• 제목/요약/키워드: imaging techniques

검색결과 1,053건 처리시간 0.028초

Novel Technology for View Angle Performance Measurement

  • Rykowski, Ronald;Lee, Jong-Seo
    • 한국정보디스플레이학회:학술대회논문집
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    • 한국정보디스플레이학회 2008년도 International Meeting on Information Display
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    • pp.1358-1361
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    • 2008
  • Several different techniques currently exist for measuring display view angle performance. These include conoscopes, goniometric systems, and, most recently introduced to the market, instruments based on Imaging Sphere technology. This paper will compare measurement accuracy and speed of these various methodologies with different FPD types and even 3-D displays.

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Functional Magnetic Resonance Imaging with Arterial Spin Labeling: Techniques and Potential Clinical and Research Applications

  • Kim, Ju Ho;Choi, Dae Seob;Park, Sung Eun;Choi, Ho Cheol;Kim, Seong Hu
    • Investigative Magnetic Resonance Imaging
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    • 제21권2호
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    • pp.91-96
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    • 2017
  • Purpose: To describe technical methods for functional magnetic resonance imaging (fMRI) study with arterial spin labeling (ASL) compared to blood oxygenation level-dependent (BOLD) technique and discuss the potential of ASL for research and clinical practice. Materials and Methods: Task-based (n = 1) and resting-state fMRI (rs-fMRI) (n = 20) were performed using ASL and BOLD techniques. Results of both techniques were compared. Results: For task-based fMRI with finger-tapping, the primary motor cortex of the contralateral frontal lobe and the ipsilateral cerebellum were activated by both BOLD and ASL fMRI. For rs-fMRI of sensorimotor network, functional connectivity showed similar results between BOLD and ASL. Conclusion: ASL technique has potential application in clinical and research fields because all brain perfusion imaging, CBF measurement, and rs-fMRI study can be performed in a single acquisition.

Applications of Digital Holography in Biomedical Microscopy

  • Kim, Myung-K.
    • Journal of the Optical Society of Korea
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    • 제14권2호
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    • pp.77-89
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    • 2010
  • Digital holography (DH) is a potentially disruptive new technology for many areas of imaging science, especially in microscopy and metrology. DH offers a number of significant advantages such as the ability to acquire holograms rapidly, availability of complete amplitude and phase information of the optical field, and versatility of the interferometric and image processing techniques. This article provides a review of the digital holography, with an emphasis on its applications in biomedical microscopy. The quantitative phase microscopy by DH is described including some of the special techniques such as optical phase unwrapping and holography of total internal reflection. Tomographic imaging by digital interference holography (DIH) and related methods is described, as well as its applications in ophthalmic imaging and in biometry. Holographic manipulation and monitoring of cells and cellular components is another exciting new area of research. We discuss some of the current issues, trends, and potentials.

Neuroimaging Studies of Chronic Pain

  • Kang, Do-Hyung;Son, June-Hee;Kim, Yong-Chul
    • The Korean Journal of Pain
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    • 제23권3호
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    • pp.159-165
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    • 2010
  • The evolution of brain imaging techniques over the last decade has been remarkable. Along with such technical developments, research into chronic pain has made many advances. Given that brain imaging is a non-invasive technique with great spatial resolution, it has played an important role in finding the areas of the brain related to pain perception as well as those related to many chronic pain disorders. Therefore, in the near future, brain imaging techniques are expected to be the key to the discovery of many unknown etiologies of chronic pain disorders and to the subjective diagnoses of such disorders.

Normal and Abnormal Postoperative Imaging Findings after Gastric Oncologic and Bariatric Surgery

  • Cheong-Il Shin;Se Hyung Kim
    • Korean Journal of Radiology
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    • 제21권7호
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    • pp.793-811
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    • 2020
  • Surgical resection remains the primary choice of treatment and the only potentially curative option for gastric carcinoma, and is increasingly performed laparoscopically. Gastric resection represents a challenging procedure, with a significant morbidity and non-negligible postoperative mortality. The interpretation of imaging after gastric surgery can be challenging due to significant modifications of the normal anatomy. After the surgery, the familiarity with expected imaging appearances is crucial for diagnosis and appropriate management of potentially life-threatening complications in patients who underwent gastric surgery. We review various surgical techniques used in gastric surgery and describe fluoroscopic and cross-sectional imaging appearances of normal postoperative anatomic changes as well as early and late complications after gastric surgery.

Detection of E.coli biofilms with hyperspectral imaging and machine learning techniques

  • Lee, Ahyeong;Seo, Youngwook;Lim, Jongguk;Park, Saetbyeol;Yoo, Jinyoung;Kim, Balgeum;Kim, Giyoung
    • 농업과학연구
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    • 제47권3호
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    • pp.645-655
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    • 2020
  • Bacteria are a very common cause of food poisoning. Moreover, bacteria form biofilms to protect themselves from harsh environments. Conventional detection methods for foodborne bacterial pathogens including the plate count method, enzyme-linked immunosorbent assays (ELISA), and polymerase chain reaction (PCR) assays require a lot of time and effort. Hyperspectral imaging has been used for food safety because of its non-destructive and real-time detection capability. This study assessed the feasibility of using hyperspectral imaging and machine learning techniques to detect biofilms formed by Escherichia coli. E. coli was cultured on a high-density polyethylene (HDPE) coupon, which is a main material of food processing facilities. Hyperspectral fluorescence images were acquired from 420 to 730 nm and analyzed by a single wavelength method and machine learning techniques to determine whether an E. coli culture was present. The prediction accuracy of a biofilm by the single wavelength method was 84.69%. The prediction accuracy by the machine learning techniques were 87.49, 91.16, 86.61, and 86.80% for decision tree (DT), k-nearest neighbor (k-NN), linear discriminant analysis (LDA), and partial least squares-discriminant analysis (PLS-DA), respectively. This result shows the possibility of using machine learning techniques, especially the k-NN model, to effectively detect bacterial pathogens and confirm food poisoning through hyperspectral images.

