Biomedical subjects

Richard H Bayford

Publications and source records attributed to Richard H Bayford.

4 recordsLinked to original sources

PubMed2005

Spatial resolution improvement of 3D EIT images by the shrinking sLORETA-FOCUSS algorithm.

This paper describes the use of the shrinking sLORETA-FOCUSS algorithm to improve the spatial resolution of three-dimensional (3D) EIT images. Conventional EIT yields inaccurate, low spatial resolution images, due to noise, the low sensitivity of boundary voltages to inner conductivity perturbations and a limited number of boundary voltage measurements. The focal underdetermined system solver (FOCUSS) algorithm produces a localized energy solution based on the weighted minimum-norm least-squares (MNLS) solution. It was successfully applied for the spatial resolution improvement of EIT images of simulated and tank data for a 2D homogeneous circular disc. However, due to the fact that a 3D mesh system contains many more elements, much more memory is required to store the weighting matrix. In order to extend the work to 3D, the shrinking-FOCUSS method is utilized to shrink the solution space as well as the weighting matrix in each iteration step. The solution of the standardized low resolution electromagnetic tomography algorithm (sLORETA) is adopted as the initial estimate of the shrinking-FOCUSS. The effectiveness is verified by implementing the new algorithm on tank data for a three-dimensional homogeneous sphere.

Algorithms↗

PubMed2004

The spatial resolution improvement of EIT images by GVSPM-FOCUSS algorithm.

The focal underdetermined system solver (FOCUSS) algorithm is a recursive algorithm to find the localized energy solution. It is an initialization-dependent algorithm. The generalized vector sample pattern matching (GVSPM) method has been applied to solve the inverse problem of electrical impedance tomography (EIT) and obtain smooth reconstructed images. By combining the GVSPM solution as the initial estimation of the FOCUSS algorithm, an idea termed the GVSPM-FOCUSS method is presented in this paper to improve the spatial resolution and precision of localization for EIT images. The comparisons are carried out between the EIT images reconstructed with the GVSPM-FOCUSS method and the GVSPM method alone. The effectiveness is verified by simulated and tank data for a model of a two-dimensional homogeneous circular disk.

Algorithms↗

PubMed2003

Electrical impedance tomography of human brain function using reconstruction algorithms based on the finite element method.

Electrical impedance tomography (EIT) is a recently developed technique which enables the internal conductivity of an object to be imaged using rings of external electrodes. In a recent study, EIT during cortical evoked responses showed encouraging changes in the raw impedance measurements, but reconstructed images were noisy. A simplified reconstruction algorithm was used which modelled the head as a homogeneous sphere. In the current study, the development and validation of an improved reconstruction algorithm are described in which realistic geometry and conductivity distributions have been incorporated using the finite element method. Data from computer simulations and spherical or head-shaped saline-filled tank phantoms, in which the skull was represented by a concentric shell of plaster of Paris or a real human skull, have been reconstructed into images. There were significant improvements in image quality as a result of the incorporation of accurate geometry and extracerebral layers in the reconstruction algorithm. Image quality, assessed by blinded subjective expert observers, also improved significantly when data from the previous evoked response study were reanalysed with the new algorithm. In preliminary images collected during epileptic seizures, the new algorithm generated EIT conductivity changes which were consistent with the electrographic ictal activity. Incorporation of realistic geometry and conductivity into the reconstruction algorithm significantly improves the quality of EIT images and lends encouragement to the belief that EIT may provide a low-cost, portable functional neuroimaging system in the foreseeable future.

Adult↗

PubMed2003

The application of the generalized vector sample pattern matching method for EIT image reconstruction.

This paper presents a new application of a generalized vector sample pattern matching (GVSPM) method for image reconstruction of conductivity changes in electrical impedance tomography. GVSPM is an iterative method for linear inverse problems. The key concept of the GVSPM is that the objective function is defined in terms of an angular component between the inner product of the known vector and solution of a system of equations. Comparisons are presented between images of simulated and experimental data, reconstructed using truncated singular value decomposition and GVSPM. In both cases, a normalized sensitivity matrix is constructed using the finite volume method to solve the forward problem.

Artifacts↗