Biomedical subjects

Chunjae Park

Publications and source records attributed to Chunjae Park.

7 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↗

PubMed2006

Conductivity image reconstruction from defective data in MREIT: numerical simulation and animal experiment.

Magnetic resonance electrical impedance tomography (MREIT) is designed to produce high resolution conductivity images of an electrically conducting subject by injecting current and measuring the longitudinal component, Bz, of the induced magnetic flux density B = (Bx, By, Bz). In MREIT, accurate measurements of Bz are essential in producing correct conductivity images. However, the measured Bz data may contain fundamental defects in local regions where MR magnitude image data are small. These defective Bz data result in completely wrong conductivity values there and also affect the overall accuracy of reconstructed conductivity images. Hence, these defects should be appropriately recovered in order to carry out any MREIT image reconstruction algorithm. This paper proposes a new method of recovering Bz data in defective regions based on its physical properties and neighboring information of Bz. The technique will be indispensable for conductivity imaging in MREIT from animal or human subjects including defective regions such as lungs, bones, and any gas-filled internal organs.

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↗

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↗

PubMed2004

Static conductivity imaging using variational gradient Bz algorithm in magnetic resonance electrical impedance tomography.

A new image reconstruction algorithm is proposed to visualize static conductivity images of a subject in magnetic resonance electrical impedance tomography (MREIT). Injecting electrical current into the subject through surface electrodes, we can measure the induced internal magnetic flux density B = (Bx, By, Bz) using an MRI scanner. In this paper, we assume that only the z-component Bz is measurable due to a practical limitation of the measurement technique in MREIT. Under this circumstance, a constructive MREIT imaging technique called the harmonic Bz algorithm was recently developed to produce high-resolution conductivity images. The algorithm is based on the relation between inverted delta2Bz and the conductivity requiring the computation of inverted delta2Bz. Since twice differentiations of noisy Bz data tend to amplify the noise, the performance of the harmonic Bz algorithm is deteriorated when the signal-to-noise ratio in measured Bz data is not high enough. Therefore, it is highly desirable to develop a new algorithm reducing the number of differentiations. In this work, we propose the variational gradient Bz algorithm where Bz is differentiated only once. Numerical simulations with added random noise confirmed its ability to reconstruct static conductivity images in MREIT. We also found that it outperforms the harmonic Bz algorithm in terms of noise tolerance. From a careful analysis of the performance of the variational gradient Bz algorithm, we suggest several methods to further improve the image quality including a better choice of basis functions, regularization technique and multilevel approach. The proposed variational framework utilizing only Bz will lead to different versions of improved algorithms.

Algorithms↗

PubMed2004

Electrical conductivity imaging using gradient B, decomposition algorithm in magnetic resonance electrical impedance tomography (MREIT).

In magnetic resonance electrical impedance tomography (MREIT), we try to visualize cross-sectional conductivity (or resistivity) images of a subject. We inject electrical currents into the subject through surface electrodes and measure the z component Bz of the induced internal magnetic flux density using an MRI scanner. Here, z is the direction of the main magnetic field of the MRI scanner. We formulate the conductivity image reconstruction problem in MREIT from a careful analysis of the relationship between the injection current and the induced magnetic flux density Bz. Based on the novel mathematical formulation, we propose the gradient Bz decomposition algorithm to reconstruct conductivity images. This new algorithm needs to differentiate Bz only once in contrast to the previously developed harmonic Bz algorithm where the numerical computation of (inverted delta)2Bz is required. The new algorithm, therefore, has the important advantage of much improved noise tolerance. Numerical simulations with added random noise of realistic amounts show the feasibility of the algorithm in practical applications and also its robustness against measurement noise.

Algorithms↗