The Experts below are selected from a list of 85893 Experts worldwide ranked by ideXlab platform
Fred A Hamprecht - One of the best experts on this subject based on the ideXlab platform.
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estimating Kinetic Parameter maps from dynamic contrast enhanced mri using spatial prior knowledge
IEEE Transactions on Medical Imaging, 2009Co-Authors: B M Kelm, Bjoern H Menze, Christian M Zechmann, Fred A HamprechtAbstract:Dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging can be used to study microvascular structure in vivo by monitoring the abundance of an injected diffusible contrast agent over time. The resulting spatially resolved intensity-time curves are usually interpreted in terms of Kinetic Parameters obtained by fitting a pharmacoKinetic model to the observed data. Least squares estimates of the highly nonlinear model Parameters, however, can exhibit high variance and can be severely biased. As a remedy, we bring to bear spatial prior knowledge by means of a generalized Gaussian Markov random field (GGMRF). By using information from neighboring voxels and computing the maximum a posteriori solution for entire Parameter maps at once, both bias and variance of the Parameter estimates can be reduced thus leading to smaller root mean square error (RMSE). Since the number of variables gets very big for common image resolutions, sparse solvers have to be employed. To this end, we propose a generalized iterated conditional modes (ICM) algorithm operating on blocks instead of sites which is shown to converge considerably faster than the conventional ICM algorithm. Results on simulated DCE-MR images show a clear reduction of RMSE and variance as well as, in some cases, reduced estimation bias. The mean residual bias (MRB) is reduced on the simulated data as well as for all 37 patients of a prostate DCE-MRI dataset. Using the proposed algorithm, average computation times only increase by a factor of 1.18 (871 ms per voxel) for a Gaussian prior and 1.51 (1.12 s per voxel) for an edge-preserving prior compared to the single voxel approach (740 ms per voxel).
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estimating Kinetic Parameter maps from dynamic contrast enhanced mri using spatial prior knowledge
IEEE Transactions on Medical Imaging, 2009Co-Authors: B M Kelm, Bjoern H Menze, Christian M Zechmann, Oliver Nix, Fred A HamprechtAbstract:Dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging can be used to study microvascular structure in vivo by monitoring the abundance of an injected diffusible contrast agent over time. The resulting spatially resolved intensity-time curves are usually interpreted in terms of Kinetic Parameters obtained by fitting a pharmacoKinetic model to the observed data. Least squares estimates of the highly nonlinear model Parameters, however, can exhibit high variance and can be severely biased. As a remedy, we bring to bear spatial prior knowledge by means of a generalized Gaussian Markov random field (GGMRF). By using information from neighboring voxels and computing the maximum a posteriori solution for entire Parameter maps at once, both bias and variance of the Parameter estimates can be reduced thus leading to smaller root mean square error (RMSE). Since the number of variables gets very big for common image resolutions, sparse solvers have to be employed. To this end, we propose a generalized iterated conditional modes (ICM) algorithm operating on blocks instead of sites which is shown to converge considerably faster than the conventional ICM algorithm. Results on simulated DCE-MR images show a clear reduction of RMSE and variance as well as, in some cases, reduced estimation bias. The mean residual bias (MRB) is reduced on the simulated data as well as for all 37 patients of a prostate DCE-MRI dataset. Using the proposed algorithm, average computation times only increase by a factor of 1.18 (871 ms per voxel) for a Gaussian prior and 1.51 (1.12 s per voxel) for an edge-preserving prior compared to the single voxel approach (740 ms per voxel).
Krishna S Nayak - One of the best experts on this subject based on the ideXlab platform.
