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  • Selections from the AIMED Digest, June 1991

    Dear Biomch-L readers,

    Below are a number of Conference Announcements and Dissertation Abstracts
    from the June 1991 AI-Medicine Digest.

    With kind regards -- Herman J. Woltring, Eindhoven/NL.

    -------------------------------------------------------------------------

    Date: Thu, 13 Jun 91 22:40:34 CDT
    Originally_from: ai-medicine digest Vol. 1 No. 8

    * Send submissions to: ai-medicine@vuse.vanderbilt.edu
    * Send administrative mail to: ai-medicine-REQUEST@vuse.vanderbilt.edu

    * Moderator: Serdar Uckun, MD, Vanderbilt University

    * An archive of the digest is available via anonymous ftp from
    lhc.nlm.nih.gov (130.14.1.128). Login as 'ftp', use your
    username as password, cd to pub/ai-medicine. Use 'get filename'
    to copy files to your system. A list of all current members is
    available in the same directory [450 today -- HJW].


    * SECOND INTERNATIONAL WORKSHOP ON PRINCIPLES OF DIAGNOSIS
    (Milano, Italy, October 14-16, 1991):

    Info: Luca Console
    Email: lconsole@pianeta.di.unito.it
    Phone: [+39] (11) 771-2002
    Fax: [+39] (11) 751-603


    * 13th ANNUAL MEETING OF THE SOCIETY FOR MEDICAL DECISION MAKING
    (Rochester, NY, October 20-23, 1991):

    Info: Society for Medical Decision Making
    The George Washington University
    Office of Continuing Medical Education
    2300 K Street, NW, Washington, DC 20037
    Phone: [+1] (202) 994-8929
    Fax: [+1] (202) 994-1791


    * 13th INT'L IEEE EMBS CONFERENCE
    (Orlando, FL, October 31-November 3, 1991):

    Info: c/o LRW Associates
    Phone: [+1] (301) 647-1591


    * Symposium on Computer Applications in Medical Care (SCAMC'91):
    Washington, DC, November 17-21, 1991:

    Info: American Medical Informatics Association
    Suite 302
    4915 St. Elmo Avenue
    Bethesda, MD 20814


    * Biomedical Image Processing III and 3-D Microscopy:
    (San Jose, CA, February 9-14, 1992):
    --> abstracts due: July 15, 1991

    Info: SPIE/SPSE Technical Program Committee:
    Electronic Imaging: Science and Technology 1992
    P.O. Box 10, Bellingham, WA 98227-0010
    Phone: [+1] (206) 676-3290
    Fax: [+1] (206) 647-1445


    * MEDINFO 1992 (Geneva, Switzerland, September 6-10, 1992):
    --> send proposal NOW. Submissions due October 15, 1991.

    Info: Medinfo 92 Administrative Office
    E-mail: mednfo92@cih.hcuge.ch
    Phone: [+41] (22) 786 37 44

    ------------------------------

    From: kk@aipna.edinburgh.ac.uk
    Date: Thu, 16 May 91 12:08:52 BST
    To: ai-medicine@vuse
    Subject: favourite diagnostic algorithms

    I'm currently trying to amass as many different `diagnostic
    algorithms' as I can. The methods don't need to be *good* ones, I
    merely want to collect as many as possible at the moment and do the
    assessment later. One aim is to discover the range of methods
    available; i.e. what all diagnostic methods have in common (if
    anything) and what the differences are.

    They seem to come from several potential sources:
    Diagnostic practice: how people actually do it
    Training: how people are taught to do it
    Introspection: how people think they do it
    Extant implementations: how computational tools do it
    Imagination: how people, or machines, might do it

    One problem with all these sources (except perhaps the last) is that
    it's very difficult to disentangle the `task' information from the
    `domain' information. Descriptions of diagnostic method a rare enough,
    but they always seem to be obscured be specific case details. This
    is also, surprisingly, true of descriptions of computational methods,
    although `rational reconstructions' are possibly better. Maybe it has
    to be this way...

    Apologies for not including any examples in this message, but I fear
    they might colour the range of response if I did. I'd like to
    initially throw the net as wide as possible.

    All ideas, pointers to good descriptions in the literature or (best)
    personal descriptions of your fave diagnostic algorithm most
    gratefully received.

    kathleen

    Kathleen King, Dept of AI, JANET: kk@uk.ac.ed.aipna
    University of Edinburgh, ARPA:kk%uk.ac.ed.aipna@nsfnet-relay.ac.uk
    80 South Bridge, Edinburgh UUCP: ...!ukc!aipna.ed.ac.uk!kk

    ------------------------------

    Subject: Some Dissertation Abstracts - June 1991
    Original Date: Thu, 13 Jun 91 22:49:35 EDT
    4 out of 10 selected by HJW from: humphrey@nlm.nih.gov (Susanne M Humphrey)

    AU LIANG, ROYCE CHING-YU.
    TI KNOWLEDGE-BASED SIGNAL PROCESSING AND SCORING SYSTEM FOR THE EXERCISE
    ELECTROCARDIOGRAM.
    IN The University of Utah Ph.D. 1990, 134 pages.

