The Experts below are selected from a list of 18276 Experts worldwide ranked by ideXlab platform
Guobao Wang - One of the best experts on this subject based on the ideXlab platform.
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High Temporal-Resolution Dynamic PET Image Reconstruction Using a New Spatiotemporal Kernel Method
IEEE Transactions on Medical Imaging, 2019Co-Authors: Guobao WangAbstract:Current clinical dynamic PET has an effective temporal resolution of 5-10 seconds, which can be adequate for traditional compartmental modeling but is inadequate for exploiting the benefit of more advanced tracer kinetic modeling for characterization of diseases (e.g., cancer and heart disease). There is a need to improve dynamic PET to allow fine temporal sampling of 1-2 seconds. However, the reconstruction of these short-time frames from tomographic data is extremely challenging as the count level of each frame is very low and high noise presents in both spatial and temporal domains. Previously, the Kernel framework has been developed and demonstrated as a statistically efficient approach to utilizing image prior for low-count PET image reconstruction. Nevertheless, the existing Kernel Methods mainly explore spatial correlations in the data and only have a limited ability in suppressing temporal noise. In this paper, we propose a new Kernel Method which extends the previous spatial Kernel Method to the general spatiotemporal domain. The new Kernelized model encodes both spatial and temporal correlations obtained from image prior information and are incorporated into the PET forward projection model to improve themaximumlikelihood(ML) image reconstruction. Computer simulations and an application to real patient scan have shown that the proposed approach can achieve effective noise reduction in both spatial and temporal domains and outperform the spatial Kernel Method and conventional ML reconstruction Method for improving the high temporal-resolution dynamic PET imaging.
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Dynamic PET Image Reconstruction Using the Wavelet Kernel Method
2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS MIC), 2019Co-Authors: Zahra Ashouri, Guobao Wang, Chad R. Hunter, Benjamin A. Spencer, Richard M. Dansereau, Robert. A. DekempAbstract:Dynamic PET imaging is used to monitor the spatio-temporal distribution of a tracer in a tissue region. Dynamic PET can suffer from high noise; to address this problem, the Kernel Method has been developed for efficient dynamic PET image reconstruction. Previous Kernel approaches used a Gaussian Kernel to exploit nonlocal spatial correlations from image priors. The Gaussian Kernel, has an undesired effect of smoothing high frequencies. In this work, we propose using a wavelet Kernel with good energy compaction to further enhance Kernel-based dynamic PET image reconstruction. The oscillation in the wavelet Kernel can result in better representation of details in the final reconstructed images. We evaluated the wavelet Kernel approach using patient data acquired from dynamic C-11 hydroxyephedrine (HED) PET imaging. Reconstruction results demonstrate that this wavelet Kernel approach achieves better image quality than standard reconstruction and the Gaussian Kernel approaches.
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Statistical Image Reconstruction for Shortened Dynamic PET Using a Dual Kernel Method
2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS MIC), 2017Co-Authors: Benjamin Spencer, Guobao WangAbstract:Dynamic F-18 FDG PET imaging along with tracer kinetic modeling can provide parametric images of physiologically important parameters for characterization of tumor and other diseases. This technique often requires a 1-hour long scanning time, which is less practical in clinic; a more practical Method is to use a shortened dynamic scan time of thirty or forty minutes. However, a shortened dynamic scan acquires less data and tracer kinetic modeling becomes more sensitive to high noise in dynamic PET. To address the noise challenge, the Kernel Method has been developed for efficient dynamic PET image reconstruction. Previous Kernel approaches use a single Kernel type, which exploits either nonlocal or local spatial correlations from image priors but does not explore the full potential of the Kernel framework. In this work, we propose a new dualKernel approach to further enhance Kernel-based dynamic PET image reconstruction. The dual Kernel combines the existing non-local Kernel with a local convolutional Kernel that can be easily trained from image priors. We evaluated the new Kernel approach for shortened dynamic FDG-PET imaging using a digital brain phantom. Simulation results have demonstrated that the dual-Kernel approach can achieve better image quality than standard reconstruction approach and the single Kernel approach.
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anatomically aided pet reconstruction using the Kernel Method
Physics in Medicine and Biology, 2016Co-Authors: Will Hutchcroft, Guobao Wang, Kevin T Chen, Ciprian Catana, Jinyi QiAbstract:This paper extends the Kernel Method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing Methods that incorporate anatomical information using a penalized likelihood framework, the proposed Method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new Method also does not require any segmentation of the anatomical image to obtain edge information. We compare the Kernel Method with the Bowsher Method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the Kernel Method offers advantages over the Bowsher Method in region of interest quantification. Additionally the Kernel Method is applied to a 3D patient data set. The Kernel Method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.
