics for multiple organs simultaneously. But some emerging issues also need to be addressed, e.g., the large-scale data size and organ-specific physiology. The direct implementation of classical methods for total-body PET imaging without proper validation may lead to less accurate results.

CONCLUSIONS

In this contribution, the published dynamic total-body PET datasets are outlined, and several challenges/opportunities for quantitation of such types of studies are presented. An overview of the basic equation, calculation of input function (based on blood sampling, image, population or mathematical model), and kinetic analysis encompassing parametric (compartmental model, graphical plot and spectral analysis) and non-parametric (B-spline and piece-wise basis elements) approaches is provided. The discussion mainly focuses on the feasibilities, recent developments, and future perspectives of these methodologies for a diverse-tissue environment.

PET AIF estimation when available ROI data is impacted by dispersive and/or background effects

Objective.Blood pool region of interest (ROI) data extracted from the field of view of a PET scanner can be impacted by both dispersive and background effects. This circumstance compromises the ability to correctly extract the arterial input function (AIF) signal. The paper explores a novel approach to addressing this difficulty.Approach.The method involves representing the AIF in terms of the whole-body impulse response (IR) to the injection profile. Analysis of a collection/population of directly sampled arterial data sets allows the statistical behaviour of the tracer's impulse response to be evaluated. It is proposed that this information be used to develop a penalty term for construction of a data-adaptive method of regularisation estimator of the AIF when dispersive and/or background effects maybe impacting the blood pool ROI data.Main results.Computational efficiency of the approach derives from the linearity of the impulse response representation of the AIF and the ability to substantially rely on quadratic programming techniques for numerical implementation. Data from eight different tracers, used in PET cancer imaging studies, are considered. Sample image-based AIF extractions for brain studies with:18F-labeled fluoro-deoxyglucose and fluoro-thymidine (FLT),11C-labeled carbon dioxide (CO2) and15O-labeled water (H2O) are presented. Results are compared to the true AIF based on direct arterial sampling. Formal numerical simulations are used to evaluate the performance of the AIF extraction method when the ROI data has varying amounts of contamination, in comparison to a direct approach that ignores such effects. It is found that even with quite small amounts of contamination, the mean squared error of the regularised AIF is significantly better than the error associated with direct use of the ROI data.Significance.The proposed IR-based AIF extraction scheme offers a practical methodological approach for situations where the available image ROI data may be contaminated by background and/or dispersion effects.

Patient-Adaptive Population-Based Modeling of Arterial Input Functions

Kinetic modeling of dynamic PET data requires knowledge of tracer concentration in blood plasma, described by the arterial input function (AIF). Arterial blood sampling is the gold standard for AIF measurement, but is invasive and labour intensive. A number of methods have been proposed to accurately estimate the AIF directly from blood sampling and/or imaging data. Here we consider fitting a patient-adaptive mixture of historical population time course profiles to estimate individual AIFs. Travel time of a tracer atom from the injection site to the right ventricle of the heart is modeled as a realization from a Gamma distribution, and the time this atom spends in circulation before being sampled is represented by a subject-specific linear mixture of population profiles. These functions are estimated from independent population data. Individual AIFs are obtained by projection onto this basis of population profile components. The model incorporates knowledge of injection duration into the fit, allowing for varying injection protocols. Analyses of arterial sampling data from 18F-FDG, 15O-H2O and 18F-FLT clinical studies show that the proposed model can outperform reference techniques. The statistically significant gain achieved by using population data to train the basis components, instead of fitting these from the single individual sampling data, is measured on the FDG cohort. Kinetic analyses of simulated data demonstrate the reliability and potential benefit of this approach in estimating physiological parameters. These results are further supported by numerical simulations that demonstrate convergence and stability of the proposed technique under varying training population sizes and noise levels.

Role of dynamic and parametric whole-body FDG PET/CT imaging in molecular characterization of primary breast cancer: a single institution experience

AIM

The aim of this pilot study was to assess the role of dynamic whole-body PET and parametric imaging in the biological characterization of primary breast cancer.

