Biomedical subjects

Ohin Kwon

Publications and source records attributed to Ohin Kwon.

16 recordsLinked to original sources

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↗

PubMed2005

Electrical conductivity images of biological tissue phantoms in MREIT.

We present cross-sectional conductivity images of two biological tissue phantoms. Each of the cylindrical phantoms with both diameter and height of 140 mm contained chunks of biological tissues such as bovine tongue and liver, porcine muscle and chicken breast within a conductive agar gelatin as the background medium. We attached four recessed electrodes on the sides of the phantom with equal spacing among them. Injecting current pulses of 480 or 120 mA ms into the phantom along two different directions, we measured the z-component Bz of the induced magnetic flux density B=(Bx, By, Bz) with a magnetic resonance electrical impedance tomography (MREIT) system based on a 3.0 T MRI scanner. Using the harmonic Bz algorithm, we reconstructed cross-sectional conductivity images from the measured Bz data. Reconstructed images clearly distinguish different tissues in terms of both their shapes and conductivity values. In this paper, we experimentally demonstrate the feasibility of the MREIT technique in producing conductivity images of different biological soft tissues with a high spatial resolution and accuracy when we use a sufficient amount of the injection current.

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↗

PubMed2004

Magnetic resonance electrical impedance tomography at 3 Tesla field strength.

Magnetic resonance electrical impedance tomography (MREIT) is a recently developed imaging technique that combines MRI and electrical impedance tomography (EIT). In MREIT, cross-sectional electrical conductivity images are reconstructed from the internal magnetic field density data produced inside an electrically conducting object when an electrical current is injected into the object. In this work we present the results of electrical conductivity imaging experiments, and performance evaluations of MREIT in terms of noise characteristics and spatial resolution. The MREIT experiment was performed with a 3.0 Tesla MRI system on a phantom with an inhomogeneous conductivity distribution. We reconstructed the conductivity images in a 128 x 128 matrix format by applying the harmonic B(z) algorithm to the z-component of the internal magnetic field density data. Since the harmonic B(z) algorithm uses only a single component of the internal magnetic field data, it was not necessary to rotate the object in the MRI scan. The root mean squared (RMS) errors of the reconstructed images were between 11% and 35% when the injection current was 24 mA.

Electric Conductivity↗

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

A mathematical model for breast cancer lesion estimation: electrical impedance technique using TS2000 commercial system.

We present a mathematical model to analyze transadmittance data for the detection of breast cancer using TransScan TS2000 commercial system. The model was constructed based on the assumption that a lesion exists near the surface of a breast region. The breast region that is considered as a background is assumed to be homogeneous at least near the surface where we attach a planar array of electrodes. Based on the model, we developed a lesion estimation algorithm utilizing single- or multifrequency transadmittance data. The approximate ratio of two conductivity values for the lesion and background needs to be known to estimate the size of the lesion even though the location estimate does not require this ratio. From the results of numerical simulations with added noise, we suggest better ways of interpreting TS2000 transadmittance images for the detection of breast cancer with improved accuracy. Since this study provides a rigorous mathematical modeling of TS2000 commercial system, it will be possible to apply the technique to lesion estimation problems based on more realistic models of breast regions in future studies.

Breast Neoplasms↗

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↗

PubMed2003

Conductivity and current density image reconstruction using harmonic Bz algorithm in magnetic resonance electrical impedance tomography.

