• Journal of Biomedical Engineering Research
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• v.30 no.6
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• pp.461-468
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• 2009

Magnetic resonance electrical impedance tomography (MREIT) enables us to perform high-resolution conductivity imaging of an electrically conducting object. Injecting low-frequency current through a pair of surface electrodes, we measure an induced magnetic flux density using an MRI scanner and this requires a sophisticated MR phase imaging method. Applying a conductivity image reconstruction algorithm to measured magnetic flux density data subject to multiple injection currents, we can produce multi-slice cross-sectional conductivity images. When there exists a local region of fat, the well-known chemical shift phenomenon produces misalignments of pixels in MR images. This may result in artifacts in magnetic flux density image and consequently in conductivity image. In this paper, we investigate chemical shift artifact correction in MREIT based on the well-known three-point Dixon technique. The major difference is in the fact that we must focus on the phase image in MREIT. Using three Dixon data sets, we explain how to calculate a magnetic flux density image without chemical shift artifact. We test the correction method through imaging experiments of a cheese phantom and postmortem canine head. Experimental results clearly show that the method effectively eliminates artifacts related with the chemical shift phenomenon in a reconstructed conductivity image.

• Investigative Magnetic Resonance Imaging
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• v.20 no.4
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• pp.215-223
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• 2016

Purpose: The management of metal-induced field inhomogeneities is one of the major concerns of distortion-free magnetic resonance images near metallic implants. The recently proposed method called "Slice Encoding for Metal Artifact Correction (SEMAC)" is an effective spin echo pulse sequence of magnetic resonance imaging (MRI) near metallic implants. However, as SEMAC uses the noisy resolved data elements, SEMAC images can have a major problem for improving the signal-to-noise ratio (SNR) without compromising the correction of metal artifacts. To address that issue, this paper presents a novel reconstruction technique for providing an improvement of the SNR in SEMAC images without sacrificing the correction of metal artifacts. Materials and Methods: Low-rank approximation in each coil image is first performed to suppress the noise in the slice direction, because the signal is highly correlated between SEMAC-encoded slices. Secondly, SEMAC images are reconstructed by the best linear unbiased estimator (BLUE), also known as Gauss-Markov or weighted least squares. Noise levels and correlation in the receiver channels are considered for the sake of SNR optimization. To this end, since distorted excitation profiles are sparse, $l_1$ minimization performs well in recovering the sparse distorted excitation profiles and the sparse modeling of our approach offers excellent correction of metal-induced distortions. Results: Three images reconstructed using SEMAC, SEMAC with the conventional two-step noise reduction, and the proposed image denoising for metal MRI exploiting sparsity and low rank approximation algorithm were compared. The proposed algorithm outperformed two methods and produced 119% SNR better than SEMAC and 89% SNR better than SEMAC with the conventional two-step noise reduction. Conclusion: We successfully demonstrated that the proposed, novel algorithm for SEMAC, if compared with conventional de-noising methods, substantially improves SNR and reduces artifacts.

• Investigative Magnetic Resonance Imaging
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• v.19 no.1
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• pp.47-51
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• 2015

Sarcomatous Intrahepatic cholangiocarcinoma is a rare but an aggressive variant of cholangiocarcinoma with a very poor prognosis. A 79-year-old man was admitted to our hospital because of incidentally found liver mass. Magnetic resonance imaging (MRI) revealed well-defined hypointense mass on T1WI and heterogeneous hyperintense mass on T2WI. Gd-EOB-DTPA enhanced study shows peripheral rim-like enhancement in arterial phase and progressive concentric filling of contrast in delayed phase. And mass shows significant enhancement in hepatobiliary phase. The pathologic diagnosis was intrahepatic cholangiocarcinoma with sarcomatous change.

• Investigative Magnetic Resonance Imaging
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• v.20 no.2
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• pp.136-139
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• 2016

Clear cell sarcoma is rare and difficult to diagnose. Herein, we present a case of clear cell sarcoma in the dorsum of the wrist with MRI findings, including diffusion-weighted imaging, and histopathologic correlation, which was initially diagnosed as giant cell tumor of tendon sheath.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2783-2785
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• 2003

In multi-dimensional magnetic resonance imaging, data is obtained in the spatial frequency domain. Since the signal variation in the spatial frequency domain is much larger than that in the spatial domain, analog-to-digital converts with wide conversion bits are required. In this paper, the quantization noise in magnetic resonance imaging is analyzed. The signal-to-quantization noise ratio(SQNR) in the reconstructed image is derived from the level of quantization in the data acquisition. Since the quantization noise is proportional to the signal amplitude, it becomes more dominant in high field imaging. Using the derived formula the SQNR for several MRI systems are evaluated, and it is shown that the quantization noise can be a limiting factor in high field imaging, especially in three dimensional imaging in magnetic resonance imaging.