MATERIALS AND METHOD

In total 24 histologically proven primary breast cancer lesions in 21 consecutive patients were retrospectively analyzed. Each patient underwent 18F-fluoro-deoxyglucose whole-body dynamic PET-CT before any treatment. Dynamic PET images were acquired in the list mode for a total duration of 70 min. The reconstructed parametric imaging generated Patlak plot-based 'Slope' and 'Intercept' images, from which parametric indices ki and DV were obtained. The standard uptake value (SUV) metric was also obtained by summing the last few frames of the dynamic study. ki, distribution volume (DV) and SUV were correlated with the histological tumor grade, biomarkers [hormone receptors and human epidermal growth factor receptor 2 (HER-2) neu expression] and molecular subtypes (A, B and C) as well as with tumor size, regional nodal metastases and distant metastases.

RESULTS

The mean ki was found to be significantly higher in grade III than II lesions (P = 0.005), HER-2 neu positive status (P = 0.04) and molecular subtype B (P = 0.04) as well as in greater than T1 lesions(P = 0.0003 and P = 0.04, respectively) and node-positive lesions (P = 0.009). Though mean ki was not found to be significant for the hormone receptors status (P = 0.08), it showed the best correlation compared to the other parameters (P = 0.8 for DV and P = 0.1 for SUV). Spearman's correlation test, area under the curve (AUC) and mismatch percentage also revealed ki to predict tumor grade (AUC, 0.95; r = 0.7; P = 0.0001), HER-2 neu status and molecular subtypes (AUC, 0.81; r = 0.49 and P = 0.01) along with the hormone receptors status (AUC, 0.83; r = 0.32; P = 0.1). The mean DV failed to show any association with any of the biological or anatomical staging parameters. Though ki was found to be comparable to that of SUV in almost all the assessed parameters, it appeared to be better for predicting hormone receptors status even though both parameters were not statistically significant.

CONCLUSION

Our initial observation in a small cohort of breast cancer patients suggests that ki is promising in stratifying primary breast cancer lesions according to the tumor grade and biological characteristics.

Mixing Matrix-corrected Whole-body Pharmacokinetic Modeling Using Longitudinal Micro-computed Tomography and Fluorescence-mediated Tomography

PURPOSE

Pharmacokinetic modeling can be applied to quantify the kinetics of fluorescently labeled compounds using longitudinal micro-computed tomography and fluorescence-mediated tomography (μCT-FMT). However, fluorescence blurring from neighboring organs or tissues and the vasculature within tissues impede the accuracy in the estimation of kinetic parameters. Contributions of elimination and retention activities of fluorescent probes inside the kidneys and liver can be hard to distinguish by a kinetic model. This study proposes a deconvolution approach using a mixing matrix to model fluorescence contributions to improve whole-body pharmacokinetic modeling.

PROCEDURES

In the kinetic model, a mixing matrix was applied to unmix the fluorescence blurring from neighboring tissues and blood vessels and unmix the fluorescence contributions of elimination and retention in the kidney and liver compartments. Accordingly, the kinetic parameters of the hepatobiliary and renal elimination routes and five major retention sites (the kidneys, liver, bone, spleen, and lung) were investigated in simulations and in an in vivo study. In the latter, the pharmacokinetics of four fluorescently labeled compounds (indocyanine green (ICG), HITC-iodide-microbubbles (MB), Cy7-nanogels (NG), and OsteoSense 750 EX (OS)) were evaluated in BALB/c nude mice.

RESULTS

In the simulations, the corrected modeling resulted in lower relative errors and stronger linear relationships (slopes close to 1) between the estimated and simulated parameters, compared to the uncorrected modeling. For the in vivo study, MB and NG showed significantly higher hepatic retention rates (P<0.05 and P<0.05, respectively), while OS had smaller renal and hepatic retention rates (P<0.01 and P<0.01, respectively). Additionally, the bone retention rate of OS was significantly higher (P<0.01).

CONCLUSIONS

The mixing matrix correction improves pharmacokinetic modeling and thus enables a more accurate assessment of the biodistribution of fluorescently labeled pharmaceuticals by μCT-FMT.

