• Title/Summary/Keyword: brain noise

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A Study on Radiation Dose and Image Quality according to CT Table Height in Brain CT (두부 CT 검사 시 테이블 높이에 따른 선량과 화질에 관한 연구)

  • Ki-Won Kim;Joo-Young Oh;Jung-Whan Min;Sang-Sun Lee;Young-Bong Lee;Kyung-Hwan Lim;Yun Yi
    • Journal of radiological science and technology
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    • v.46 no.2
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    • pp.99-106
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    • 2023
  • The height of the table should be considered important during computed tomography (CT) examination, but according to previous studies, not all radiology technologists set the table at the patient's center at the examination, which affects the exposure dose and image quality received by the patient. Therefore, this study intends to study the image quality exposure dose according to the height of the table to realize the optimal image quality and dose during the brain CT scan. The head phantom images were acquired using Philips Brilliance iCT 256. When the image was acquired, the table height was adjusted to 815, 865, 915, 965, 1015, and 1030 mm, respectively, and each scan was performed 3 times for each height. For the exposure dose measurement, optically stimulated luminescence dosimeter (OSLD) was attached to the front, side, eye, and thyroid gland of the head phantom. In the signal to noise ratio (SNR) measurement result, The SNR values for each table height were all lower than 915 mm. As a result of exposure dose, the exposure dose on each area increased as the table height decreased. The height of the table has a close relationship with the patient's radiation exposure dose in the CT scan.

A Study on the Artifact Reduction Method of Magnetic Resonance Imaging in Dental Implants and Prostheses (치아 임플란트와 보철에서 발생하는 자기공명영상의 인공물 감소방안 연구)

  • Shin, Woon-Jae
    • Journal of the Korean Society of Radiology
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    • v.13 no.7
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    • pp.1025-1033
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    • 2019
  • Although magnetic resonance imaging without linear hardening of CT is recognized as a method of obtaining high contrast of tissue and excellent resolution image in brain disease and head and neck examination, magnetic susceptibility artifact is generated in case of metal implants in the oral cavity, which is an obstacle to image diagnosis. Therefore, an effort was made in this thesis to find a method to reduce artifacts caused by dental implants and prosthesis in MRI. Implant-induced artifacts in magnetic resonance imaging showed that the signal size increased with shorter TE in GE technique and was inconsistent with water temperature change. In SE technique as well, the signal size of water was generally higher than that of air, but the signal to noise ratio (SNR) was not different by air and temperature. In EPI technique, images with fewer artifacts were obtained quantitatively and qualitatively when there was more water than air, and the signal to noise ratio was measured the highest, especially at water temperatures of 20° and 30°. In conclusion, when examining using the EPI technique rather than the SE or the GE technique, obtaining brain diffusion using a 20° and 30° water bag reduces the magnetic susceptibility artifacts caused by implants and prosthesis, suggesting that it may provide images with high diagnostic value.

Co-registration of PET-CT Brain Images using a Gaussian Weighted Distance Map (가우시안 가중치 거리지도를 이용한 PET-CT 뇌 영상정합)

  • Lee, Ho;Hong, Helen;Shin, Yeong-Gil
    • Journal of KIISE:Software and Applications
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    • v.32 no.7
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    • pp.612-624
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    • 2005
  • In this paper, we propose a surface-based registration using a gaussian weighted distance map for PET-CT brain image fusion. Our method is composed of three main steps: the extraction of feature points, the generation of gaussian weighted distance map, and the measure of similarities based on weight. First, we segment head using the inverse region growing and remove noise segmented with head using region growing-based labeling in PET and CT images, respectively. And then, we extract the feature points of the head using sharpening filter. Second, a gaussian weighted distance map is generated from the feature points in CT images. Thus it leads feature points to robustly converge on the optimal location in a large geometrical displacement. Third, weight-based cross-correlation searches for the optimal location using a gaussian weighted distance map of CT images corresponding to the feature points extracted from PET images. In our experiment, we generate software phantom dataset for evaluating accuracy and robustness of our method, and use clinical dataset for computation time and visual inspection. The accuracy test is performed by evaluating root-mean-square-error using arbitrary transformed software phantom dataset. The robustness test is evaluated whether weight-based cross-correlation achieves maximum at optimal location in software phantom dataset with a large geometrical displacement and noise. Experimental results showed that our method gives more accuracy and robust convergence than the conventional surface-based registration.

