• Journal of the Korea Safety Management & Science
• /
• v.17 no.4
• /
• pp.231-240
• /
• 2015

In this study, to find out the effect of psychological anxiety of the patients during MRI examination on the claustrophobia and vital signs, As for a study tool, to measure Anxiety Sensitivity Index(ASI), Kamsung Evaluation Index of Life Environmental Noise(KEI), Diagnostic and Statistical Manual of Mental Disorders(DSM-IV) was used, and for vital signs, blood pressure and pulse rate were measured pre and post MRI examination. In conclusion, it was indicated that though the effect of the general characteristics, psychological anxiety, on noise sensitivity and claustrophobia was small, the psychological anxiety of the patients during MRI examination affected claustrophobia and vital signs.

• Investigative Magnetic Resonance Imaging
• /
• v.1 no.1
• /
• pp.51-57
• /
• 1997

MRI의 artifact는 대부분 신호의 부호화 방향에 따라서 방향성을 가지는데, 이를 요약해보면, 위상부호화 방향의 artifact에는 motion artifact, flow artifact, RF noise등이 있고, 주파수 부호화 방향의 artfact는 susceptibility artfact, chemical shift artifact, central line artifact등이 있으며, 양방향 모두 생길수 있는 것은 Aliasing artifact와 Gibb's phenomenon이고, 전체적으로 영샹의 질을 떨어뜨리는 것은 susceptibility artifact, Eddy current, cross talk등이 있다. 이런 artifact는 대부분은 MRI 자체의 물리적 특성에 다소간 기인하므로, artifact가 없는 양호한 영상을 얻기 위해서는 MRI의 설치 단계부터 관심이 필요하고, MRI의 기본원리와 다양한 artifact에 대해 이해함으로써, 제거 가능한 artifact는 제거하여 양질의 영상을 만들고 판독시의 오류를 피할 수 있도록 해야할 것이다.

• The Korean Journal of Applied Statistics
• /
• v.16 no.2
• /
• pp.305-319
• /
• 2003

This paper provides a non-Bayesian method based on the expanded EM algorithm for segmenting the magnetic resonance images degraded by bias field. For the images with the intensity as a pixel value, many segmentation methods often fail to segment it because of the bias field(with low frequency) as well as noise(with high frequency). Our contextual approach is appropriately designed by using normal mixture model incorporated with Markov random field for noise-corrective segmentation and by using the penalized likelihood to estimate bias field for efficient bias filed-correction.

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.871-872
• /
• 2006

In this paper, we designed bio-signal acquisition system in Magnetic Resonance Imager(MRI) Environment. In MRI Environment, Strong RF Pulse and Gradient Field Switching Noise exist and can cause distortion of ECG. By this, ECG can lose their important information. So we proposed a bio-signal acquisition system with robust immunity to RF pulse and gradient switching noise. In conclusions, the proposed system showed the prevent saturation of measured biosignal and possibility of using cardiac gating and respiration gating method.

• Journal of Biomedical Engineering Research
• /
• v.28 no.3
• /
• pp.338-343
• /
• 2007

BOLD T2*-weighted MR images reflects cortical blood flow and oxygenation alterations. fMRI study relies on the detection of localized changes in BOLD signal intensity. Since fMRI measures the very small modulations in BOLD signal intensity that occur during changes in brain activity, it is also very sensitive to small signal intensity variations caused by physiologic noise during the scan. Due to the complexity of movement of various organs associated with heart beat, it is important to reduce cardiac related noise rather than other physiological noise which could be required with relatively simple method. Therefore, a number of methods have been developed for the estimation and reduction of cardiac noise in fMRI study. But, each method has limitation. In this study, we proposed a new estimation method for brain activities influenced by blood pulsation effect using regression analysis with blood pulsation signal and the correspond slice of fMRI. We could find out that the right anterior cingulate cortex and right olfactory cortex and left olfactory cortex were largely influenced by blood pulsation effect for new method. These observed areas are mostly on the structure of anterior cerebral artery in the brain. That is convinced with that our method would be valid and our new method is easier to apply in practice and reduce computational burden than the retrospective method.