Noninvasive quantification of nonhuman primate dynamic 18F-FDG PET imaging

¹⁸F-FDG uptake rate constant K_i is the main physiology parameter measured in dynamic PET studies. A model-independent graphical analysis using Patlak plot with plasma input function (PIF) is a standard approach used to estimate K_i . The PIF is the ¹⁸F-FDG time activity curve (TAC) in plasma that is obtained by serial arterial blood sampling. The purpose of the study is to evaluate a Patlak plot-based optimization approach with reduced blood samples for noninvasive quantification of dynamic ¹⁸F-FDG PET imaging. Eight 60 min rhesus monkey brain dynamic ¹⁸F-FDG PET scans with arterial blood samples were collected. The measured PIF (mPIF) was determined by arterial blood samples. TACs of seven cerebral regions of interest were generated from each study. With a given number of blood samples, the population-based PIF (pPIF) was determined by either interpolation or extrapolation method using scale calibrated population mean of normalized PIF. The optimal sampling scheme with given blood sample size was determined by maximizing the correlations between the K_i estimated from pPIF and those obtained by mPIF. A leave-two-out cross-validation method was used for evaluation. The linear correlations between the K_i estimates from pPIF with optimal sampling schemes and those from mPIF were: K_i (pPIF 1 sample at 40 min) = 1.015 K_i (mPIF) - 0.000, R ² = 0.974; K_i (pPIF 2 samples at 35 and 50 min) = 1.052 K_i (mPIF) - 0.001, R ² = 0.976; K_i (pPIF 3 samples at 12, 40, and 50 min) = 1.030 K_i (mPIF) - 0.000, R ² = 0.985; and K_i (pPIF 4 samples at 10, 20, 40, and 50 min) = 1.016 K_i (mPIF)- 0.000, R ² = 0.993. As the sample size became greater or equal to 4, the K_i estimates from pPIF with the optimal protocol were almost identical to those from mPIF. The Patlak plot-based optimization approach is a reliable method to estimate PIF for noninvasive quantification of non-human primate dynamic ¹⁸F-FDG PET imaging and is potentially extendable to further translational human studies.

Analysis of Four-Dimensional Data for Total Body PET Imaging

The high sensitivity and total-body coverage of total-body PET scanners will be valuable for a number of clinical and research applications outlined in this article.

Symmetry and spatial distribution of muscle glucose uptake in the lower limbs during walking measured using FDG-PET

Purpose

This study aimed to elucidate whether muscle activity (in terms of glucose uptake) between the legs can be considered symmetrical during walking. Furthermore, we aimed to determine whether the [¹⁸F]-fluorodeoxyglucose was distributed heterogeneously throughout each muscle, and if so, whether areas of high uptake would be clustered.

Methods

Ten healthy participants walked on a treadmill at self-selected comfortable walking speed for a total of 90 minutes, 60 minutes before and 30 minutes after intravenous injection of 50 MBq [¹⁸F]-fluorodeoxyglucose. Thereafter, a positron emission tomography/computed tomography scan of the lower limb was acquired. Three-dimensional muscle contours of 78 (= 39x2) muscles of the left and right lower limb were semi-automatically determined from magnetic resonance imaging scans. After non-rigid registration, those muscle contours were used to extract [¹⁸F]-fluorodeoxyglucose uptake from the positron emission tomography scans.

Results

Large asymmetries were observed in the lower leg muscles (e.g. median absolute asymmetry index of 42% in the gastrocnemius medialis) and in the gluteus minimus (30% asymmetry) and gluteus medius (15% asymmetry), whereas the uptake in the thighs was relatively symmetrical between the limbs (<6% asymmetry). These were not related to limb-dominance nor to inter-limb differences in muscle volume. The [¹⁸F]-fluorodeoxyglucose distribution was not distributed normally; most voxels had a relatively low standardized uptake value, and a minority of voxels had a relatively high standardized uptake value. The voxels with higher [¹⁸F]-fluorodeoxyglucose uptake were distributed heterogeneously; they were clustered in virtually all muscles.

Conclusion

The findings in this study challenge the common assumption of symmetry in muscle activity between the limbs in healthy subjects. The clustering of voxels with high uptake suggests that even in this prolonged repetitive task, different spatial regions of muscles contribute differently to walking than others.