치매 환자에서 기능 영상법의 역할 (The Role of Functional Imaging Techniques in the Dementia)

  • 유영훈
    • 대한핵의학회지
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    • 제38권3호
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    • pp.209-217
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    • 2004
  • Evaluation of dementia in patients with early symptoms of cognitive decline is clinically challenging, but the need for early, accurate diagnosis has become more crucial, since several medication for the treatment of mild to moderate Alzheimer' disease are available. Many neurodegenerative diseases produce significant brain function alteration even when structural imaging (CT or MRI) reveal no specific abnormalities. The role of PET and SPECT brain imaging in the initial assessment and differential diagnosis of dementia is beginning to evolve vapidly and growing evidence indicates that appropriate incorporation of PET into the clinical work up can improve diagnostic and prognostic accuracy with respect to Alzheimer's disease, the most common cause of dementia in the geriatric population. in the fast few years, studios comparing neuropathologic examination with PET have established reliable and consistent accuracy for diagnostic evaluations using PET - accuracies substantially exceeding those of comparable studies of diagnostic value of SPECT or of both modalities assessed side by side, or of clinical evaluations done without nuclear imaging. This review deals the role of functional brain imaging techniques in the evaluation of dementias and the role of nuclear neuroimaging in the early detection and diagnosis of Alzheimer's disease.

3D 가상 이미지의 텍스타일 소재로의 적용을 통한 삼차원 변형가능한 'Living Textile'과 환경변화에 관한 연구(1) (An Investigation into Three Dimensional Mutable 'Living' Textile Materials and Environments(1))

  • 김기훈;서지성
    • 복식문화연구
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    • 제18권6호
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    • pp.1305-1317
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    • 2010
  • 본 연구는 다양한 3D imaging 기술이 생산해낼 수 있는 환영효과를 직물에 적용할 수 있는지에 대한 가능성을 통해서, 현실과 허구의 경계가 없어지는 새로운 환경을 조성하기 위한 직물을 개발하여 변형 가능한 삼차원의 살아있는 직물 같은, 관점에 따라 패턴과 색깔의 이미지가 바뀌는 흥미로운 직물패턴의 실현 가능성을 알아본다. 본 논문은 I, II로 나뉘어 있으며, 각 논문에서 각기 다른 실험을 실시하여 결과로의 적용가능성과 제한점을 살펴봄으로써 Holography, Lenticular, 등 가상의 3D 테크놀로지를 통해 2D 평면의 구조에 3D 가상 이미지의 텍스타일 적용 가능성을 기계적 실험을 통해 확인하며, 3D imaging 기술에 대한 경험과 이해를 얻고, 3D imaging 기술을 적용할 수 있는 잠재력을 연구하기 위한 것으로, 실험은 현장에 있는 전문 텍스타일연구가, 과학자, 예술가 그리고 디자이너들의 협업으로 이루어졌다.

Temporal Evolution of a Chronic Expanding Organizing Hematoma on MRI, Including Functional MR Imaging Techniques: a Case Report

  • Lee, Jeonghyun;Lee, Taebum;Oh, Eunsun;Yoon, Young Cheol
    • Investigative Magnetic Resonance Imaging
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    • 제21권1호
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    • pp.43-50
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    • 2017
  • Chronic expanding organizing hematoma (CEH) occasionally mimics a soft tissue tumor on MRI, which becomes more problematic in patients with a history of surgical resection for musculoskeletal malignancy. Herein, we present a case of CEH which we were able to differentiate from recurrent tumor through MRI follow-up, including diffusion-weighted imaging (DWI) and dynamic contrast enhanced (DCE) imaging. A 66-year-old male visited our institution under suspicion of recurrent leiomyosarcoma of the thigh, 19 months after surgery and radiation therapy. Due to inconclusive results, three US-guided biopsies and 6 MRI examinations were performed over 2 years. In the end, we could diagnose a CEH using conventional and functional MRI techniques, and it was histopathologically confirmed after surgical resection. A CEH may occur remotely after an initiating event, and it may persist and expand over several years. Functional MR sequences, in addition to conventional sequences, are helpful in differentiating CEH from malignant neoplasms.

의료영상 분야를 위한 설명가능한 인공지능 기술 리뷰 (A review of Explainable AI Techniques in Medical Imaging)

  • 이동언;박춘수;강정운;김민우
    • 대한의용생체공학회:의공학회지
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    • 제43권4호
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    • pp.259-270
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    • 2022
  • Artificial intelligence (AI) has been studied in various fields of medical imaging. Currently, top-notch deep learning (DL) techniques have led to high diagnostic accuracy and fast computation. However, they are rarely used in real clinical practices because of a lack of reliability concerning their results. Most DL models can achieve high performance by extracting features from large volumes of data. However, increasing model complexity and nonlinearity turn such models into black boxes that are seldom accessible, interpretable, and transparent. As a result, scientific interest in the field of explainable artificial intelligence (XAI) is gradually emerging. This study aims to review diverse XAI approaches currently exploited in medical imaging. We identify the concepts of the methods, introduce studies applying them to imaging modalities such as computational tomography (CT), magnetic resonance imaging (MRI), and endoscopy, and lastly discuss limitations and challenges faced by XAI for future studies.