• Investigative Magnetic Resonance Imaging
• /
• v.26 no.2
• /
• pp.125-134
• /
• 2022

Purpose: The aim of this study was to investigate the change in signal sensitivity over different acquisition start times and optimize the scanning window to provide the maximal signal sensitivity of [1-¹³C]pyruvate and its metabolic products, lactate and alanine, using spatially localized hyperpolarized 3D ¹³C magnetic resonance spectroscopic imaging (MRSI). Materials and Methods: We acquired 3D ¹³C MRSI data from the brain (n = 3), kidney (n = 3), and liver (n = 3) of rats using a 3T clinical scanner and a custom RF coil after the injection of hyperpolarized [1-¹³C]pyruvate. For each organ, we obtained three consecutive 3D ¹³C MRSI datasets with different acquisition start times per animal from a total of three animals. The mean signal-to-noise ratios (SNRs) of pyruvate, lactate, and alanine were calculated and compared between different acquisition start times. Based on the SNRs of lactate and alanine, we identified the optimal acquisition start timing for each organ. Results: For the brain, the acquisition start time of 18 s provided the highest mean SNR of lactate. At 18 s, however, the lactate signal predominantly originated from not the brain, but the blood vessels; therefore, the acquisition start time of 22 s was recommended for 3D ¹³C MRSI of the rat brain. For the kidney, all three metabolites demonstrated the highest mean SNR at the acquisition start time of 32 s. Similarly, the acquisition start time of 22 s provided the highest SNRs for all three metabolites in the liver. Conclusion: In this study, the acquisition start timing was optimized in an attempt to maximize metabolic signals in hyperpolarized 3D ¹³C MRSI examination with [1-¹³C] pyruvate as a substrate. We investigated the changes in metabolic signal sensitivity in the brain, kidney, and liver of rats to establish the optimal acquisition start time for each organ. We expect the results from this study to be of help in future studies.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.8
• /
• pp.2082-2102
• /
• 2024

Accurate segmentation of magnetic resonance (MR) images is crucial for providing doctors with effective quantitative information for diagnosis. However, the presence of weak boundaries, intensity inhomogeneity, and noise in the images poses challenges for segmentation models to achieve optimal results. While deep learning models can offer relatively accurate results, the scarcity of labeled medical imaging data increases the risk of overfitting. To tackle this issue, this paper proposes a novel fuzzy c-means (FCM) model that integrates a deep learning approach. To address the limited accuracy of traditional FCM models, which employ Euclidean distance as a distance measure, we introduce a measurement function based on the skewed normal distribution. This function enables us to capture more precise information about the distribution of the image. Additionally, we construct a regularization term based on the Kullback-Leibler (KL) divergence of high-confidence deep learning results. This regularization term helps enhance the final segmentation accuracy of the model. Moreover, we incorporate orthogonal basis functions to estimate the bias field and integrate it into the improved FCM method. This integration allows our method to simultaneously segment the image and estimate the bias field. The experimental results on both simulated and real brain MR images demonstrate the robustness of our method, highlighting its superiority over other advanced segmentation algorithms.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.4
• /
• pp.515-520
• /
• 2012

In brain computer interface (BCI) system, the most important part is classification of human thoughts in order to translate into commands. The more accuracy result in classification the system gets, the more effective BCI system is. To increase the quality of BCI system, we proposed to reduce noise and artifact from the recording data to analyzing data. We used auditory stimuli instead of visual ones to eliminate the eye movement, unwanted visual activation, gaze control. We applied independent component analysis (ICA) algorithm to purify the sources which constructed the raw signals. One of the most famous spatial filter in BCI context is common spatial patterns (CSP), which maximize one class while minimize the other by using covariance matrix. ICA and CSP also do the filter job, as a raw filter and refinement, which increase the classification result of linear discriminant analysis (LDA).

• Journal of the Optical Society of Korea
• /
• v.15 no.1
• /
• pp.103-108
• /
• 2011

Recently reported asynchronous-optical-sampling terahertz (THz) time-domain spectroscopy enables high-resolution spectroscopy due to a long time-delay window. However, a long-lasting tail signal following the main pulse is often measured in a time-domain waveform, resulting in spectral fluctuation above a background noise level on a high-resolution THz amplitude spectrum. Here, we adopt the wavelet power spectrum estimation technique (WPSET) to effectively remove the spectral fluctuation without sacrificing spectral features. Effectiveness of the WPSET is verified by investigating a transmission spectrum of water vapor.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.12
• /
• pp.35-41
• /
• 2018

In this paper, we propose a swarm-based possibilistic c-means(PCM) algorithm in order to overcome the problems of PCM, which are sensitiveness of clustering performance due to initial cluster center's values and producing coincident or close clusters. To settle the former problem of PCM, we adopt a swam-based global optimization method which can be provided the optimal initial cluster centers. Furthermore, to settle the latter problem of PCM, we design an adaptive thresholding model based on the optimized cluster centers that yields preliminary clustered and un-clustered dataset. The preliminary clustered dataset plays a role of preventing coincident or close clusters and the un-clustered dataset is lastly clustered by PCM. From the experiment, the proposed method obtains a better performance than other PCM algorithms on a simulated magnetic resonance(MR) brain image dataset which is corrupted by various noises and bias-fields.