A new methodology to derive 3D kinetic parametric FDG PET images based on a mathematical approach integrating an error model of measurement

Background

In FDG-PET, SUV images are hampered by large potential biases. Our aim was to develop an alternative method (ParaPET) to generate 3D kinetic parametric FDG-PET images easy to perform in clinical oncology.

Methods

The key points of our method are the use of a new error model of PET measurement extracted from a late dynamic PET acquisition of 15 min, centered over the lesion and an image-derived input function (IDIF). The 15-min acquisition is reconstructed to obtain five images of FDG mean activity concentration and images of its variance to model errors of PET measurement. Our approach is carried out on each voxel to derive 3D kinetic parameter images. ParaPET was evaluated and compared to Patlak analysis as a reference. Hunter and Barbolosi methods (Barbolosi-Bl: with blood samples or Barbolosi-Im: with IDIF) were also investigated and compared to Patlak. Our evaluation was carried on K_i index, the net influx rate and its maximum value in the lesion (K_i,max).

Results

This parameter was obtained from 41 non-small cell lung cancer lesions associated with 4 to 5 blood samples per patient, required for the Patlak analysis. Compare to Patlak, the median relative difference and associated range (median; [min;max]) in K_i,max estimates were not statistically significant (Wilcoxon test) for ParaPET (− 3.0%; [− 31.9%; 47.3%]; p = 0.08) but statistically significant for Barbolosi-Bl (− 8.0%; [− 30.8%; 53.7%]; p = 0.001), Barbolosi-Im (− 7.9%; [− 38.4%; 30.6%]; p = 0.007) or Hunter (32.8%; [− 14.6%; 132.2%]; p < 10^− 5). In the Bland-Altman plots, the ratios between the four methods and Patlak are not dependent of the K_i magnitude, except for Hunter. The 95% limits of agreement are comparable for ParaPET (34.7%), Barbolosi-Bl (30.1%) and Barbolosi-Im (30.8%), lower to Hunter (81.1%). In the 25 lesions imaged before and during the radio-chemotherapy, the decrease in the FDG uptake (ΔSUV_max or ΔK_i,max) is statistically more important (p < 0.02, Wilcoxon one-tailed test) when estimated from the K_i images than from the SUV images (additional median variation of − 2.3% [− 52.6%; + 19.1%] for ΔK_i,max compared to ΔSUV_max).

Conclusion

None of the four methodologies is yet ready to replace the Patlak approach, and further improvements are still required. Nevertheless, ParaPET remains a promising approach, offering a non-invasive alternative to methods based on multiple blood samples and only requiring a late PET acquisition. It allows deriving K_i values, highly correlated and presenting the lowest relative bias with Patlak estimates, in comparison to the other methods we evaluated. Moreover, ParaPET gives access to quantitative information at the pixel level, which needs to be evaluated in the perspective of radiomic and tumour response.

Trial registration

NCT 02821936; May 2016.

Assessment of a statistical AIF extraction method for dynamic PET studies with 15O water and 18F fluorodeoxyglucose in locally advanced breast cancer patients

Blood flow-metabolism mismatch from dynamic positron emission tomography (PET) studies with O15-labeled water (H2O) and F18-labeled fluorodeoxyglucose (FDG) has been shown to be a promising diagnostic for locally advanced breast cancer (LABCa) patients. The mismatch measurement involves kinetic analysis with the arterial blood time course (AIF) as an input function. We evaluate the use of a statistical method for AIF extraction (SAIF) in these studies. Fifty three LABCa patients had dynamic PET studies with H2O and FDG. For each PET study, two AIFs were recovered, an SAIF extraction and also a manual extraction based on a region of interest placed over the left ventricle (LV-ROI). Blood flow-metabolism mismatch was obtained with each AIF, and kinetic and prognostic reliability comparisons were made. Strong correlations were found between kinetic assessments produced by both AIFs. SAIF AIFs retained the full prognostic value, for pathologic response and overall survival, of LV-ROI AIFs.

