• Journal of radiological science and technology
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• v.47 no.1
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• pp.21-28
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• 2024

This study was purpose to quantitative evaluation of comparison of the image intensity uniformity and noise power spectrum (NPS) by using American college of radiology (ACR) phantom for magnetic resonance imaging (MRI). The MRI was used achiva 3.0T MRI and discovery MR 750, 3.0T, the head and neck matrix shim SENSE head coil were 32 channels receive MR coil. The MRI was used parameters of image sequence for ACR standard and general hospital. NPS value of the ACR standard T2 vertical image in GE equipment was 7.65E-06 when the frequency was 1.0 mm^-1. And the NPS value of the ACR hospital T1 region of interest (ROI) 9 over all vertical image in Philips equipment was 9E-08 when the frequency was 1.0 mm^-1 and the NPS value of the hospital T2 ROI 9 over all vertical image in Philips equipment was 1.06E-07 when the frequency was 1.0 mm^-1. NPS was used efficiently by using a general hospital vertical sequence more than the standard vertical sequence method by using the ACR phantom. Furthermore NPS was the quantitative quality assurance (QA) assessment method for noise and image intensity uniformity characteristics was applied mutatis mutandis, and the results values of the physical imaging NPS of the 3.0T MRI and ACR phantom were presented.

• Health Policy and Management
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• v.31 no.3
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• pp.261-271
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• 2021

Background: In 2018, the government increased the fee for the magnetic resonance imaging (MRI) image deciphering services of the external hospital to discourage the redundant MRI scan and to induce appropriate use of the MRI services. It is important to evaluate the effect of the policy to provide the basis for establishing other MRI-related policies. Methods: The healthcare data of the patients who had brain MRI scans were organized by episode and analyzed using the panel study in order to find out the effect of the MRI-related policy on the substitution effect and the medical expenses. Results: As a result of the increase in the fee of deciphering the MRI image, there has been an uplift in deciphering the MRI scan of the external hospital. It implies that more hospitals chose to use the MRI scan taken by other clinics or hospitals, rather than the MRI scan taken at their own facilities. Conclusion: The research results imply that a policy that facilitates the exchange of the medical image data between the hospitals is needed in order to establish an efficient management system of the healthcare resources. Such improvement is expected to reduce the social cost and contribute to the stability in the finance of national health insurance.

• Journal of radiological science and technology
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• v.45 no.1
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• pp.11-17
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• 2022

This study was purpose to quantitative assessment of the resolution characteristics by using American college of radiology(ACR) phantom for magnetic resonance imaging (MRI). The MRI equipment was used (Achiva 3.0T MRI, Philips system, Netherlands) and the head/neck matrix shim SENSE head coil were 32 channels(elements) receive MR coil. And the MRI equipment was used (Discovery MR 750, 3.0T MRI, GE medical system, America) and the head/neck matrix shim MC 3003G-32R 32-CH head coil were receive MR coil. As for the modulation transfer function(MTF) comparison result by using ACR magnetic resonance imaging phantom, the MTF value of the ACR standard T2 image in GE equipment is 0.199 when the frequency is 1.0 mm^-1 and the MTF value of the hospital T2 image in Philips equipment is 0.528. It was used efficiently by using a general sequence more than the standard sequence method using the ACR phantom. In addition it is significant that the quantitative quality assurance evaluation method for resolution characteristics was applied mutatis mutandis, and the result values of the physical image characteristics of the 3.0T MRI device were presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1167-1187
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• 2020

With the development of medical imaging technology, image registration has been widely used in the field of disease diagnosis. The registration between different modal images of brain magnetic resonance (MR) is particularly important for the diagnosis of brain diseases. However, previous registration methods don't take advantage of the prior knowledge of bilateral brain symmetry. Moreover, the difference in gray scale information of different modal images increases the difficulty of registration. In this paper, a multimodal medical image registration method based on image segmentation and symmetric self-similarity is proposed. This method uses modal independent self-similar information and modal consistency information to register images. More particularly, we propose two novel symmetric self-similarity constraint operators to constrain the segmented medical images and convert each modal medical image into a unified modal for multimodal image registration. The experimental results show that the proposed method can effectively reduce the error rate of brain MR multimodal medical image registration with rotation and translation transformations (average 0.43mm and 0.60mm) respectively, whose accuracy is better compared to state-of-the-art image registration methods.