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PET Image Reconstruction Using Kernel Method
IEEE Transactions on Medical Imaging, 2015Co-Authors: Guobao Wang, Jinyi QiAbstract:Image reconstruction from low-count positron emission tomography (PET) projection data is challenging because the inverse problem is ill-posed. Prior information can be used to improve image quality. Inspired by the Kernel Methods in machine learning, this paper proposes a Kernel based Method that models PET image intensity in each pixel as a function of a set of features obtained from prior information. The Kernel-based image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by the maximum likelihood (ML) or penalized likelihood image reconstruction. A Kernelized expectation-maximization algorithm is presented to obtain the ML estimate. Computer simulations show that the proposed approach can achieve better bias versus variance trade-off and higher contrast recovery for dynamic PET image reconstruction than the conventional maximum likelihood Method with and without post-reconstruction denoising. Compared with other regularization-based Methods, the Kernel Method is easier to implement and provides better image quality for low-count data. Application of the proposed Kernel Method to a 4-D dynamic PET patient dataset showed promising results.
Andrew J Reader - One of the best experts on this subject based on the ideXlab platform.
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Spatially Compact MR-Guided Kernel EM for PET Image Reconstruction
IEEE Transactions on Radiation and Plasma Medical Sciences, 2018Co-Authors: James Bland, Martin A Belzunce, Sam Ellis, Colm J Mcginnity, Alexer Hammers, Andrew J ReaderAbstract:Positron emission tomography (PET) is a highly sensitive functional and molecular imaging modality which can measure picomolar concentrations of an injected radionuclide. However, the physical sensitivity of PET is limited, and reducing the injected dose leads to low count data and noisy reconstructed images. A highly effective way of reducing noise is to reparameterize the reconstruction in terms of MR-derived spatial basis functions. Spatial basis functions derived using the Kernel Method have demonstrated excellent noise reduction properties and maintain shared PET-MR detailed structures. However, as previously shown in the literature, the MR-guided Kernel Method may lead to excessive smoothing of structures that are only present in the PET data. This paper makes two main contributions in order to address this problem: first, we exploit the potential of the MR-guided Kernel Method to form more spatially compact basis functions which are able to preserve PET-unique structures, and second, we consider reconstruction at the native MR resolution. The former contribution notably improves the recovery of structures which are unique to the PET data. These adaptations of the Kernel Method were compared to the conventional implementation of the MR-guided Kernel Method and also to maximum likelihood expectation maximization, in terms of ability to recover PET unique structures for both simulated and real data. The spatially compact Kernel Method showed clear visual and quantitative improvements in the reconstruction of the PET unique structures, relative to the conventional Kernel Method for all sizes of PET unique structures investigated, whilst maintaining to a large extent the impressive noise mitigating and detail preserving properties of the conventional MR-guided Kernel Method. We therefore conclude that a spatially compact parameterization of the MR-guided Kernel Method, should be the preferred implementation strategy in order to obviate unnecessary losses in PET-unique details.
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MR-Resolution Kernel Method for PET Reconstruction
2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS MIC), 2017Co-Authors: James Bland, Abolfazl Mehranian, Martin A Belzunce, Sam Ellis, Colm J Mcginnity, Alexer Hammers, Andrew J ReaderAbstract:Conventional PET reconstruction produces noisy images. Recently proposed techniques such as the MR-guided Kernel Method have been employed to reduce the impact of noise, whilst retaining important image details. However, this can lead to over smoothing of PET unique features. To address this issue, this work extends the MR-guided Kernel Method to use MR resolution basis functions, which are extracted from an MR image at its native resolution. Furthermore, this MR-resolution Kernel Method is modified to produce spatially constrained basis functions in order to limit the smoothing of PET-unique features whilst still reducing the impact of noise. The MR-resolution Kernel reconstruction is compared to MLEM and conventional PET resolution Kernel Methods for tumour contrast recovery. These Methods are applied to real patient FDG data augmented with simulated tumours. The proposed Kernel Method shows an improved contrast to noise ratio compared to the conventional Kernel Method for all tumour sizes. However, MLEM attained a higher contrast to noise ratio for the small tumour. In summary, the MR resolution spatially constrained Kernel Method maintains the noise reduction properties of the conventional Kernel Method implementation, whilst better retaining the features unique to the PET data.
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mr guided dynamic pet reconstruction with the Kernel Method and spectral temporal basis functions
Physics in Medicine and Biology, 2016Co-Authors: Philip Novosad, Andrew J ReaderAbstract:Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the Kernel Method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [18F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or Kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with Kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral/Kernel model can also be used for effective post-reconstruction denoising, through the use of an EM-like image-space algorithm. Finally, we applied the proposed algorithm to reconstruction of real high-resolution dynamic [11C]SCH23390 data, showing promising results.
