The Experts below are selected from a list of 57 Experts worldwide ranked by ideXlab platform

Jeffrey A. Fessler - One of the best experts on this subject based on the ideXlab platform.

  • ISBI - A new statistical image reconstruction algorithm for polyenergetic X-ray CT
    2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009
    Co-Authors: Monica Abella, Jeffrey A. Fessler
    Abstract:

    This paper presents a new statistical reconstruction algorithm for X-ray CT. The algorithm is based on Poisson statistics and a physical model that accounts for the Measurement Nonlinearities caused by energy-dependent attenuation. We model each voxel's attenuation as a mixture of bone and soft tissue by defining density-dependent tissue fractions, maintaining one unknown per voxel avoiding the need of a pre-segmentation. Rather than requiring the entire X-ray spectrum, the method approximates the 2D beam hardening function corresponding to bone and soft tissue with the 1D function corresponding to water and one or two empirical tuning parameters. Results on simulated human data (NCAT phantom) showed a beam hardening reduction similar to conventional post-processing techniques, but with an improved signal to noise ratio.

  • Statistical image reconstruction for polyenergetic X-ray computed tomography
    IEEE transactions on medical imaging, 2002
    Co-Authors: Idris A. Elbakri, Jeffrey A. Fessler
    Abstract:

    This paper describes a statistical image reconstruction method for X-ray computed tomography (CT) that is based on a physical model that accounts for the polyenergetic X-ray source spectrum and the Measurement Nonlinearities caused by energy-dependent attenuation. We assume that the object consists of a given number of nonoverlapping materials, such as soft tissue and bone. The attenuation coefficient of each voxel is the product of its unknown density and a known energy-dependent mass attenuation coefficient. We formulate a penalized-likelihood function for this polyenergetic model and develop an ordered-subsets iterative algorithm for estimating the unknown densities in each voxel. The algorithm monotonically decreases the cost function at each iteration when one subset is used. Applying this method to simulated X-ray CT Measurements of objects containing both bone and soft tissue yields images with significantly reduced beam hardening artifacts.

  • ISBI - Segmentation-free statistical image reconstruction for polyenergetic X-ray computed tomography
    Proceedings IEEE International Symposium on Biomedical Imaging, 1
    Co-Authors: Idris A. Elbakri, Jeffrey A. Fessler
    Abstract:

    This paper describes a statistical iterative reconstruction method for X-ray CT based on a physical model that accounts for the polyenergetic X-ray source spectrum and the Measurement Nonlinearities caused by energy-dependent attenuation. The algorithm accommodates mixtures of tissues with known mass attenuation coefficients but unknown densities. We formulate a penalized-likelihood approach for this polyenergetic model based on Poisson statistics.

Idris A. Elbakri - One of the best experts on this subject based on the ideXlab platform.

  • Statistical image reconstruction for polyenergetic X-ray computed tomography
    IEEE transactions on medical imaging, 2002
    Co-Authors: Idris A. Elbakri, Jeffrey A. Fessler
    Abstract:

    This paper describes a statistical image reconstruction method for X-ray computed tomography (CT) that is based on a physical model that accounts for the polyenergetic X-ray source spectrum and the Measurement Nonlinearities caused by energy-dependent attenuation. We assume that the object consists of a given number of nonoverlapping materials, such as soft tissue and bone. The attenuation coefficient of each voxel is the product of its unknown density and a known energy-dependent mass attenuation coefficient. We formulate a penalized-likelihood function for this polyenergetic model and develop an ordered-subsets iterative algorithm for estimating the unknown densities in each voxel. The algorithm monotonically decreases the cost function at each iteration when one subset is used. Applying this method to simulated X-ray CT Measurements of objects containing both bone and soft tissue yields images with significantly reduced beam hardening artifacts.

  • ISBI - Segmentation-free statistical image reconstruction for polyenergetic X-ray computed tomography
    Proceedings IEEE International Symposium on Biomedical Imaging, 1
    Co-Authors: Idris A. Elbakri, Jeffrey A. Fessler
    Abstract:

    This paper describes a statistical iterative reconstruction method for X-ray CT based on a physical model that accounts for the polyenergetic X-ray source spectrum and the Measurement Nonlinearities caused by energy-dependent attenuation. The algorithm accommodates mixtures of tissues with known mass attenuation coefficients but unknown densities. We formulate a penalized-likelihood approach for this polyenergetic model based on Poisson statistics.

Jang Gyu Lee - One of the best experts on this subject based on the ideXlab platform.

  • Improved Kalman filter design for three-dimensional radar tracking
    IEEE Transactions on Aerospace and Electronic Systems, 2001
    Co-Authors: Seong-taek Park, Jang Gyu Lee
    Abstract:

    The problem of three-dimensional (3D) radar tracking is considered. The usual tracking filter design relying on first-order (or linear) approximations leads to poor convergence and erratic filter behavior in highly nonlinear situations. Simple filter algorithms that can overcome these ill effects are developed for two different types of 3D radar Measurement. For each type of radar Measurement, an accurate expression for the Measurement covariance is obtained by evaluating inherent Nonlinearities of radar Measurements via coordinate transformation. Then algebraic manipulations and reasonable approximations are employed to yield a simple filter formulation based on the expression. The resulting filter equations are similar to the extended Kalman filter (EKF) and provide some useful insights into the behavior of linearized Kalman filters designed with radar Measurements. Finally, simulation results show that the proposed approach is very effective in accounting for the Measurement Nonlinearities.

  • Design of a tracking filter for improved state estimation with radar Measurements
    Proceedings of 35th IEEE Conference on Decision and Control, 1
    Co-Authors: Seong-taek Park, Jang Gyu Lee
    Abstract:

    A new approach to improved filter design is presented for the radar tracking problem. An idealized version of the extended Kalman filter, which is unrealizable in practice, is constructed using a universal linearization concept, and then it is utilized for the development of a practical tracking filter. The resulting filter tunes the Measurement error variance in an adaptive manner to account for the Measurement Nonlinearities effectively.

Stefano Battilotti - One of the best experts on this subject based on the ideXlab platform.

  • Control of linear systems with Measurement Nonlinearities
    IEEE Transactions on Automatic Control, 2005
    Co-Authors: Stefano Battilotti
    Abstract:

    In this note, we give a general result on the control of linear systems with Measurement Nonlinearities and multiple inputs and multiple outputs. We prove that if the Measurement Nonlinearities satisfy a sector condition and the system has vector relative degree and its invariant zeroes have nonpositive real part then there exists a linear Measurement feedback controller which semiglobally stabilizes the system.

Elio Usai - One of the best experts on this subject based on the ideXlab platform.

  • On the Finite-Time Stabilization of Uncertain Nonlinear Systems With Relative Degree Three
    IEEE Transactions on Automatic Control, 2007
    Co-Authors: Giorgio Bartolini, Alessandro Pisano, Elio Usai
    Abstract:

    This technical note discusses a finite-time stabilization problem in a nonlinear, uncertain, environment. The main result of the present note is presented making reference to a triple integrator affected by bounded uncertainties and subject to ldquohardrdquo Measurement Nonlinearities such that the sign of the state variables is the only reliable information available for feedback. We propose a discontinuous control scheme guaranteeing the practical finite-time annihilation of the three state variables. Constructive proof and computer simulations, as well as guidelines for practical implementations, are provided throughout this technical note.