Magnetic resonance electrical impedance tomography (MREIT) is to provide cross-sectional images of the conductivity distribution sigma of a subject. While injecting current into the subject, we measure one component Bz of the induced magnetic flux density B = (Bx, By, Bz) using an MRI scanner. Based on the relation between (inverted delta)2 Bz and inverted delta sigma, the harmonic Bz algorithm reconstructs an image of sigma using the measured Bz data from multiple imaging slices. After we obtain sigma, we can reconstruct images of current density distributions for any given current injection method. Following the description of the harmonic Bz algorithm, this paper presents reconstructed conductivity and current density images from computer simulations and phantom experiments using four recessed electrodes injecting six different currents of 26 mA. For experimental results, we used a three-dimensional saline phantom with two polyacrylamide objects inside. We used our 0.3 T (tesla) experimental MRI scanner to measure the induced Bz. Using the harmonic Bz algorithm, we could reconstruct conductivity and current density images with 82 x 82 pixels. The pixel size was 0.6 x 0.6 mm2. The relative L2 errors of the reconstructed images were between 13.8 and 21.5% when the signal-to-noise ratio (SNR) of the corresponding MR magnitude images was about 30. The results suggest that in vitro and in vivo experimental studies with animal subjects are feasible. Further studies are requested to reduce the amount of injection current down to less than 1 mA for human subjects.

Algorithms↗

PubMed2003

Three-dimensional forward solver and its performance analysis for magnetic resonance electrical impedance tomography (MREIT) using recessed electrodes.

In magnetic resonance electrical impedance tomography (MREIT), we try to reconstruct a cross-sectional resistivity (or conductivity) image of a subject. When we inject a current through surface electrodes, it generates a magnetic field. Using a magnetic resonance imaging (MRI) scanner, we can obtain the induced magnetic flux density from MR phase images of the subject. We use recessed electrodes to avoid undesirable artefacts near electrodes in measuring magnetic flux densities. An MREIT image reconstruction algorithm produces cross-sectional resistivity images utilizing the measured internal magnetic flux density in addition to boundary voltage data. In order to develop such an image reconstruction algorithm, we need a three-dimensional forward solver. Given injection currents as boundary conditions, the forward solver described in this paper computes voltage and current density distributions using the finite element method (FEM). Then, it calculates the magnetic flux density within the subject using the Biot-Savart law and FEM. The performance of the forward solver is analysed and found to be enough for use in MREIT for resistivity image reconstructions and also experimental designs and validations. The forward solver may find other applications where one needs to compute voltage, current density and magnetic flux density distributions all within a volume conductor.

Algorithms↗

PubMed2003

Reconstruction of current density distributions in axially symmetric cylindrical sections using one component of magnetic flux density: computer simulation study.

In magnetic resonance current density imaging (MRCDI), we inject current into a subject through surface electrodes and measure the induced magnetic flux density B inside the subject using an MRI scanner. Once we have obtained all three components of B, we can reconstruct the internal current density distribution J = inverted triangle x B/mu0). This technique, however, requires subject rotation since the MRI scanner can measure only one component of B that is parallel to the direction of its main magnetic field. In this paper, under the assumption that the out-of-plane current density Jz is negligible in an imaging slice belonging to the xy-plane, we developed an imaging technique of current density distributions using only Bz, the z-component of B. The technique described in this paper does not require a subject rotation but the quality of reconstructed images depends on the amount of out-of-plane current density Jz. From numerical simulations, we found that the new algorithm could be applied to subjects such as human limbs using longitudinal electrodes.

Algorithms↗

PubMed2003

Static resistivity image of a cubic saline phantom in magnetic resonance electrical impedance tomography (MREIT).

In magnetic resonance electrical impedance tomography (MREIT) we inject currents through electrodes placed on the surface of a subject and try to reconstruct cross-sectional resistivity (or conductivity) images using internal magnetic flux density as well as boundary voltage measurements. In this paper we present a static resistivity image of a cubic saline phantom (50 x 50 x 50 mm3) containing a cylindrical sausage object with an average resistivity value of 123.7 ohms cm. Our current MREIT system is based on an experimental 0.3 T MRI scanner and a current injection apparatus. We captured MR phase images of the phantom while injecting currents of 28 mA through two pairs of surface electrodes. We computed current density images from magnetic flux density images that are proportional to the MR phase images. From the current density images and boundary voltage data we reconstructed a cross-sectional resistivity image within a central region of 38.5 x 38.5 mm2 at the middle of the phantom using the J-substitution algorithm. The spatial resolution of the reconstructed image was 64 x 64 and the reconstructed average resistivity of the sausage was 117.7 ohms cm. Even though the error in the reconstructed average resistivity value was small, the relative L2-error of the reconstructed image was 25.5% due to the noise in measured MR phase images. We expect improvements in the accuracy by utilizing an MRI scanner with higher SNR and increasing the size of voxels scarifying the spatial resolution.