• ETRI Journal
• /
• v.36 no.2
• /
• pp.242-252
• /
• 2014

Since July of 2012, the 3D video extension of H.264/AVC has been under development to support the multi-view video plus depth format. In 3D video applications such as multi-view and free-view point applications, synthesized views are generated using coded texture video and coded depth video. Such synthesized views can be distorted by quantization noise and inaccuracy of 3D wrapping positions, thus it is important to improve their quality where possible. To achieve this, the relationship among the depth video, texture video, and synthesized view is investigated herein. Based on this investigation, an edge noise suppression filtering process to preserve the edges of the depth video and a method based on a total variation approach to maximum a posteriori probability estimates for reducing the quantization noise of the coded texture video. The experiment results show that the proposed methods improve the peak signal-to-noise ratio and visual quality of a synthesized view compared to a synthesized view without post processing methods.

• Journal of Audiology & Otology
• /
• v.25 no.1
• /
• pp.22-26
• /
• 2021

Background and Objectives: Music listening has a concomitant effect on structural and functional organization of the brain. It helps in relaxation, mind training and neural strengthening. In relation to it, the present study was aimed to find the effect of Carnatic music listening training (MLT) on speech in noise performance in adults. Subjects and Methods: A total of 28 participants (40-70 years) were recruited in the study. Based on randomized control trial, they were divided into intervention and control group. Intervention group underwent a short-term MLT. Quick Speech-in-Noise in Kannada was used as an outcome measure. Results: Results were analysed using mixed method analysis of variance (ANOVA) and repeated measures ANOVA. There was a significant difference between intervention and control group post MLT. The results of the second continuum revealed no statistically significant difference between post training and follow-up scores in both the groups. Conclusions: In conclusion short-term MLT resulted in betterment of speech in noise performance. MLT can be hence used as a viable tool in formal auditory training for better prognosis.

• Korean Journal of Audiology
• /
• v.25 no.1
• /
• pp.22-26
• /
• 2021

Background and Objectives: Music listening has a concomitant effect on structural and functional organization of the brain. It helps in relaxation, mind training and neural strengthening. In relation to it, the present study was aimed to find the effect of Carnatic music listening training (MLT) on speech in noise performance in adults. Subjects and Methods: A total of 28 participants (40-70 years) were recruited in the study. Based on randomized control trial, they were divided into intervention and control group. Intervention group underwent a short-term MLT. Quick Speech-in-Noise in Kannada was used as an outcome measure. Results: Results were analysed using mixed method analysis of variance (ANOVA) and repeated measures ANOVA. There was a significant difference between intervention and control group post MLT. The results of the second continuum revealed no statistically significant difference between post training and follow-up scores in both the groups. Conclusions: In conclusion short-term MLT resulted in betterment of speech in noise performance. MLT can be hence used as a viable tool in formal auditory training for better prognosis.

• Journal of radiological science and technology
• /
• v.39 no.1
• /
• pp.51-57
• /
• 2016

Acoustic noise during magnetic resonance imaging (MRI) is the main source for patient discomfort. we report our preliminary experience with this technique in neuroimaging with regard to subjective and objective noise levels and image quality. 60 patients(29 males, 31 females, average age of 60.1) underwent routine brain MRI with 3.0 Tesla (MAGNETOM Tim Trio; Siemens, Germany) system and 12-channel head coil. Q-$T_2$ and $T_2$ sequence were performed. Measurement of sound pressure levels (SPL) and heart rate on Q-$T_2$ and $T_2$ was performed respectively. Quantitative analysis was carried out by measuring the SNR, CNR, and SIR values of Q-$T_2$, $T_2$ and a statistical analysis was performed using independent sample T-test. Qualitative analysis was evaluated by the eyes for the overall quality image of Q-$T_2$ and $T_2$. A 5-point evaluation scale was used, including excellent(5), good(4), fair(3), poor(2), and unacceptable(1). The average noise and peak noise decreased by $15dB_A$ and $10dB_A$ on $T_2$ and Q-$T_2$ test. Also, the average value of heartbeat rate was lower in Q-$T_2$ for 120 seconds in each test, but there was no statistical significance. The quantitative analysis showed that there was no significant difference between CNR and SIR, and there was a significant difference (p<0.05) as SNR had a lower average value on Q-$T_2$. According to the qualitative analysis, the overall quality image of 59 case $T_2$ and Q-$T_2$ was evaluated as excellent at 5 points, and 1 case was evaluated as good at 4 points due to a motion artifact. Q-$T_2$ is a promising technique for acoustic noise reduction and improved patient comfort.

• Journal of radiological science and technology
• /
• v.40 no.2
• /
• pp.275-280
• /
• 2017

In addition to tumors, normal tissues, such as the brain and myocardium can intake $^{18}F$-FDG, and the amount of $^{18}F$-FDG intake by normal tissues can be altered by the surrounding environment. Therefore, a process is necessary during which the contrasts of the tumor and normal tissues can be enhanced. Thus, this study examines the effects of glucose levels on FDG PET images of brain tissues, which features high glucose activity at all times, in small animals. Micro PET scan was performed on fourteen mice after injecting $^{18}F$-FDG. The images were compared in relation to fasting. The findings showed that the mean SUV value w as 0.84 higher in fasted mice than in non-fasted mice. During observation, the images from non-fasted mice showed high accumulation in organs other than the brain with increased surrounding noise. In addition, compared to the non-fasted mice, the fasted mice showed higher early intake and curve increase. The findings of this study suggest that fasting is important in assessing brain functions in brain PET using $^{18}F$-FDG. Additional studies to investigate whether caffeine levels and other preprocessing items have an impact on the acquired images would contribute to reducing radiation exposure in patients.