A virtual clinical trial comparing static versus dynamic PET imaging in measuring response to breast cancer therapy

We developed a method to evaluate variations in the PET imaging process in order to characterize the relative ability of static and dynamic metrics to measure breast cancer response to therapy in a clinical trial setting. We performed a virtual clinical trial by generating 540 independent and identically distributed PET imaging study realizations for each of 22 original dynamic fluorodeoxyglucose (¹⁸F-FDG) breast cancer patient studies pre- and post-therapy. Each noise realization accounted for known sources of uncertainty in the imaging process, such as biological variability and SUV uptake time. Four definitions of SUV were analyzed, which were SUVmax, SUVmean, SUVpeak, and SUV50%. We performed a ROC analysis on the resulting SUV and kinetic parameter uncertainty distributions to assess the impact of the variability on the measurement capabilities of each metric. The kinetic macro parameter, K _i , showed more variability than SUV (mean CV K _i   =  17%, SUV  =  13%), but K _i pre- and post-therapy distributions also showed increased separation compared to the SUV pre- and post-therapy distributions (mean normalized difference K _i   =  0.54, SUV  =  0.27). For the patients who did not show perfect separation between the pre- and post-therapy parameter uncertainty distributions (ROC AUC  <  1), dynamic imaging outperformed SUV in distinguishing metabolic change in response to therapy, ranging from 12 to 14 of 16 patients over all SUV definitions and uptake time scenarios (p  <  0.05). For the patient cohort in this study, which is comprised of non-high-grade ER+  tumors, K _i outperformed SUV in an ROC analysis of the parameter uncertainty distributions pre- and post-therapy. This methodology can be applied to different scenarios with the ability to inform the design of clinical trials using PET imaging.

The extent to which standardized uptake values reflect FDG phosphorylation in the liver and spleen as functions of time after injection of 18F-fluorodeoxyglucose

Purpose

In FDG PET/CT, standardized uptake value (SUV) is used to measure metabolic activity but detects un-phosphorylated FDG as well as phosphorylated FDG (FDG6P). Our aim was to determine the proportions of intrahepatic and intrasplenic FDG that are phosphorylated after FDG injection and compare them with SUVs.

Methods

Sixty patients undergoing whole-body PET/CT 60 min post-injection of FDG first had dynamic PET imaging for 30 min with measurement of hepatic and splenic FDG clearances using Patlak-Rutland analysis. The gradient of the Patlak-Rutland plot, which is proportional to clearance (Ki), was normalized to the intercept, which is proportional to FDG distribution volume (V(0)) with the same proportionality constant. Using measured values of Ki/V(0), FDG6P/FDG ratios as functions of time in the two organs were measured for assumed FDG blood disappearance half-times of 40, 50 and 60 min. Hepatic and splenic SUVs were measured from whole-body PET/CT.

Results

The mean (SD) Ki/V(0) was 0.0036 (0.0021) and 0.0060 (0.0041) ml/min/ml for the liver and spleen, respectively, but the hepatic SUV was 1.36-fold higher than the splenic SUV. This discrepancy was explained by the hepatic V(0) being 1.6-fold higher than the splenic V(0). The percentages of FDG phosphorylated 60 min post-injection were 27, 25 and 23% for the liver and 39, 36 and 34% for the spleen, for blood clearance half-times of 40, 50 and 60 min, respectively. SUV indices correlated poorly with Ki/V(0) for both organs.

Conclusions

SUV is largely determined by un-phosphorylated FDG in dynamic exchange with blood FDG, explaining the poor correlations between SUV indices and Ki/V(0).

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge

Dynamic contrast-enhanced MRI (DCE-MRI) has been widely used in tumor detection and therapy response evaluation. Pharmacokinetic analysis of DCE-MRI time-course data allows estimation of quantitative imaging biomarkers such as K^trans(rate constant for plasma/interstitium contrast reagent (CR) transfer) and v_e (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical prostate imaging islimited, with uncertainty in arterial input function (AIF, i.e., the time rate of change of the concentration of CR in the blood plasma) determination being one of the primary reasons. In this multicenter data analysis challenge to assess the effects of variations in AIF quantification on estimation of DCE-MRI parameters, prostate DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Each center used its site-specific method to determine the individual AIF from each data set and submitted the results to the managing center. Along with a literature population averaged AIF, these AIFs and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic analysis of the DCE-MRI data sets using the Tofts model (TM). All other variables including tumor region of interest (ROI) definition and pre-contrast T₁ were kept the same to evaluate parameter variations caused by AIF variations only. Considerable pharmacokinetic parameter variations were observed with the within-subject coefficient of variation (wCV) of K^trans obtained with unadjusted AIFs as high as 0.74. AIF-caused variations were larger in K^trans than v_e and both were reduced when reference-tissue-adjusted AIFs were used. The parameter variations were largely systematic, resulting in nearly unchanged parametric map patterns. The CR intravasation rate constant, k_ep (= K^trans/v_e), was less sensitive to AIF variation than K^trans (wCV for unadjusted AIFs: 0.45 for k_epvs. 0.74 for K^trans), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than K^trans.