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

• Korean Journal of Radiology
• /
• v.25 no.4
• /
• pp.374-383
• /
• 2024

Objective: To evaluate the diagnostic performance and image quality of 1.5-mm slice thickness MRI with deep learningbased image reconstruction (1.5-mm MRI + DLR) compared to routine 3-mm slice thickness MRI (routine MRI) and 1.5-mm slice thickness MRI without DLR (1.5-mm MRI without DLR) for evaluating temporal lobe epilepsy (TLE). Materials and Methods: This retrospective study included 117 MR image sets comprising 1.5-mm MRI + DLR, 1.5-mm MRI without DLR, and routine MRI from 117 consecutive patients (mean age, 41 years; 61 female; 34 patients with TLE and 83 without TLE). Two neuroradiologists evaluated the presence of hippocampal or temporal lobe lesions, volume loss, signal abnormalities, loss of internal structure of the hippocampus, and lesion conspicuity in the temporal lobe. Reference standards for TLE were independently constructed by neurologists using clinical and radiological findings. Subjective image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were analyzed. Performance in diagnosing TLE, lesion findings, and image quality were compared among the three protocols. Results: The pooled sensitivity of 1.5-mm MRI + DLR (91.2%) for diagnosing TLE was higher than that of routine MRI (72.1%, P < 0.001). In the subgroup analysis, 1.5-mm MRI + DLR showed higher sensitivity for hippocampal lesions than routine MRI (92.7% vs. 75.0%, P = 0.001), with improved depiction of hippocampal T2 high signal intensity change (P = 0.016) and loss of internal structure (P < 0.001). However, the pooled specificity of 1.5-mm MRI + DLR (76.5%) was lower than that of routine MRI (89.2%, P = 0.004). Compared with 1.5-mm MRI without DLR, 1.5-mm MRI + DLR resulted in significantly improved pooled accuracy (91.2% vs. 73.1%, P = 0.010), image quality, SNR, and CNR (all, P < 0.001). Conclusion: The use of 1.5-mm MRI + DLR enhanced the performance of MRI in diagnosing TLE, particularly in hippocampal evaluation, because of improved depiction of hippocampal abnormalities and enhanced image quality.

• Investigative Magnetic Resonance Imaging
• /
• v.8 no.1
• /
• pp.42-49
• /
• 2004

Purpose : The quantization noise in magnetic resonance imaging (MRI) systems is analyzed. The signal-to-quantization noise ratio (SQNR) in the reconstructed image is derived from the level of quantization in the signal in spatial frequency domain. Based on the derived formula, the SQNRs in various main magnetic fields with different receiver systems are evaluated. From the evaluation, the quantization noise could be a major noise source determining overall system signal-to-noise ratio (SNR) in high field MRI system. A few methods to reduce the quantization noise are suggested. Materials and methods : In Fourier imaging methods, spin density distribution is encoded by phase and frequency encoding gradients in such a way that it becomes a distribution in the spatial frequency domain. Thus the quantization noise in the spatial frequency domain is expressed in terms of the SQNR in the reconstructed image. The validity of the derived formula is confirmed by experiments and computer simulation. Results : Using the derived formula, the SQNRs in various main magnetic fields with various receiver systems are evaluated. Since the quantization noise is proportional to the signal amplitude, yet it cannot be reduced by simple signal averaging, it could be a serious problem in high field imaging. In many receiver systems employing analog-to-digital converters (ADC) of 16 bits/sample, the quantization noise could be a major noise source limiting overall system SNR, especially in a high field imaging. Conclusion : The field strength of MRI system keeps going higher for functional imaging and spectroscopy. In high field MRI system, signal amplitude becomes larger with more susceptibility effect and wider spectral separation. Since the quantization noise is proportional to the signal amplitude, if the conversion bits of the ADCs in the receiver system are not large enough, the increase of signal amplitude may not be fully utilized for the SNR enhancement due to the increase of the quantization noise. Evaluation of the SQNR for various systems using the formula shows that the quantization noise could be a major noise source limiting overall system SNR, especially in three dimensional imaging in a high field imaging. Oversampling and off-center sampling would be an alternative solution to reduce the quantization noise without replacement of the receiver system.

• Journal of Magnetics
• /
• v.20 no.3
• /
• pp.302-307
• /
• 2015

The purpose of the study was to identify how isotope and magnetic resonance imaging (MRI) contrast media impact on noise to computed tomography (CT) examination. For the study, divide the phantoms to two groups: 1) saline, saline + different kinds of contrast agent without $^{99m}Tc$ administration; 2) $^{99m}Tc$ administration: saline, saline + different kinds of contrast agent with $^{99m}Tc$ administration. CT contrast agent was used for Iopamidol$^{(R)}$ and Dotarem. And MRI contrast agent was used for Primovist$^{(R)}$ and Gadovist$^{(R)}$. To obtain an image, we used CT scanner. With an obtained image, we set the $1cm^2$ region of interest in the middle of bottle to measure the noise and CT number. As a result, there was no difference in CT number before and after inserting $^{99m}Tc$ into all contrast media including Normal Saline. However, when it comes to Noise, there was a difference before and after inserting $^{99m}Tc$ into every contrast media except MRI contrast media such as Primovist$^{(R)}$ and Gadovist$^{(R)}$.

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