Dataset Information

Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data.

ABSTRACT: Exploratory (i.e., voxelwise) spatial methods are commonly used in neuroimaging to identify areas that show an effect when a region-of-interest (ROI) analysis cannot be performed because no strong a priori anatomical hypothesis exists. However, noise at a single voxel is much higher than noise in a ROI making noise management critical to successful exploratory analysis. This work explores how preprocessing choices affect the bias and variability of voxelwise kinetic modeling analysis of brain positron emission tomography (PET) data. These choices include the use of volume- or cortical surface-based smoothing, level of smoothing, use of voxelwise partial volume correction (PVC), and PVC masking threshold. PVC was implemented using the Muller-Gartner method with the masking out of voxels with low gray matter (GM) partial volume fraction. Dynamic PET scans of an antagonist serotonin-4 receptor radioligand ([(11)C]SB207145) were collected on sixteen healthy subjects using a Siemens HRRT PET scanner. Kinetic modeling was used to compute maps of non-displaceable binding potential (BPND) after preprocessing. The results showed a complicated interaction between smoothing, PVC, and masking on BPND estimates. Volume-based smoothing resulted in large bias and intersubject variance because it smears signal across tissue types. In some cases, PVC with volume smoothing paradoxically caused the estimated BPND to be less than when no PVC was used at all. When applied in the absence of PVC, cortical surface-based smoothing resulted in dramatically less bias and the least variance of the methods tested for smoothing levels 5mm and higher. When used in combination with PVC, surface-based smoothing minimized the bias without significantly increasing the variance. Surface-based smoothing resulted in 2-4 times less intersubject variance than when volume smoothing was used. This translates into more than 4 times fewer subjects needed in a group analysis to achieve similarly powered statistical tests. Surface-based smoothing has less bias and variance because it respects cortical geometry by smoothing the PET data only along the cortical ribbon and so does not contaminate the GM signal with that of white matter and cerebrospinal fluid. The use of surface-based analysis in PET should result in substantial improvements in the reliability and detectability of effects in exploratory PET analysis, with or without PVC.

SUBMITTER: Greve DN

PROVIDER: S-EPMC4008670 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data.

Greve Douglas N DN Svarer Claus C Fisher Patrick M PM Feng Ling L Hansen Adam E AE Baare William W Rosen Bruce B Fischl Bruce B Knudsen Gitte M GM

NeuroImage 20131219

Exploratory (i.e., voxelwise) spatial methods are commonly used in neuroimaging to identify areas that show an effect when a region-of-interest (ROI) analysis cannot be performed because no strong a priori anatomical hypothesis exists. However, noise at a single voxel is much higher than noise in a ROI making noise management critical to successful exploratory analysis. This work explores how preprocessing choices affect the bias and variability of voxelwise kinetic modeling analysis of brain po ...[more]

PMID: 24361666

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Project description:The variances and biases inherent in quantifying PET tracer uptake from instrumentation factors are needed to ascertain the significance of any measured differences such as in quantifying response to therapy. The authors studied the repeatability and reproducibility of serial PET measures of activity as a function of object size, acquisition, reconstruction, and analysis method on one scanner and at three PET centers using a single protocol with long half-life phantoms.The authors assessed standard deviations (SDs) and mean biases of consecutive measures of PET activity concentrations in a uniform phantom and a NEMA NU-2 image quality (IQ) phantom filled with 9 months half-life 68Ge in an epoxy matrix. Activity measurements were normalized by dividing by a common decay corrected true value and reported as recovery coefficients (RCs). Each experimental set consisted of 20 consecutive PET scans of either a stationary phantom to evaluate repeatability or a repositioned phantom to assess reproducibility. One site conducted a comprehensive series of repeatability and reproducibility experiments, while two other sites repeated the reproducibility experiments using the same IQ phantom. An equation was derived to estimate the SD of a new PET measure from a known SD based on the ratios of available coincident counts between the two PET measures.For stationary uniform phantom scans, the SDs of maximum RCs were three to five times less than predicted for uncorrelated pixels within circular regions of interest (ROIs) with diameters ranging from 1 to 15 cm. For stationary IQ phantom scans from 1 cm diameter ROIs, the average SDs of mean and maximum RCs ranged from 1.4% to 8.0%, depending on the methods of acquisition and reconstruction (coefficients of variation range 2.5% to 9.8%). Similar SDs were observed for both analytic and iterative reconstruction methods (p > or = 0.08). SDs of RCs for 2D acquisitions were significantly higher than for 3D acquisitions (p < or =s 0.008) for same acquisition and processing parameters. SDs of maximum RCs were larger than corresponding mean values for stationary IQ phantom scans ( < or = 0.02), although the magnitude of difference is reduced due to noise correlations in the image. Increased smoothing decreased SDs ( < or =s 0.045) and decreased maximum and mean RCs (p < or = 0.02). Reproducibility of GE DSTE, Philips Gemini TF, and Siemens Biograph Hi-REZ PET/CT scans of the same IQ phantom, with similar acquisition, reconstruction, and repositioning among 20 scans, were, in general, similar (mean and maximum RC SD range 2.5% to 4.8%).Short-term scanner variability is low compared to other sources of error. There are tradeoffs in noise and bias depending on acquisition, processing, and analysis methods. The SD of a new PET measure can be estimated from a known SD if the ratios of available coincident counts between the two PET scanner acquisitions are known and both employ the same ROI definition. Results suggest it is feasible to use PET/CTs from different vendors and sites in clinical trials if they are properly cross-calibrated.

Project description:BackgroundStable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly [Formula: see text]-enriched glucose ([Formula: see text]-Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups.ResultsWe propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts' knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux .ConclusionsOur new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data.

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Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data.

Publications

Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data.

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