Dataset Information

Functional Data Analysis of Dynamic PET Data.

ABSTRACT: One application of positron emission tomography (PET), a nuclear imaging technique, in neuroscience involves in vivo estimation of the density of various proteins (often, neuroreceptors) in the brain. PET scanning begins with the injection of a radiolabeled tracer that binds preferentially to the target protein; tracer molecules are then continuously delivered to the brain via the bloodstream. By detecting the radioactive decay of the tracer over time, dynamic PET data are constructed to reflect the concentration of the target protein in the brain at each time. The fundamental problem in the analysis of dynamic PET data involves estimating the impulse response function (IRF), which is necessary for describing the binding behavior of the injected radiotracer. Virtually all existing methods have three common aspects: summarizing the entire IRF with a single scalar measure; modeling each subject separately; and the imposition of parametric restrictions on the IRF. In contrast, we propose a functional data analytic approach that regards each subject's IRF as the basic analysis unit, models multiple subjects simultaneously, and estimates the IRF nonparametrically. We pose our model as a linear mixed effect model in which population level fixed effects and subject-specific random effects are expanded using a B-spline basis. Shrinkage and roughness penalties are incorporated in the model to enforce identifiability and smoothness of the estimated curves, respectively, while monotonicity and non-negativity constraints impose biological information on estimates. We illustrate this approach by applying it to clinical PET data with subjects belonging to three diagnosic groups. We explore differences among groups by means of pointwise confidence intervals of the estimated mean curves based on bootstrap samples.

SUBMITTER: Chen Y

PROVIDER: S-EPMC6707736 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Functional Data Analysis of Dynamic PET Data.

Chen Yakuan Y Goldsmith Jeff J Ogden R Todd RT

Journal of the American Statistical Association 20181026 526

One application of positron emission tomography (PET), a nuclear imaging technique, in neuroscience involves in vivo estimation of the density of various proteins (often, neuroreceptors) in the brain. PET scanning begins with the injection of a radiolabeled tracer that binds preferentially to the target protein; tracer molecules are then continuously delivered to the brain via the bloodstream. By detecting the radioactive decay of the tracer over time, dynamic PET data are constructed to reflect ...[more]

PMID: 31447493

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Project description:Highly constrained backprojection-local reconstruction (HYPR-LR) has made a dramatic impact on magnetic resonance angiography (MRA) and shows promise for positron emission tomography (PET) because of the improvements in the signal-to-noise ratio (SNR) it provides dynamic images. For PET in particular, HYPR-LR could improve kinetic analysis methods that are sensitive to noise. In this work, the authors closely examine the performance of HYPR-LR in the context of kinetic analysis, they develop an implementation of the algorithm that can be tailored to specific PET imaging tasks to minimize bias and maximize improvement in variance, and they provide a framework for validating the use of HYPR-LR processing for a particular imaging task.HYPR-LR can introduce errors into non sparse PET studies that might bias kinetic parameter estimates. An implementation of HYPR-LR is proposed that uses multiple temporally summed composite images that are formed based on the kinetics of the tracer being studied (HYPR-LR-MC). The effects of HYPR-LR-MC and of HYPR-LR using a full composite formed with all the frames in the study (HYPR-LR-FC) on the kinetic analysis of Pittsburgh compound-B ([11C]-PIB) are studied. HYPR-LR processing is compared to spatial smoothing. HYPR-LR processing was evaluated using both simulated and human studies. Nondisplaceable binding potential (BP(ND)) parametric images were generated from fifty noise realizations of the same numerical phantom and eight [(11)C]-PIB positive human scans before and after HYPR-LR processing or smoothing using the reference region Logan graphical method and receptor parametric mapping (RPM2). The bias and coefficient of variation in the frontal and parietal cortex in the simulated parametric images were calculated to evaluate the absolute performance of HYPR-LR processing. Bias in the human data was evaluated by comparing parametric image BP(ND) values averaged over large regions of interest (ROIs) to Logan estimates of the BP(ND) from TACs averaged over the same ROIs. Variance was assessed qualitatively in the parametric images and semiquantitatively by studying the correlation between voxel BP(ND) estimates from Logan analysis and RPM2.Both the simulated and human data show that HYPR-LR-FC overestimates BP(ND) values in regions of high [(11)C]-PIB uptake. HYPR-LR-MC virtually eliminates this bias. Both implementations of HYPR-LR reduce variance in the parametric images generated with both Logan analysis and RPM2, and HYPR-LR-FC provides a greater reduction in variance. This reduction in variance nearly eliminates the noise-dependent Logan bias. The variance reduction is greater for the Logan method, particularly for HYPR-LR-MC, and the variance in the resulting Logan images is comparable to that in the RPM2 images. HYPR-LR processing compares favorably with spatial smoothing, particularly when the data are analyzed with the Logan method, as it provides a reduction in variance with no loss of spatial resolution.HYPR-LR processing shows significant potential for reducing variance in parametric images, and can eliminate the noise-dependent Logan bias. HYPR-LR-FC processing provides the greatest reduction in variance but introduces a positive bias into the BP(ND) of high-uptake border regions. The proposed method for forming HYPR composite images, HYPR-LR-MC, eliminates this bias at the cost of less variance reduction.

Project description:Hypoxic tumors exhibit increased resistance to radiation, chemical, and immune therapies. 18F-fluoromisonidazole (18F-FMISO) PET is a noninvasive, quantitative imaging technique used to evaluate the magnitude and spatial distribution of tumor hypoxia. In this study, pharmacokinetic analysis (PKA) of 18F-FMISO dynamic PET extended to 3 h after injection is reported for the first time, to our knowledge, in stage III-IV non-small cell lung cancer (NSCLC) patients. Methods: Sixteen patients diagnosed with NSCLC underwent 2 PET/CT scans (1-3 d apart) before radiation therapy: a 3-min static 18 F-FDG and a dynamic 18F-FMISO scan lasting 168 ± 15 min. The latter data were acquired in 3 serial PET/CT dynamic imaging sessions, registered with each other and analyzed using pharmacokinetic modeling software. PKA was performed using a 2-tissue, 3-compartment irreversible model, and kinetic parameters were estimated for the volumes of interest determined using coregistered 18F-FDG images for both the volume of interest-averaged and the voxelwise time-activity curves for each patient's lesions, normal lung, and muscle. Results: We derived average values of 18F-FMISO kinetic parameters for NSCLC lesions as well as for normal lung and muscle. We also investigated the correlation between the trapping rate (k3) and delivery rate (K1), influx rate (Ki ) constants, and tissue-to-blood activity concentration ratios (TBRs) for all tissues. Lesions had trapping rates 1.6 times larger, on average, than those of normal lung and 4.4 times larger than those in muscle. Additionally, for almost all cases, k3 and Ki had a significant strong correlation for all tissue types. The TBR-k3 correlation was less straightforward, showing a moderate to strong correlation for only 41% of lesions. Finally, K1-k3 voxelwise correlations for tumors were varied, but negative for 76% of lesions, globally exhibiting a weak inverse relationship (average R = -0.23 ± 0.39). However, both normal tissue types exhibited significant positive correlations for more than 60% of patients, with 41% having moderate to strong correlations (R > 0.5). Conclusion: All lesions showed distinct 18F-FMISO uptake. Variable 18F-FMISO delivery was observed across lesions, as indicated by the variable values of the kinetic rate constant K1 Except for 3 cases, some degree of hypoxia was apparent in all lesions based on their nonzero k3 values.

Dataset Information

Functional Data Analysis of Dynamic PET Data.

Publications

Functional Data Analysis of Dynamic PET Data.

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