Effect of 18F-FDG uptake time on lesion detectability in PET imaging of early stage breast cancer

Prior reports have suggested that delayed FDG-PET oncology imaging can improve the contrast-to-noise ratio (CNR) for known lesions. Our goal was to estimate realistic bounds for lesion detectability for static measurements with one to four hours between FDG injection and image acquisition. Tumor and normal tissue kinetic model parameters were estimated from dynamic PET studies of patients with early stage breast cancer. These were used to generate time-activity curves (TACs) out to four hours, for which we assumed both nonreversible and reversible models with different rates of FDG dephosphorylation (k4). For each pair of tumor and normal tissue TACs, 600 PET sinogram realizations were generated, and images were reconstructed using OSEM. Test statistics for each tumor and normal tissue region of interest were output from the computer model observers and evaluated using an ROC analysis with the calculated AUC providing a measure of lesion detectability. For the nonreversible model (k4 = 0), the AUC increased in 11/23 (48%) of patients for one to two hours after the current standard post-radiotracer injection imaging window of one hour. This improvement was driven by increased tumor/normal tissue contrast before the impact of increased noise due to radiotracer decay began to dominate the imaging signal. As k4 was increased from 0 to 0.01 min-1, the time of maximum detectability shifted earlier, as the decreasing FDG concentration in the tumor lowered the CNR. These results imply that delayed PET imaging may reveal low-conspicuity lesions that would have otherwise gone undetected.

An update on novel quantitative techniques in the context of evolving whole-body PET imaging

Since its foundation PET has established itself as one of the standard imaging modalities enabling the quantitative assessment of molecular targets in vivo. In the past two decades, quantitative PET has become a necessity in clinical oncology. Despite introduction of various measures for quantification and correction of PET parameters, there is debate on the selection of the appropriate methodology in specific diseases and conditions. In this review, we have focused on these techniques with special attention to topics such as static and dynamic whole body PET imaging, tracer kinetic modeling, global disease burden, texture analysis and radiomics, dual time point imaging and partial volume correction.

VOXEL-LEVEL MAPPING OF TRACER KINETICS IN PET STUDIES: A STATISTICAL APPROACH EMPHASIZING TISSUE LIFE TABLES

Most radiotracers used in dynamic positron emission tomography (PET) scanning act in a linear time-invariant fashion so that the measured time-course data are a convolution between the time course of the tracer in the arterial supply and the local tissue impulse response, known as the tissue residue function. In statistical terms the residue is a life table for the transit time of injected radiotracer atoms. The residue provides a description of the tracer kinetic information measurable by a dynamic PET scan. Decomposition of the residue function allows separation of rapid vascular kinetics from slower blood-tissue exchanges and tissue retention. For voxel-level analysis, we propose that residues be modeled by mixtures of nonparametrically derived basis residues obtained by segmentation of the full data volume. Spatial and temporal aspects of diagnostics associated with voxel-level model fitting are emphasized. Illustrative examples, some involving cancer imaging studies, are presented. Data from cerebral PET scanning with 18F fluoro-deoxyglucose (FDG) and 15O water (H2O) in normal subjects is used to evaluate the approach. Cross-validation is used to make regional comparisons between residues estimated using adaptive mixture models with more conventional compartmental modeling techniques. Simulations studies are used to theoretically examine mean square error performance and to explore the benefit of voxel-level analysis when the primary interest is a statistical summary of regional kinetics. The work highlights the contribution that multivariate analysis tools and life-table concepts can make in the recovery of local metabolic information from dynamic PET studies, particularly ones in which the assumptions of compartmental-like models, with residues that are sums of exponentials, might not be certain.