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impact of k t sampling on dce mri tracer Kinetic Parameter estimation in digital reference objects
Magnetic Resonance in Medicine, 2020Co-Authors: Yannick Bliesener, Sajan Goud Lingala, Justin P Haldar, Krishna S NayakAbstract:Purpose To evaluate the impact of (k,t) data sampling on the variance of tracer-Kinetic Parameter (TK) estimation in high-resolution whole-brain dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) using digital reference objects. We study this in the context of TK model constraints, and in the absence of other constraints. Methods Three anatomically and physiologically realistic brain-tumor digital reference objects were generated. Data sampling strategies included uniform and variable density; zone-based, lattice, pseudo-random, and pseudo-radial; with 50-time frames and 4-fold to 25-fold undersampling. In all cases, we assume a fully sampled first time frame, and prior knowledge of the arterial input function. TK Parameters were estimated by indirect estimation (i.e., image-time-series reconstruction followed by model fitting), and direct estimation from the under-sampled data. We evaluated methods based on the Cramer-Rao bound and Monte-Carlo simulations, over the range of signal-to-noise ratio (SNR) seen in clinical brain DCE-MRI. Results Lattice-based sampling provided the lowest SDs, followed by pseudo-random, pseudo-radial, and zone-based. This ranking was consistent for the Patlak and extended Tofts model. Pseudo-random sampling resulted in 19% higher averaged SD compared to lattice-based sampling. Zone-based sampling resulted in substantially higher SD at undersampling factors above 10. CRB analysis showed only a small difference between uniform and variable density for both lattice-based and pseudo-random sampling up to undersampling factors of 25. Conclusion Lattice sampling provided the lowest SDs, although the differences between sampling schemes were not substantial at low undersampling factors. The differences between lattice-based and pseudo-random sampling strategies with both uniform and variable density were within the range of error induced by other sources, at up to 25-fold undersampling.
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joint arterial input function and tracer Kinetic Parameter estimation from undersampled dynamic contrast enhanced mri using a model consistency constraint
Magnetic Resonance in Medicine, 2018Co-Authors: Yi Guo, Yannick Bliesener, Sajan Goud Lingala, Marc R Lebel, Yinghua Zhu, Krishna S NayakAbstract:Purpose To develop and evaluate a model-based reconstruction framework for joint arterial input function (AIF) and Kinetic Parameter estimation from undersampled brain tumor dynamic contrast-enhanced MRI (DCE-MRI) data. Methods The proposed method poses the tracer-Kinetic (TK) model as a model consistency constraint, enabling the flexible inclusion of different TK models and TK solvers, and the joint estimation of the AIF. The proposed method is evaluated using an anatomic realistic digital reference object (DRO), and nine retrospectively down-sampled brain tumor DCE-MRI datasets. We also demonstrate application to 30-fold prospectively undersampled brain tumor DCE-MRI. Results In DRO studies with up to 60-fold undersampling, the proposed method provided TK maps with low error that were comparable to fully sampled data and were demonstrated to be compatible with a third-party TK solver. In retrospective undersampling studies, this method provided patient-specific AIF with normalized root mean-squared-error (normalized by the 90th percentile value) less than 8% at up to 100-fold undersampling. In the 30-fold undersampled prospective study, the proposed method provided high-resolution whole-brain TK maps and patient-specific AIF. Conclusion The proposed model-based DCE-MRI reconstruction enables the use of different TK solvers with a model consistency constraint and enables joint estimation of patient-specific AIF. TK maps and patient-specific AIF with high fidelity can be reconstructed at up to 100-fold undersampling in k,t-space. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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direct estimation of tracer Kinetic Parameter maps from highly undersampled brain dynamic contrast enhanced mri
Magnetic Resonance in Medicine, 2017Co-Authors: Yi Guo, Sajan Goud Lingala, Marc R Lebel, Yinghua Zhu, Krishna S NayakAbstract:Purpose The purpose of this work was to develop and evaluate a T1-weighted dynamic contrast enhanced (DCE) MRI methodology where tracer-Kinetic (TK) Parameter maps are directly estimated from undersampled (k,t)-space data. Theory and Methods The proposed reconstruction involves solving a nonlinear least squares optimization problem that includes explicit use of a full forward model to convert Parameter maps to (k,t)-space, utilizing the Patlak TK model. The proposed scheme is compared against an indirect method that creates intermediate images by parallel imaging and compressed sensing before to TK modeling. Thirteen fully sampled brain tumor DCE-MRI scans with 5-second temporal resolution are retrospectively undersampled at rates R = 20, 40, 60, 80, and 100 for each dynamic frame. TK maps are quantitatively compared based on root mean-squared-error (rMSE) and Bland-Altman analysis. The approach is also applied to four prospectively R = 30 undersampled whole-brain DCE-MRI data sets. Results In the retrospective study, the proposed method performed statistically better than indirect method at R ≥ 80 for all 13 cases. This approach provided restoration of TK Parameter values with less errors in tumor regions of interest, an improvement compared to a state-of-the-art indirect method. Applied prospectively, the proposed method provided whole-brain, high-resolution TK maps with good image quality. Conclusion Model-based direct estimation of TK maps from k,t-space DCE-MRI data is feasible and is compatible up to 100-fold undersampling. Magn Reson Med 78:1566–1578, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
B M Kelm - One of the best experts on this subject based on the ideXlab platform.