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MR-guided dynamic PET image reconstruction with the Kernel Method and spectral basis functions
2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS MIC), 2015Co-Authors: Philip Novosad, Andrew J ReaderAbstract:Regularization of iterative reconstruction for fully dynamic PET has often been achieved implicitly by estimating coefficients relating to temporal basis functions, such as data-derived temporal basis functions, wavelet temporal basis functions, or compartmental model based temporal basis functions (direct kinetic parameter estimation). In this work, we propose and evaluate a Method for anatomy-guided dynamic PET reconstruction using a joint parameterization of the PET image in terms of spatial basis functions from the Kernel Method applied to a co-registered MR anatomical image, and temporal basis functions using the spectral analysis Method. Since the model of the dynamic image is linear, the EM algorithm can be used to find an estimate for the coefficients. We demonstrate that the proposed Method combining both basis functions outperforms reconstruction using either spectral temporal basis functions alone or Kernel spatial basis functions alone, offering substantially reduced pixel-level RMSE in post-reconstruction parametric maps. Importantly, some benefits are retained even in the case where structures are present in the emission image but absent in the anatomical image.
Jinyi Qi - One of the best experts on this subject based on the ideXlab platform.
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anatomically aided pet reconstruction using the Kernel Method
Physics in Medicine and Biology, 2016Co-Authors: Will Hutchcroft, Guobao Wang, Kevin T Chen, Ciprian Catana, Jinyi QiAbstract:This paper extends the Kernel Method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing Methods that incorporate anatomical information using a penalized likelihood framework, the proposed Method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new Method also does not require any segmentation of the anatomical image to obtain edge information. We compare the Kernel Method with the Bowsher Method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the Kernel Method offers advantages over the Bowsher Method in region of interest quantification. Additionally the Kernel Method is applied to a 3D patient data set. The Kernel Method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.
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PET Image Reconstruction Using Kernel Method
IEEE Transactions on Medical Imaging, 2015Co-Authors: Guobao Wang, Jinyi QiAbstract:Image reconstruction from low-count positron emission tomography (PET) projection data is challenging because the inverse problem is ill-posed. Prior information can be used to improve image quality. Inspired by the Kernel Methods in machine learning, this paper proposes a Kernel based Method that models PET image intensity in each pixel as a function of a set of features obtained from prior information. The Kernel-based image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by the maximum likelihood (ML) or penalized likelihood image reconstruction. A Kernelized expectation-maximization algorithm is presented to obtain the ML estimate. Computer simulations show that the proposed approach can achieve better bias versus variance trade-off and higher contrast recovery for dynamic PET image reconstruction than the conventional maximum likelihood Method with and without post-reconstruction denoising. Compared with other regularization-based Methods, the Kernel Method is easier to implement and provides better image quality for low-count data. Application of the proposed Kernel Method to a 4-D dynamic PET patient dataset showed promising results.
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PET image reconstruction using Kernel Method
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013Co-Authors: Guobao Wang, Jinyi QiAbstract:Image reconstruction from low-count PET projection data is challenging because the inverse problem is ill-posed. Inspired by the Kernel Methods for machine learning, this paper proposes a Kernel based Method that models PET image intensity in each pixel as a function of a set of features obtained from prior information. The Kernel-based image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by maximum likelihood or penalized likelihood image reconstruction. Computer simulation shows that the proposed approach can achieve a higher signal-to-noise ratio for dynamic PET image reconstruction than the conventional maximum likelihood Method with and without post-reconstruction denoising.
Marie Wiberg - One of the best experts on this subject based on the ideXlab platform.
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performing the Kernel Method of test equating with the package kequate
Journal of Statistical Software, 2013Co-Authors: Bjorn Andersson, Kenny Branberg, Marie WibergAbstract:In standardized testing it is important to equate tests in order to ensure that the test takers, regardless of the test version given, obtain a fair test. Recently, the Kernel Method of test equating, which is a conjoint framework of test equating, has gained popularity. The Kernel Method of test equating includes five steps: (1) pre-smoothing, (2) estimation of the score probabilities, (3) continuization, (4) equating, and (5) computing the standard error of equating and the standard error of equating difference. Here, an implementation has been made for six different equating designs: equivalent groups, single group, counter balanced, non-equivalent groups with anchor test using either chain equating or post- stratification equating, and non-equivalent groups using covariates. An R package for the Kernel Method of test equating called kequate is presented. Included in the package are also diagnostic tools aiding in the search for a proper log-linear model in the pre-smoothing step for use in conjunction with the R function glm.
Bjorn Andersson - One of the best experts on this subject based on the ideXlab platform.
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performing the Kernel Method of test equating with the package kequate
Journal of Statistical Software, 2013Co-Authors: Bjorn Andersson, Kenny Branberg, Marie WibergAbstract:In standardized testing it is important to equate tests in order to ensure that the test takers, regardless of the test version given, obtain a fair test. Recently, the Kernel Method of test equating, which is a conjoint framework of test equating, has gained popularity. The Kernel Method of test equating includes five steps: (1) pre-smoothing, (2) estimation of the score probabilities, (3) continuization, (4) equating, and (5) computing the standard error of equating and the standard error of equating difference. Here, an implementation has been made for six different equating designs: equivalent groups, single group, counter balanced, non-equivalent groups with anchor test using either chain equating or post- stratification equating, and non-equivalent groups using covariates. An R package for the Kernel Method of test equating called kequate is presented. Included in the package are also diagnostic tools aiding in the search for a proper log-linear model in the pre-smoothing step for use in conjunction with the R function glm.
