Biomedical subjects

Hyun Chan Pyo

Publications and source records attributed to Hyun Chan Pyo.

5 recordsLinked to original sources

PubMed2006

Optimization of current injection pulse width in MREIT.

In magnetic resonance electrical impedance tomography (MREIT), we inject electrical current into a volume conductor to induce a distribution of magnetic flux density. By measuring the internal magnetic flux density using an MR scanner, we reconstruct images of cross-sectional conductivity and current density distributions. One of the most important technical problems in MREIT is to reduce the noise level in the measured magnetic flux density data since it limits the quality of reconstructed images. The noise level is inversely proportional to the current injection pulse width and signal-to-noise ratio (SNR) of MR magnitude images. Knowing that we cannot simultaneously increase both factors for a chosen echo time, we show that there is an optimal current injection pulse width minimizing the noise level. Experimental results demonstrate that the optimal current injection pulse width and appropriately chosen data acquisition time considerably reduce the noise level. We suggest future works to reduce undesirable side effects due to an increased data acquisition time.

Electric Conductivity↗

PubMed2006

Phase artefact reduction in magnetic resonance electrical impedance tomography (MREIT).

Cross-sectional conductivity imaging in magnetic resonance electrical impedance tomography (MREIT) requires the measurement of internal magnetic flux density using an MRI scanner. Current injection MRI techniques have been used to induce magnetic flux density distributions that appear in phase parts of the obtained MR signals. Since any phase error, as well as noise, deteriorates the quality of reconstructed conductivity images, we must minimize them during the data acquisition process. In this paper, we describe a new method to correct unavoidable phase errors to reduce artefacts in reconstructed conductivity images. From numerical simulations and phantom experiments, we found that the zeroth- and first-order phase errors can be effectively minimized to produce better conductivity images. The promising results suggest that this technique should be employed together with improved MREIT pulse sequences in future studies of high-resolution conductivity imaging.

Algorithms↗

PubMed2005

Noise analysis in magnetic resonance electrical impedance tomography at 3 and 11 T field strengths.

In magnetic resonance electrical impedance tomography (MREIT), we measure the induced magnetic flux density inside an object subject to an externally injected current. This magnetic flux density is contaminated with noise, which ultimately limits the quality of reconstructed conductivity and current density images. By analysing and experimentally verifying the amount of noise in images gathered from two MREIT systems, we found that a carefully designed MREIT study will be able to reduce noise levels below 0.25 and 0.05 nT at main magnetic field strengths of 3 and 11 T, respectively, at a voxel size of 3 x 3 x 3 mm(3). Further noise level reductions can be achieved by optimizing MREIT pulse sequences and using signal averaging. We suggest two different methods to estimate magnetic flux noise levels, and the results are compared to validate the experimental setup of an MREIT system.

Electric Impedance↗

PubMed2005

Identification of current density distribution in electrically conducting subject with anisotropic conductivity distribution.

Current density imaging (CDI) is able to visualize a three-dimensional current density distribution J inside an electrically conducting subject caused by an externally applied current. CDI may use a magnetic resonance imaging (MRI) scanner to measure the induced magnetic flux density B and compute J via the Ampere law [Formula: see text]. However, measuring all three components of B = (B(x), B(y), B(z)) has a technical difficulty due to the requirement of orthogonal rotations of the subject inside the MRI scanner. In this work, we propose a new method of reconstructing a current density image using only B(z) data so that we can avoid the subject rotation procedure. The method utilizes an auxiliary injection current to compensate the missing information of B(x) and B(y). The major advantage of the method is its applicability to a subject with an anisotropic conductivity distribution. Numerical experiments show the feasibility of the new technique.

Algorithms↗

PubMed2004

Image reconstruction of anisotropic conductivity tensor distribution in MREIT: computer simulation study.

We describe a novel method of reconstructing images of an anisotropic conductivity tensor distribution inside an electrically conducting subject in magnetic resonance electrical impedance tomography (MREIT). MREIT is a recent medical imaging technique combining electrical impedance tomography (EIT) and magnetic resonance imaging (MRI) to produce conductivity images with improved spatial resolution and accuracy. In MREIT, we inject electrical current into the subject through surface electrodes and measure the z-component Bz of the induced magnetic flux density using an MRI scanner. Here, we assume that z is the direction of the main magnetic field of the MRI scanner. Considering the fact that most biological tissues are known to have anisotropic conductivity values, the primary goal of MREIT should be the imaging of an anisotropic conductivity tensor distribution. However, up to now, all MREIT techniques have assumed an isotropic conductivity distribution in the image reconstruction problem to simplify the underlying mathematical theory. In this paper, we firstly formulate a new image reconstruction method of an anisotropic conductivity tensor distribution. We use the relationship between multiple injection currents and the corresponding induced Bz data. Simulation results show that the algorithm can successfully reconstruct images of anisotropic conductivity tensor distributions. While the results show the feasibility of the method, they also suggest a more careful design of data collection methods and data processing techniques compared with isotropic conductivity imaging.

Algorithms↗