    AU WU, JIANG.
    TI THREE-DIMENSIONAL RECONSTRUCTION OF CORONARY ARTERIES FROM MULTIPLE
    PROJECTIONS.
    IN The University of Utah Ph.D. 1991, 115 pages.

    AU FISHER, DAVID JUDE.
    TI AUTOMATIC TRACKING OF CARDIAC WALL MOTION USING MAGNETIC RESONANCE
    MARKERS.
    IN The University of Iowa Ph.D. 1990, 220 pages.

    AU AL-SALEH, RAED IBRAHEEM.
    TI GEOMETRIC SOLID MODEL GENERATION FROM COMPUTED TOMOGRAPHY SCANS.
    IN University of Louisville M.S. 1990, 100 pages.

    Introduction

    The following are citations selected by title and abstract as being of
    potential interest to the ai-medicine community, resulting from a
    computer search, using the BRS Information Technologies retrieval
    service, of the Dissertation Abstracts International (DAI) database
    produced by University Microfilms International (UMI). Included are UMI
    order number, title, author, degree, year, institution; number of pages,
    one or more DAI subject descriptors chosen by the author, and abstract.
    Unless otherwise specified, paper or microform copies of dissertations
    may be ordered from University Microfilms International, Dissertation
    Copies, Post Office Box 1764, Ann Arbor, MI 48106; telephone for U.S.
    (except Michigan, Hawaii, Alaska): 1-800-521-3042, for Canada:
    1-800-268-6090; fax: 313-973-1540. Price lists and other ordering and
    shipping information are in the introduction to the published DAI. An
    alternate source for copies is sometimes provided. Dissertation titles
    and abstracts contained here are published with permission of University
    Microfilms International, publishers of Dissertation Abstracts
    International (copyright by University Microfilms International), and
    may not be reproduced without their prior permission.

    AN University Microfilms Order Number ADG91-12351.
    AU LIANG, ROYCE CHING-YU.
    TI KNOWLEDGE-BASED SIGNAL PROCESSING AND SCORING SYSTEM FOR THE EXERCISE
    ELECTROCARDIOGRAM.
    IN The University of Utah Ph.D. 1990, 134 pages.
    SO DAI V51(12), SecB, pp5975.
    DE Computer Science. Health Sciences, Radiology.
    Biophysics, Medical. Artificial Intelligence.
    AB Analysis of exercise electrocardiogram (ECG) signals is used to
    evaluate the status of patients suspected of having ischemia
    problems. Since exercise tests generate a large volume of ECG
    signals, several computerized systems have been built to match the
    special needs of the exercise testing using predesigned methods. If
    systems, however, have all been programmed with fixed processing
    algorithms set by the developer, there is limited ability for the
    user to modify the methodologies used to obtain measurements and to
    interpret test results.

    The goal of this research was to develop a knowledge base controlled
    digital signal processing and numerical scoring system. The system's
    flexibility was provided by the knowledge based structure other than
    predesigned subroutines. The scores are numerical results calculated
    by user defined scoring equations and rules based on the test data.
    Each test can have one or more scores which are the integration of
    the measurements and can be treated as the medical decision of the
    test.

    With the knowledge based structure, the system can be used not only
    as a clinical tool for evaluation of coronary artery disease using
    accepted predefined scoring rules, but also as an exercise testing
    methodology research tool to test user defined rules.

    Results accuracy and the system flexibility have been tested based on
    real data from patients and artificial data. The system is now being
    used at the exercise testing facility of LDS Hospital, Salt Lake
    City, Utah.

    AN University Microfilms Order Number ADG91-14782.
    AU WU, JIANG.
    TI THREE-DIMENSIONAL RECONSTRUCTION OF CORONARY ARTERIES FROM MULTIPLE
    PROJECTIONS.
    IN The University of Utah Ph.D. 1991, 115 pages.
    SO DAI V51(12), SecB, pp5983.
    DE Computer Science. Biophysics, General.
    AB Research is conducted to develop and evaluate quantitative techniques
    to assess the morphology of major coronary arteries by means of
    three-dimensional (3-D) reconstructions from X-ray angiography. The
    fundamental research hypothesis is: positions and dimensions of major
    coronary arteries can be obtained, to the accuracy and precision on
    the order of the pixel dimension, by systematically integrating
    geometric and densitometric information extracted from a few views of
    X-ray angiograms through 3-D reconstruction. With sufficient
    reconstruction accuracy and precision, this technique can provide
    global quantitative information about the coronary arteries that may
    not be available using other techniques.