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estimating Kinetic Parameter maps from dynamic contrast enhanced mri using spatial prior knowledge
IEEE Transactions on Medical Imaging, 2009Co-Authors: B M Kelm, Bjoern H Menze, Christian M Zechmann, Fred A HamprechtAbstract:Dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging can be used to study microvascular structure in vivo by monitoring the abundance of an injected diffusible contrast agent over time. The resulting spatially resolved intensity-time curves are usually interpreted in terms of Kinetic Parameters obtained by fitting a pharmacoKinetic model to the observed data. Least squares estimates of the highly nonlinear model Parameters, however, can exhibit high variance and can be severely biased. As a remedy, we bring to bear spatial prior knowledge by means of a generalized Gaussian Markov random field (GGMRF). By using information from neighboring voxels and computing the maximum a posteriori solution for entire Parameter maps at once, both bias and variance of the Parameter estimates can be reduced thus leading to smaller root mean square error (RMSE). Since the number of variables gets very big for common image resolutions, sparse solvers have to be employed. To this end, we propose a generalized iterated conditional modes (ICM) algorithm operating on blocks instead of sites which is shown to converge considerably faster than the conventional ICM algorithm. Results on simulated DCE-MR images show a clear reduction of RMSE and variance as well as, in some cases, reduced estimation bias. The mean residual bias (MRB) is reduced on the simulated data as well as for all 37 patients of a prostate DCE-MRI dataset. Using the proposed algorithm, average computation times only increase by a factor of 1.18 (871 ms per voxel) for a Gaussian prior and 1.51 (1.12 s per voxel) for an edge-preserving prior compared to the single voxel approach (740 ms per voxel).
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estimating Kinetic Parameter maps from dynamic contrast enhanced mri using spatial prior knowledge
IEEE Transactions on Medical Imaging, 2009Co-Authors: B M Kelm, Bjoern H Menze, Christian M Zechmann, Oliver Nix, Fred A HamprechtAbstract:Dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging can be used to study microvascular structure in vivo by monitoring the abundance of an injected diffusible contrast agent over time. The resulting spatially resolved intensity-time curves are usually interpreted in terms of Kinetic Parameters obtained by fitting a pharmacoKinetic model to the observed data. Least squares estimates of the highly nonlinear model Parameters, however, can exhibit high variance and can be severely biased. As a remedy, we bring to bear spatial prior knowledge by means of a generalized Gaussian Markov random field (GGMRF). By using information from neighboring voxels and computing the maximum a posteriori solution for entire Parameter maps at once, both bias and variance of the Parameter estimates can be reduced thus leading to smaller root mean square error (RMSE). Since the number of variables gets very big for common image resolutions, sparse solvers have to be employed. To this end, we propose a generalized iterated conditional modes (ICM) algorithm operating on blocks instead of sites which is shown to converge considerably faster than the conventional ICM algorithm. Results on simulated DCE-MR images show a clear reduction of RMSE and variance as well as, in some cases, reduced estimation bias. The mean residual bias (MRB) is reduced on the simulated data as well as for all 37 patients of a prostate DCE-MRI dataset. Using the proposed algorithm, average computation times only increase by a factor of 1.18 (871 ms per voxel) for a Gaussian prior and 1.51 (1.12 s per voxel) for an edge-preserving prior compared to the single voxel approach (740 ms per voxel).
Christina Schenk - One of the best experts on this subject based on the ideXlab platform.
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Kinetic Parameter estimation from spectroscopic data for a multi stage solid liquid pharmaceutical process
Organic Process Research & Development, 2020Co-Authors: Lorenz T Biegler, Christina Schenk, Lu Han, Jason MustakisAbstract:Laboratory and process measurements from spectroscopic instruments are ubiquitous in pharma processes, and directly using the data can pose a number of challenges for Kinetic model building. Moreov...