• Communications of the Korean Mathematical Society
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• v.16 no.3
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• pp.519-541
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• 2001

Magnetic Resonance Electrical Impedance Tomography(MREIT) is a new medical imaging technique for the cross-sectional conductivity distribution of a human body using both EIT(Electrical Impedance Tomography) and MRI(Magnetic Resonance Imaging) system. MREIT system was designed to enhance EIT imaging system which has inherent low sensitivity of boundary measurements to any changes of internal tissue conductivity values. MREIT utilizes a recent CDI (Current Density Imaging) technique of measuring the internal current density by means of MRI technique. In this paper, a mathematical modeling for MREIT and image reconstruction method called the alternating J-substitution algorithm are presented. Computer simulations show that the alternating J-substitution algorithm provides accurate high-resolution conductivity images.

• Investigative Magnetic Resonance Imaging
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• v.25 no.2
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• pp.129-134
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• 2021

The prevalence of cheek lymphoma, especially a mucosa-associated lymphoid tissue lymphoma (MALT), is very rare. Non-specific symptoms and image findings of cheek lymphoma may mimic benign entities and make it difficult to diagnose. In this case report, we present a case of MALT lymphoma of the cheek mimicking benign entities on computed tomography and magnetic resonance imaging.

• Journal of radiological science and technology
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• v.44 no.4
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• pp.327-333
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• 2021

Magnetic resonance imaging(MRI) uses strong magnetic field to image the cross-section of human body and has excellent image quality with no risk of radiation exposure. Because of above-mentioned advantages, MRI has been widely used in clinical fields. However, the noise generated in MRI degrades the quality of medical images and has a negative effect on quick and accurate diagnosis. In particular, examining a object with a detailed structure such as brain, image quality degradation becomes a problem for diagnosis. Therefore, in this study, we acquired T2 weighted 3D data of multiple sclerosis disease using BrainWeb simulation program, and used quantitative evaluation factors to find appropriate slice thickness among 1, 3, 5, and 7 mm. Coefficient of variation and contrast to noise ratio were calculated to evaluate the noise level, and root mean square error and peak signal to noise ratio were used to evaluate the similarity with the reference image. As a result, the noise level decreased as the slice thickness increased, while the similarity decreased after 5 mm. In conclusion, as the slice thickness increases, the noise is reduced and the image quality is improved. However, since the edge signal is lost due to overlapped signal, it is considered that selecting appropriate slice thickness is necessary.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.525-528
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• 1999

Abstract - In this paper, a segmentation method for brain Magnetic Resonance(MR) image using region clustering technique with statistical distribution of gradient image and fuzzy rules is described. The brain MRI consists of gray matter and white matter, cerebrospinal fluid. But due to noise, overlap, vagueness, and various parameters, segmentation of MR image is a very difficult task. We use gradient information rather than intensity directly from the MR images and find appropriate thresholds for region classification using gradient approximation, rayleigh distribution function, region clustering, and merging techniques. And then, we propose the adaptive fuzzy rules in order to extract anatomical structures and diseases from brain MR image data. The experimental results shows that the proposed segmentation algorithm given better performance than traditional segmentation techniques.

• Journal of the Korean Magnetic Resonance Society
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• v.4 no.1
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• pp.64-73
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• 2000

There are several methods to achieve selective NMR image of differing chemical species with the three most popular methods of Dixon's, CHESS, and SECSI. A major problem common to all chemical shift imaging methods is the uniformity of the static magnetic field and distortions introduced when RF coils are loaded with a conducting specimen. Without magnetic field shimming, these methods cannot be used to acquire selectively image protons in fat and water which are separated by approximately 3.0ppm. Experiments with a phantom, with linewidths of 2.5 to 3.5ppm, were quantitatively evaluated for the three methods and a new chemical shift imaging method. In this study the new chemical shift imaging method (modified CHESS+SECSI technique) which included a selective saturation and refocusing pulse, was developed to determine the ratios of water and fat in different samples.