    A multi-view reconstruction algorithm is developed and is implemented
    on a graphics-oriented workstation. The output of the system is a
    3-D tree structure, which includes vessel positions and dimensions
    for each branch, of the coronary arterial bed. The final
    reconstructed tree is obtained by iteratively incorporating into the
    structure 2-D geometric and densitometric vascular information form
    multiple views. Edge detection using matched-filtering and dynamic
    programming provides vessel geometric edge and centerline positions.
    The 2-D centerlines are used, along with the view geometry, to
    determine corresponding 3-D centerlines. Points in all the views are
    matched using the epipolar constraints and the criteria that minimize
    projection errors in all the views. Densitometric measurements of
    vessels are performed in each view. By averaging these measurements
    across the image, a density-length conversion factor k is determined.
    Absolute vessel lumen areas are computed by weighted combinations of
    inclination angle corrected densitometric areas which are
    independently obtained from all views.

    The study of reconstruction accuracy and precision is difficult
    because the true positions and dimensions are normally unknown. A
    series of experiments are designed and performed including computer
    simulations, aluminum, wire phantom studies, pig coronary artery cast
    studies, and a clinical study. The phantoms used in these studies
    are constructed so that at least one parameter under investigation is
    precisely known or is carefully measured. For the clinical study,
    the reconstruction is compared with a clinically used manual method.
    It is demonstrated that subpixel accuracy can be obtained in
    reconstructing vessel lumen areas and pixel accuracy can be obtained
    in reconstructing vessel centerline positions using two or three
    views.

    AN University Microfilms Order Number ADG91-12421.
    AU FISHER, DAVID JUDE.
    TI AUTOMATIC TRACKING OF CARDIAC WALL MOTION USING MAGNETIC RESONANCE
    MARKERS.
    IN The University of Iowa Ph.D. 1990, 220 pages.
    SO DAI V51(12), SecB, pp5993.
    DE Engineering, Biomedical.
    Engineering, Electronics and Electrical. Computer Science.
    AB Substantial experimental and clinical research has been devoted to
    the development and evaluation of quantitative methods for assessment
    of regional ventricular wall motion. The critical step of all such
    methods is the identification of corresponding points in the
    myocardium at end diastole and end systole. Although seldom
    explicitly acknowledged, this step is accomplished by assuming some
    model for cardiac contraction. Most such models have little
    physiological basis and often perform poorly, particularly in
    abnormal patients. A factor that has impeded the development of more
    appropriate models has been the inability to directly test them.
    Recently, magnetic resonance (MR) imaging techniques have been
    developed that provide the capability to directly image motion by
    spatially modulating the degree of magnetization prior to image
    acquisition. Nonselective radio-frequency pulses may be used to
    produce a two-dimensional grid of saturation bands within the heart
    wall. The resulting tagged magnetic resonance images contain dark
    markers that move with the myocardium. In this project, a complete
    wall motion analysis method has been developed that uses localized
    spatial displacement information obtained from a series of tagged
    images acquired at different phases in the cardiac cycle. A cine MR
    pulse sequence that incorporates the SPAMM MR tagging technique was
    designed to acquire the image data. An automatic MR marker detection
    and tracking algorithm was developed and used to measure myocardial
    wall motion. This computer-based image processing technique greatly
    reduced analysis time and improved accuracy as compared to manual
    analysis methods. The cine MR tagging technique and automated
    detection and tracking software was validated using a specially
    designed rotating phantom doped to match the MR properties of the
    heart. The technique was found to be accurate within several pixels
    over a wide range of velocities.

    Regional wall motion of the left ventricle was represented by a
    two-dimensional vector field of MR marker displacements. This data
    was used to assess the performance of both a conventional model of
    cardiac contraction and a new physiologically based model. The use
    of noninvasive myocardial markers shows great promise in future
    quantitative wall motion analysis methods.

    AN University Microfilms Order Number ADG13-41882.
    AU AL-SALEH, RAED IBRAHEEM.
    TI GEOMETRIC SOLID MODEL GENERATION FROM COMPUTED TOMOGRAPHY SCANS.
    IN University of Louisville M.S. 1990, 100 pages.
    SO MAI V29(02) pp312.
    DE Engineering, Mechanical. Computer Science. Engineering, Biomedical.
    AB Computerized tomography, is an imaging technique which can generate
    three-dimensional images of natural or irregular objects from a set
    of two-dimensional cross-sections. However it does not provide
    prototype models for engineering analysis. In order to develop such
    models, computer aided engineering software must be utilized. Such
    software can be made to generate three-dimensional models from a set
    of two-dimensional profiles. However, the two-dimensional computed
    tomography slices must be processed and manipulated to enable the
    engineering software to create the solid model.

    In this study, two programs, written in Pascal, have been developed
    to extract contours from CT images and to import these contours to
    I-DEAS, an engineering software package. A solid object is then
    generated from the surfaces defined by the imported contours. This
    study is the first step in developing automated techniques for
    creating solid models, which can then be later used in finite element
    analysis on the object.

    (Cross-posted from the AIMED Digest by authority of its moderator)
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