Dynamic Positron Emission Tomography Data-Driven Analysis Using Sparse Bayesian Learning

Peng, Jyh-Ying; Aston, John A. D.; Gunn, Roger N.; Liou, Cheng-Yuan; Ashburner, John

doi:10.1109/tmi.2008.922185

Cited by 26 publications

(9 citation statements)

References 26 publications

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“…The V T estimates by FPCA are about 12.4% lower than those by DEPICT, while pDEPICT was similar to that of DEPICT (approximately 2.2% lower, data not shown). Positive biases of 10% or more are not uncommon for parameter values in the range of those present here (bias is parameter dependent) for PET compartmental models when analyzed with spectral analysis (see Peng et al 2008 ), and as such the estimates provided by FPCA are closer to what might be expected from previous simulation results. Thus, the FPCA yields results which are more quantitatively plausible for comparison across a population.…”

Section: Measured 11 C-diprenorphine Datasupporting

confidence: 83%

“…Presmoothing decreases the noise in the data, hence can reduce overall potential biases in further analysis, as even though smoothing itself introduces a small bias, in many nonlinear situations, parameter or functional biases can be noise level dependent. In parametric nonlinear models such as compartmental models, this is well known (Peng et al 2008 ), while here the errors in the observed functions are somewhat similar to those in measurement error models, which yield biases in traditional regression analysis. The presmoothing also produces functions that are smoother than the original data, making subsequent deconvolution easier, as large independent measurement errors tend to result in considerable instability in deconvolution settings.…”

Section: Introductionsupporting

confidence: 56%

“…Therefore, compartmental analysis can produce both biased and incomparable estimates across the brain. In addition, fitting methods which are stable for large numbers of voxels, such as nonnegative least squares, tend to have parameter dependent bias, while methods such as nonlinear least squares tend to be somewhat unstable (Peng et al 2008 ).…”

Section: Introductionmentioning

confidence: 99%

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A Functional Approach to Deconvolve Dynamic Neuroimaging Data

Jiang¹,

Aston²,

Wang³

2016

Journal of the American Statistical Association

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Positron emission tomography (PET) is an imaging technique which can be used to investigate chemical changes in human biological processes such as cancer development or neurochemical reactions. Most dynamic PET scans are currently analyzed based on the assumption that linear first-order kinetics can be used to adequately describe the system under observation. However, there has recently been strong evidence that this is not the case. To provide an analysis of PET data which is free from this compartmental assumption, we propose a nonparametric deconvolution and analysis model for dynamic PET data based on functional principal component analysis. This yields flexibility in the possible deconvolved functions while still performing well when a linear compartmental model setup is the true data generating mechanism. As the deconvolution needs to be performed on only a relative small number of basis functions rather than voxel by voxel in the entire three-dimensional volume, the methodology is both robust to typical brain imaging noise levels while also being computationally efficient. The new methodology is investigated through simulations in both one-dimensional functions and 2D images and also applied to a neuroimaging study whose goal is the quantification of opioid receptor concentration in the brain.

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Section: Measured 11 C-diprenorphine Datasupporting

confidence: 83%

Section: Introductionsupporting

confidence: 56%

Section: Introductionmentioning

confidence: 99%

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A Functional Approach to Deconvolve Dynamic Neuroimaging Data

Jiang¹,

Aston²,

Wang³

2016

Journal of the American Statistical Association

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“…Positron emission tomography (PET) image reconstruction methods based on list-mode acquisition have many advantages compared with those using sinograms, especially for time-of-flight (ToF), 1-3 high resolution, 4,5 and dynamic 6 PET data. List-mode data can be reconstructed using iterative algorithms such as maximum likelihood expectation maximization (MLEM) (Ref.…”

Section: Introductionmentioning

confidence: 99%

Fully 3D list‐mode time‐of‐flight PET image reconstruction on GPUs using CUDA

et al. 2011

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A list-mode ToF OSEM library was developed on the GPU-CUDA platform. Our studies show that the GPU reformulation is considerably faster than a single-threaded reference CPU method especially for ToF processing, while producing virtually identical images. This new method can be easily adapted to enable more advanced algorithms for high resolution PET reconstruction based on additional information such as depth of interaction (DoI), photon energy, and point spread functions (PSFs).

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“…However, the definition of an adequate penalty function to use in practice has proved to be difficult. This concept was further developed by Gunn and colleagues in 2002 and it originated what are now called basis pursuit methods [31, 32]. In 1994, Turkheimer and colleagues proposed a high-pass filter for equilibrating components, with the aim of improving estimates of α 0 and thus determining a more accurate and precise estimate of regional cerebral metabolic rate for glucose in PET studies [21].…”

Section: Methods Implementationmentioning

confidence: 99%

Spectral Analysis of Dynamic PET Studies: A Review of 20 Years of Method Developments and Applications

Veronese

Rizzo

Bertoldo

et al. 2016

Computational and Mathematical Methods in Medicine

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In Positron Emission Tomography (PET), spectral analysis (SA) allows the quantification of dynamic data by relating the radioactivity measured by the scanner in time to the underlying physiological processes of the system under investigation. Among the different approaches for the quantification of PET data, SA is based on the linear solution of the Laplace transform inversion whereas the measured arterial and tissue time-activity curves of a radiotracer are used to calculate the input response function of the tissue. In the recent years SA has been used with a large number of PET tracers in brain and nonbrain applications, demonstrating that it is a very flexible and robust method for PET data analysis. Differently from the most common PET quantification approaches that adopt standard nonlinear estimation of compartmental models or some linear simplifications, SA can be applied without defining any specific model configuration and has demonstrated very good sensitivity to the underlying kinetics. This characteristic makes it useful as an investigative tool especially for the analysis of novel PET tracers. The purpose of this work is to offer an overview of SA, to discuss advantages and limitations of the methodology, and to inform about its applications in the PET field.

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Dynamic Positron Emission Tomography Data-Driven Analysis Using Sparse Bayesian Learning

Cited by 26 publications

References 26 publications

A Functional Approach to Deconvolve Dynamic Neuroimaging Data

A Functional Approach to Deconvolve Dynamic Neuroimaging Data

Fully 3D list‐mode time‐of‐flight PET image reconstruction on GPUs using CUDA

Spectral Analysis of Dynamic PET Studies: A Review of 20 Years of Method Developments and Applications

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