Artifacts↗

PubMed2003

Estimation of anomaly location and size using electrical impedance tomography.

We developed a new algorithm that estimates locations and sizes of anomalies in electrically conducting medium based on electrical impedance tomography (EIT) technique. When only the boundary current and voltage measurements are available, it is not practically feasible to reconstruct accurate high-resolution cross-sectional conductivity or resistivity images of a subject. In this paper, we focus our attention on the estimation of locations and sizes of anomalies with different conductivity values compared with the background tissues. We showed the performance of the algorithm from experimental results using a 32-channel EIT system and saline phantom. With about 1.73% measurement error in boundary current-voltage data, we found that the minimal size (area) of the detectable anomaly is about 0.72% of the size (area) of the phantom. Potential applications include the monitoring of impedance related physiological events and bubble detection in two-phase flow. Since this new algorithm requires neither any forward solver nor time-consuming minimization process, it is fast enough for various real-time applications in medicine and nondestructive testing.

Algorithms↗

PubMed2003

Reconstruction of conductivity and current density images using only one component of magnetic field measurements.

Magnetic resonance current density imaging (MRCDI) is to provide current density images of a subject using a magnetic resonance imaging (MRI) scanner with a current injection apparatus. The injection current generates a magnetic field that we can measure from MR phase images. We obtain internal current density images from the measured magnetic flux densities via Ampere's law. However, we must rotate the subject to acquire all of the three components of the induced magnetic flux density. This subject rotation is impractical in clinical MRI scanners when the subject is a human body. In this paper, we propose a way to eliminate the requirement of subject rotation by careful mathematical analysis of the MRCDI problem. In our new MRCDI technique, we need to measure only one component of the induced magnetic flux density and reconstruct both cross-sectional conductivity and current density images without any subject rotation.

Algorithms↗

PubMed2002

Magnetic resonance electrical impedance tomography (MREIT): simulation study of J-substitution algorithm.

We developed a new image reconstruction algorithm for magnetic resonance electrical impedance tomography (MREIT). MREIT is a new EIT imaging technique integrated into magnetic resonance imaging (MRI) system. Based on the assumption that internal current density distribution is obtained using magnetic resonance imaging (MRI) technique, the new image reconstruction algorithm called J-substitution algorithm produces cross-sectional static images of resistivity (or conductivity) distributions. Computer simulations show that the spatial resolution of resistivity image is comparable to that of MRI. MREIT provides accurate high-resolution cross-sectional resistivity images making resistivity values of various human tissues available for many biomedical applications.

Algorithms↗

PubMed2002

J-substitution algorithm in magnetic resonance electrical impedance tomography (MREIT): phantom experiments for static resistivity images.

Recently, a new static resistivity image reconstruction algorithm is proposed utilizing internal current density data obtained by magnetic resonance current density imaging technique. This new imaging method is called magnetic resonance electrical impedance tomography (MREIT). The derivation and performance of J-substitution algorithm in MREIT have been reported as a new accurate and high-resolution static impedance imaging technique via computer simulation methods. In this paper, we present experimental procedures, denoising techniques, and image reconstructions using a 0.3-tesla (T) experimental MREIT system and saline phantoms. MREIT using J-substitution algorithm effectively utilizes the internal current density information resolving the problem inherent in a conventional EIT, that is, the low sensitivity of boundary measurements to any changes of internal tissue resistivity values. Resistivity images of saline phantoms show an accuracy of 6.8%-47.2% and spatial resolution of 64 x 64. Both of them can be significantly improved by using an MRI system with a better signal-to-noise ratio.

Algorithms↗