• Journal of the Korean Society of Radiology
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• v.7 no.1
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• pp.57-62
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• 2013

Despite the many advantages of magnetic resonance imaging in orthopedic prosthetic body image distortion to the differences in the magnetic susceptibility occurs. Attached to the phantom and pork produced by the same $65{\times}15{\times}2mm$ stainless steel and titanium specimen examined the relationship between magnetic resonance imaging and phantom images, the signal intensity changes of the subcutaneous tissue, fat-suppressed quantitative assessment of the degree through the length of image distortion and pig bones. Stainless steel to titanium to 2.8 times 4.4 times in the longitudinal direction than in the direction of the height of large image distortion, signal strength is relatively low 58.5%. Normal 56.2% compared to the subcutaneous tissue, fat-suppressed, were stainless steel 16.04%, 54.53% titanium. Experimental results than the diagnostic value of magnetic resonance imaging (MRI) images of stainless steel with a titanium metal if better could see.

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.132-138
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• 2007

MR(magnetic resonance) image of moving organ such as heart shows serious distortion of MR image due to motion itself. To eliminate motion artifacts, MRI(magnetic resonance imaging) scan sequences requires a trigger pulse like ECG(electro-cardiography) R-wave. ECG-gating using cardiac cycle synchronizes the MRI sequence acquisition to the R-wave in order to eliminate image motion artifacts. In this paper, we designed ECG/PPG(photo-plethysmography) gating system which is for eliminating motion artifacts due to moving organ. This system uses nonmagnetic carbon electrodes, lead wire and shield case for minimizing RF(radio-frequency) pulse and gradient effect. Also, we developed a ECG circuit for preventing saturation by magnetic field and a finger plethysmography sensor using optic fiber. And then, gating pulse is generated by adaptive filtering based on NLMS(normalized least mean square) algorithm. To evaluate the developed system, we measured and compared MR imaging of heart and neck with and without ECG/PPG gating system. As a result, we could get a clean image to be used in clinically. In conclusion, the designed ECG/PPG gating system could be useful method when we get MR imaging of moving organ like a heart.

• Investigative Magnetic Resonance Imaging
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• v.23 no.2
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• pp.81-99
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• 2019

Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.

• Journal of Biomedical Engineering Research
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• v.15 no.4
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• pp.413-418
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• 1994

In electrical impedance tomography (EIT), we use boundary current and voltage measurements to provide the information about the cross-sectional distribution of electrical impedance or resistivity One of the major problems in EIT has been the inaccessibility of internal voltage or current data in finding the internal impedance values. We propose a new image reconstruction method using internal current density data measured by NMR. We obtained a two-dimensional current density distribution within a phantom by processing the real and imaginary MR images from a 4.7T NMR machine. We implemented a resistivity image reconstruction algorithm using the finite element method and sensitivity matrix. We presented computer simulation results of the image reconstruction algorithm and furture direction of the research.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.169-172
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• 1996

In this paper, we present the evaluation of signal to noise ratio(SNR) and images of Magnetic resonance imaging system which is underdevelopement. For the evaluation of such parameters, we used two different phantoms, one for SNR and image homogeneity, and the other is for the slice thickness measurement. Further, comparison with other leading MR systems may be needed for the better image quality assessment.

• Investigative Magnetic Resonance Imaging
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• v.26 no.1
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• pp.1-9
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• 2022

Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in image recognition tasks. Over the past decade, AI has proven its feasibility for applications in medical imaging. Various aspects of clinical practice in neuroimaging can be improved with the help of AI. For example, AI can aid in detecting brain metastases, predicting treatment response of brain tumors, generating a parametric map of dynamic contrast-enhanced MRI, and enhancing radiomics research by extracting salient features from input images. In addition, image quality can be improved via AI-based image reconstruction or motion artifact reduction. In this review, we summarize recent clinical applications of DL in various aspects of neuroimaging.