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introducing kipet a novel open source software package for Kinetic Parameter estimation from experimental datasets including spectra
Computers & Chemical Engineering, 2020Co-Authors: Christina Schenk, Lorenz T Biegler, Michael Short, David Thierry, Jose S Rodriguez, Salvador Garciamunoz, W ChenAbstract:Abstract This paper presents KIPET (Kinetic Parameter Estimation Toolkit) an open-source toolbox for the determination of Kinetic Parameters from a variety of experimental datasets including spectra and concentrations. KIPET seeks to overcome limitations of standard Parameter estimation packages by applying a unified optimization framework based on maximum likelihood principles and large-scale nonlinear programming strategies for solving estimation problems that involve systems of nonlinear differential algebraic equations (DAEs). The software is based on recent advances proposed by Chen et al. (2016) and puts their original framework into an accessible framework for practitioners and academics. The software package includes tools for data preprocessing, estimability analysis, and determination of Parameter confidence levels for a variety of problem types. In addition KIPET introduces informative wavelength selection to improve the lack of fit. All these features have been implemented in Python with the algebraic modeling package Pyomo. KIPET exploits the flexibility of Pyomo to formulate and discretize the dynamic optimization problems that arise in the Parameter estimation algorithms. The solution of the optimization problems is obtained with the nonlinear solver IPOPT and confidence intervals are obtained through the use of either sIPOPT or a newly developed tool, k_aug. The capabilities as well as ease of use of KIPET are demonstrated with a number of examples.
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kipet an open source Kinetic Parameter estimation toolkit
2019Co-Authors: Michael Short, Lorenz T Biegler, Christina Schenk, David Thierry, Jose S Rodriguez, Salvador GarciamunozAbstract:Abstract This paper presents a new software package, KIPET, which is designed to estimate Kinetic Parameters from dynamic chemical reaction systems. The software toolkit is based on a unified framework that makes use of maximum likelihood principles, collocation-based discretization methods, and large-scale nonlinear optimization. KIPET contains a wide array of tools for Kinetic Parameter estimation and model evaluation in an easy-to-use open-source Python-based framework. The package can currently be used for data pre-processing, simulation of reactive systems described with differential algebraic equations, estimability analysis, estimation of system variances and measurement errors separately, estimation of Kinetic Parameters from spectroscopic data or concentration data, and the estimation of Parameter confidence intervals using NLP sensitivities. Since large-scale NLP problems require robust initialization strategies, a variety of tools for initialization are also included. KIPET utilizes Pyomo, a Python-based open-source optimization modeling language, in the background to formulate and solve all optimization problems and leverages other open-source Python packages to provide visualization of results. KIPET is well-documented and available for free download from the code-hosting site Github.
Alastair S Wood - One of the best experts on this subject based on the ideXlab platform.
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Kinetic Parameter estimation and simulation of trickle bed reactor for hydrodesulfurization of crude oil
Chemical Engineering Science, 2011Co-Authors: Aysar T Jarullah, Iqbal M Mujtaba, Alastair S WoodAbstract:Abstract Hydrodesulfurization (HDS) of crude oil has not been reported widely in the literature and it is one of the most challenging tasks in the petroleum refining industry. In order to obtain useful models for HDS process that can be confidently applied to reactor design, operation and control, the accurate estimation of Kinetic Parameters of the relevant reaction scheme are required. In this work, an optimization technique is used in order to obtain the best values of Kinetic Parameters in trickle-bed reactor (TBR) process used for hydrodesulfurization (HDS) of crude oil based on pilot plant experiment. The optimization technique is based on minimization of the sum of the square errors (SSE) between the experimental and predicted concentrations of sulfur compound in the products using two approaches (linear (LN) and non-linear (NLN) regressions). A set of experiments were carried out in a continuous flow isothermal trickle-bed reactor using crude oil as a feedstock and the commercial cobalt–molybdenum on alumina (Co–Mo/γ-Al 2 O 3 ) as a catalyst. The reactor temperature was varied from 335 to 400 °C, the hydrogen pressure from 4 to 10 MPa and the liquid hourly space velocity (LHSV) from 0.5 to 1.5 h −1 , keeping constant hydrogen to oil ratio (H 2 /oil) at 250 L/L. A steady-state heterogeneous model is developed based on two-film theory, which includes mass transfer phenomena in addition to many correlations for estimating physiochemical properties of the compounds. The hydrodesulfurization reaction is described by Langmuir–Hinshelwood Kinetics. gPROMS software is employed for modelling, Parameter estimation and simulation of hydrodesulfurization of crude oil in this work. The model simulations results were found to agree well with the experiments carried out in a wide range of the studied operating conditions. Following the Parameter estimation, the model is used to predict the concentration profiles of hydrogen, hydrogen sulfide and sulfur along the catalyst bed length in gas, liquid and solid phase, which provides further insight of the process.
