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Volume 34 , Issue 3
Showing 1-10 articles out of 10 articles from the selected issue
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Main Topics / Multidisciplinary Computational Anatomy
  • Yoshinobu SATO
    Volume 34 (2016) Issue 3 Pages 133-134
    Released: May 29, 2016
    JOURNALS RESTRICTED ACCESS
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  • Kenoki OHUCHIDA, Makoto HASHIZUME
    Volume 34 (2016) Issue 3 Pages 135-138
    Released: May 29, 2016
    JOURNALS RESTRICTED ACCESS
    Multidisciplinary computational anatomy comprises scientific research on innovative areas based on medical images. Multidisciplinary computational anatomy includes several axes such as the spatial axis, from a cell size to an organ size level, the time series axis from an embryo to postmortem body, the functional axis including medical image modality, physiology or metabolism and the pathological axis from a healthy physical condition to a diseased condition. In this new scientific area, we establishes a mathematical analysis base for “a comprehensive and useful understanding of the human body”, and defines a new mathematical method for early detection and a high intelligent diagnostic treatment method for diseases with difficult medical treatment. This innovative area achieves mathematical foundation enabling us to deal with not only the static computational anatomy model for handling shape but also dynamic computational anatomy model (multidisciplinary computational anatomy) for handling living human built from useful multidisciplinary information about the human body.
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  • Hidekata HONTANI, Akinobu SHIMIZU, Yoshinobu SATO
    Volume 34 (2016) Issue 3 Pages 139-143
    Released: May 29, 2016
    JOURNALS RESTRICTED ACCESS
    This paper presents objectives, plans and recent achievements of three groups in the item A01: “A01-1 Mathematical Foundations of Multi disciplinary Computational Anatomy”, “A01-2 Fundamental Technologies for Integration of Multiscale Spatio temporal Morphology in Multidisciplinary Computational Anatomy”, and “A01-3 Fundamental Technologies for Integra tion of Function and Pathology in Multidisciplinary Computational Anatomy”.
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  • Noboru NIKI, Hiroshi FUJITA, Kensaku MORI
    Volume 34 (2016) Issue 3 Pages 144-150
    Released: May 29, 2016
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    A02 group has been developing highly-intelligent diagnostic treatment and surgery systems based on multidisciplinary computational anatomy. The system leads to solutions of many diagnostic and treatment problems using basic mathematical technologies of multidisciplinary images.
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  • Etsuko KOBAYASHI, Shoji KIDO, Kenoki OHUCHIDA, Makoto HASHIZUME
    Volume 34 (2016) Issue 3 Pages 151-156
    Released: May 29, 2016
    JOURNALS RESTRICTED ACCESS
    In clinical practice, diagnosis and treatment are being carried out on the basis of a variety of medical images in addition to X-ray CT image. Therefore, it is necessary to develop a new model that integrates various medical images. We are planning to develop a novel model of “Multidisciplinary Computational Anatomy (MCA)” that combines time and space axes, functional axis, and pathology axis in collaboration with research group A01 and A02. In this paper, we introduce research outline of applications of MCA to surgery, diagnosis and biomedical engineering. In the application to surgery, surgical simulation and navigation system and prediction system of therapeutic effect based on MCA were introduced. In the application to diagnosis, robust algorithms for analyzing pathological lungs, computational fluid simulation for evaluating respiratory function after lobectomy, CAD diagnosis algorithm of estimation of elapsed times after death for autopsy images, three-dimensional scanned images of organs as new pathological images were described. In the application to biomedical engineering, basic technology which is required for combination surgical robots and MCA were introduced.
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Paper
  • Kenta TAKANOHASHI, Masahiro OGINO
    Volume 34 (2016) Issue 3 Pages 157-164
    Released: May 29, 2016
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    This paper proposes a noise reduction algorithm for images of medical X-ray fluoroscopy. Fluoroscopy generally operates at a low dose condition to reduce radiation exposures for technicians and patients. However, image noises generated by that condition degrade visibility of target objects in images. A conventional method generates image lags in the neighborhood of moving objects and another method is difficult to perform all calculations in real time. To address these problems, this paper proposes a noise reduction method, which detects motion of images using whole image information and applies time-space filter while tracking the motion. The method achieved reducing noise without image lags and real-time processing at 30 frames per seconds.
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Tutorial
  • Fumihiko NISHIKIDO
    Volume 34 (2016) Issue 3 Pages 165-169
    Released: May 29, 2016
    JOURNALS RESTRICTED ACCESS
    Positron emission tomography (PET) scanners generally consist of many PET detectors. The PET detector is important component because reconstructed imaging performance of the PET scanner depend on the PET detector performance. In this article, the basics of the PET detector are described such as important parameter, basic structure and components of the PET detector. Finally, depth-of-interaction (DOI) PET detector which is one of major topics in research field of the PET detector is introduced.
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