Comparison of Algorithms for Generating Parametric Image of Cerebral Blood Flow Using ${H_2}^{15}O$ PET Positron Emission Tomography (${H_2}^{15}O$ PET을 이용한 뇌혈류 파라메트릭 영상 구성을 위한 알고리즘 비교)

  • Lee, Jae-Sung;Lee, Dong-Soo;Park, Kwang-Suk;Chung, June-Key;Lee, Myung-Chul
    • The Korean Journal of Nuclear Medicine
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    • v.37 no.5
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    • pp.288-300
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    • 2003
  • Purpose: To obtain regional blood flow and tissue-blood partition coefficient with time-activity curves from ${H_2}^{15}O$ PET, fitting of some parameters in the Kety model is conventionally accomplished by nonlinear least squares (NLS) analysis. However, NLS requires considerable compuation time then is impractical for pixel-by-pixel analysis to generate parametric images of these parameters. In this study, we investigated several fast parameter estimation methods for the parametric image generation and compared their statistical reliability and computational efficiency. Materials and Methods: These methods included linear least squres (LLS), linear weighted least squares (LWLS), linear generalized least squares (GLS), linear generalized weighted least squares (GWLS), weighted Integration (WI), and model-based clustering method (CAKS). ${H_2}^{15}O$ dynamic brain PET with Poisson noise component was simulated using numerical Zubal brain phantom. Error and bias in the estimation of rCBF and partition coefficient, and computation time in various noise environments was estimated and compared. In audition, parametric images from ${H_2}^{15}O$ dynamic brain PET data peformed on 16 healthy volunteers under various physiological conditions was compared to examine the utility of these methods for real human data. Results: These fast algorithms produced parametric images with similar image qualify and statistical reliability. When CAKS and LLS methods were used combinedly, computation time was significantly reduced and less than 30 seconds for $128{\times}128{\times}46$ images on Pentium III processor. Conclusion: Parametric images of rCBF and partition coefficient with good statistical properties can be generated with short computation time which is acceptable in clinical situation.

Evaluation of Retro recon for SRS planning correction according to the error of recognize to coordinate (SRS의 좌표 인식 오류 시 Retro recon을 이용한 수정 방법에 관한 평가)

  • Moon, hyeon seok;Jeong, deok yang;Do, gyeong min;Lee, yeong cheol;Kim, sun myung;Kim, young bum
    • The Journal of Korean Society for Radiation Therapy
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    • v.28 no.2
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    • pp.101-108
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    • 2016
  • Purpose : The purpose of this study was to evaluate the Retro recon in SRS planning using BranLAB when stereotactic location error occurs by metal artifact. Materials and Methods : By CT simulator, image were acquired from head phantom(CIRS, PTW, USA). To observe stereotactic location recognizing and beam hardening, CT image were approved by SRS planning system(BrainLAB, Feldkirchen, Germany). In addition, we compared acquisition image(1.25mm slice thickness) and Retro recon image(using for 2.5 mm, 5mm slice thickness). To evaluate these three images quality, the test were performed by AAPM phantom study. In patient, it was verified stereotactic location error. Results : All the location recognizing error did not occur in scanned image of phantom. AAPM phantom scan images all showed the same trend. Contrast resolution and Spatial resolution are under 6.4 mm, 1.0 mm. In case of noise and uniformity, under 11, 5 of HU were measured. In patient, the stereotactic location error was not occurred at reconstructive image. Conclusion : For BrainLAB planning, using Retro recon were corrected stereotactic error at beam hardening. Retro recon may be the preferred modality for radiation treatment planning and approving image quality.

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Development of Learning Algorithm using Brain Modeling of Hippocampus for Face Recognition (얼굴인식을 위한 해마의 뇌모델링 학습 알고리즘 개발)

  • Oh, Sun-Moon;Kang, Dae-Seong
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.42 no.5 s.305
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    • pp.55-62
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    • 2005
  • In this paper, we propose the face recognition system using HNMA(Hippocampal Neuron Modeling Algorithm) which can remodel the cerebral cortex and hippocampal neuron as a principle of a man's brain in engineering, then it can learn the feature-vector of the face images very fast and construct the optimized feature each image. The system is composed of two parts. One is feature-extraction and the other is teaming and recognition. In the feature extraction part, it can construct good-classified features applying PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis) in order. In the learning part, it cm table the features of the image data which are inputted according to the order of hippocampal neuron structure to reaction-pattern according to the adjustment of a good impression in the dentate gyrus region and remove the noise through the associate memory in the CA3 region. In the CA1 region receiving the information of the CA3, it can make long-term memory learned by neuron. Experiments confirm the each recognition rate, that are face changes, pose changes and low quality image. The experimental results show that we can compare a feature extraction and learning method proposed in this paper of any other methods, and we can confirm that the proposed method is superior to existing methods.

