• DocumentCode
    1818229
  • Title

    Visual 4D MRI blood flow analysis with line predicates

  • Author

    Born, Silvia ; Pfeifle, Matthias ; Markl, Michael ; Scheuermann, Gerik

  • Author_Institution
    Univ. Leipzig, Leipzig, Germany
  • fYear
    2012
  • fDate
    Feb. 28 2012-March 2 2012
  • Firstpage
    105
  • Lastpage
    112
  • Abstract
    4D MRI is an in vivo flow imaging modality which has the potential to significantly enhance diagnostics and therapy of cardiovascular diseases. However, current analysis methods demand too much time and expert knowledge in order to apply 4D MRI in the clinics or larger clinical studies. One missing piece are methods allowing to gain a quick overview of the flow data´s main properties. We present a line predicate approach that sorts precalculated integral lines, which capture the complete flow dynamics, into bundles with similar properties. We introduce several streamline and pathline predicates that allow to structure the flow according to various features useful for blood flow analysis, such as, e.g., velocity distribution, vortices, and flow paths. The user can combine these predicates flexibly and by that create flow structures that help to gain overview and carve out special features of the current dataset. We show the usefulness of our approach by means of a detailed discussion of 4D MRI datasets of healthy and pathological aortas.
  • Keywords
    biomedical MRI; cardiovascular system; diseases; haemodynamics; medical image processing; patient treatment; blood flow analysis; cardiovascular disease diagnostics; cardiovascular disease therapy; flow dynamics; flow structures; healthy aortas; in vivo flow imaging modality; line predicates; pathological aortas; visual 4D MRI blood flow analysis; Aneurysm; Blood; Magnetic resonance imaging; Morphology; Three dimensional displays; Vectors; Visualization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Visualization Symposium (PacificVis), 2012 IEEE Pacific
  • Conference_Location
    Songdo
  • ISSN
    2165-8765
  • Print_ISBN
    978-1-4673-0863-2
  • Electronic_ISBN
    2165-8765
  • Type

    conf

  • DOI
    10.1109/PacificVis.2012.6183580
  • Filename
    6183580