Singular Value Decomposition based Noise Reduction Technique for Dynamic PET I mage : Preliminary study (특이값 분해 기반 Dynamic PET 영상의 노이즈 제거 기법 : 예비 연구)

  • Pyeon, Do-Yeong;Kim, Jung-Su;Baek, Cheol-Ha;Jung, Young-Jin
    • Journal of radiological science and technology
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    • v.39 no.2
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    • pp.227-236
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    • 2016
  • Dynamic positron emission tomography(dPET) is widely used medical imaging modality that can provide both physiological and functional neuro-image for diagnosing various brain disease. However, dPET images have low spatial-resolution and high noise level during spatio-temporal analysis (three-dimensional spatial information + one-dimensional time information), there by limiting clinical utilization. In order to overcome these issues for the spatio-temporal analysis, a novel computational technique was introduced in this paper. The computational technique based on singular value decomposition classifies multiple independent components. Signal components can be distinguished from the classified independent components. The results show that signal to noise ratio was improved up to 30% compared with the original images. We believe that the proposed computational technique in dPET can be useful tool for various clinical / research applications.

A 4×32-Channel Neural Recording System for Deep Brain Stimulation Systems

  • Kim, Susie;Na, Seung-In;Yang, Youngtae;Kim, Hyunjong;Kim, Taehoon;Cho, Jun Soo;Kim, Jinhyung;Chang, Jin Woo;Kim, Suhwan
    • JSTS:Journal of Semiconductor Technology and Science
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    • v.17 no.1
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    • pp.129-140
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    • 2017
  • In this paper, a $4{\times}32$-channel neural recording system capable of acquiring neural signals is introduced. Four 32-channel neural recording ICs, complex programmable logic devices (CPLDs), a micro controller unit (MCU) with USB interface, and a PC are used. Each neural recording IC, implemented in $0.18{\mu}m$ CMOS technology, includes 32 channels of analog front-ends (AFEs), a 32-to-1 analog multiplexer, and an analog-to-digital converter (ADC). The mid-band gain of the AFE is adjustable in four steps, and have a tunable bandwidth. The AFE has a mid-band gain of 54.5 dB to 65.7 dB and a bandwidth of 35.3 Hz to 5.8 kHz. The high-pass cutoff frequency of the AFE varies from 18.6 Hz to 154.7 Hz. The input-referred noise (IRN) of the AFE is $10.2{\mu}V_{rms}$. A high-resolution, low-power ADC with a high conversion speed achieves a signal-to-noise and distortion ratio (SNDR) of 50.63 dB and a spurious-free dynamic range (SFDR) of 63.88 dB, at a sampling-rate of 2.5 MS/s. The effectiveness of our neural recording system is validated in in-vivo recording of the primary somatosensory cortex of a rat.

Design of Variable Gain Receiver Front-end with Wide Gain Variable Range and Low Power Consumption for 5.25 GHz (5.25 GHz에서 넓은 이득 제어 범위를 갖는 저전력 가변 이득 프론트-엔드 설계)

  • Ahn, Young-Bin;Jeong, Ji-Chai
    • Journal of IKEEE
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    • v.14 no.4
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    • pp.257-262
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    • 2010
  • We design a CMOS front-end with wide variable gain and low power consumption for 5.25 GHz band. To obtain wide variable gain range, a p-type metal-oxide-semiconductor field-effect transistor (PMOS FET) in the low noise amplifier (LNA) section is connected in parallel. For a mixer, single balanced and folded structure is employed for low power consumption. Using this structure, the bias currents of the transconductance and switching stages in the mixer can be separated without using current bleeding path. The proposed front-end has a maximum gain of 33.2 dB with a variable gain range of 17 dB. The noise figure and third-order input intercept point (IIP3) are 4.8 dB and -8.5 dBm, respectively. For this operation, the proposed front-end consumes 7.1 mW at high gain mode, and 2.6 mW at low gain mode. The simulation results are performed using Cadence RF spectre with the Taiwan Semiconductor Manufacturing Company (TSMC) $0.18\;{\mu}m$ CMOS technology.)

A Bayesian Validation Method for Classification of Microarray Expression Data (마이크로어레이 발현 데이터 분류를 위한 베이지안 검증 기법)

  • Park, Su-Young;Jung, Jong-Pil;Jung, Chai-Yeoung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.11
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    • pp.2039-2044
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    • 2006
  • Since the bio-information now even exceeds the capability of human brain, the techniques of data mining and artificial intelligent are needed to deal with the information in this field. There are many researches about using DNA microarray technique which can obtain information from thousands of genes at once, for developing new methods of analyzing and predicting of diseases. Discovering the mechanisms of unknown genes by using these new method is expecting to develop the new drugs and new curing methods. In this Paper, We tested accuracy on classification of microarray in Bayesian method to compare normalization method's Performance after dividing data in two class that is a feature abstraction method through a normalization process which reduce or remove noise generating in microarray experiment by various factors. And We represented that it improve classification performance in 95.89% after Lowess normalization.