Image Processing Reference
In-Depth Information
The existing methods are rather complex visual representations that overwhelm a
considerable portion of the target user group. Future research should address simpli-
fications of the blood flow by either clustering flow or by detecting and emphasizing
relevant features. Existing techniques for flow field analysis may serve as orientation,
but certainly need to be combined with the in-depth knowledge of domain experts
regarding the relevance of certain blood flow characteristics. A better understanding
of specific tasks, decisions and relevant information is necessary to support blood
flow exploration with a guided workflow-based interaction.
Radiologists need to prepare reports where they summarize their findings verbally
including relevant images. A better understanding of such reports may help to better
support reporting, e.g., in case of cardiovascular diseases. While current applications
are strongly focused on measured cardiac flow and simulated cerebral blood flow,
advances in image acquisition will lead to further applications, e.g., where renal or
liver flow is represented.
References
1. American Heart Association: Heart and stroke statistics (2010). http://www.americanheart.org/
statistics
2. Arheden, H.k., Saeed, M., Törnqvist, E., Lund, G., Wendland, M.F., Higgins, C.B., Ståhlberg,
F.: Accuracy of segmented MR velocity mapping to measure small vessel pulsatile flow in a
phantom simulating cardiac motion. J. Magn. Reson. Imaging 13(5), 722-728 (2001)
3. Bernstein, M.A., Ikezaki, Y.: Comparison of phase-difference and complex-difference process-
ing in phase-contrast MR angiography. Magn. Reson. Imaging 1(2), 725-729 (1991). http://
www3.interscience.wiley.com/journal/112146770/abstract
4. Bernstein, M.A., Zhou, X.J., Polzin, J.A., King, K.F., Ganin, A., Pelc, N.J., Glover, G.H.:
Concomitant gradient terms in phase contrast MR: analysis and correction. Magn. Reson.
Med. 39 (2), 300-308 (1998)
5. Cebral, J.R., Castro,M.A., Appanaboyina, S., Putmann, C.M.,Millan, D., Frangi, A.F.: Efficient
pipeline for image-based patient-specific analysis of cebral aneurysmhemodynamics:technique
and sensitivity. IEEE Trans. Med. Imaging 24 (4), 457-467 (2005)
6. Cebral, J.R., Putman, C.M., Alley, M.T., Hope, T., Bammer, R., Calamante, F.: Hemodynamics
in normal cerebral arteries: qualitative comparison of 4d phase-contrast magnetic resonance
and image-based computational fluid dynamics. J. Eng. Math. 64 (4), 367-378 (2009)
7. Chung, A.C.S., Noble, J.A., Summers, P.: Vascular segmentation of phase-contrast magnetic
resonance angiograms based on statistical mixture modeling and local phase coherence. IEEE
Trans. Med. Imaging 23 (12), 1490-1507 (2004)
8. Elmqvist, N., Tudoreanu, M.E., Tsigas, P.: Evaluating motion constraints for 3d wayfinding in
immersive and desktop virtual environments. In: Proceedings of the ACMSIGCHI Conference
on Human Factors in Computing Systems, CHI '08, pp. 1769-1778. ACM, New York, NY,
USA (2008)
9. Everts, M.H., Bekker, H., Roerdink, J.B., Isenberg, T.: Depth-dependent halos: illustrative
rendering of dense line data. IEEE Trans. Vis. Comput. Graph. 15, 1299-1306 (2009). http://
doi.ieeecomputersociety.org/10.1109/TVCG.2009.138
10. Garcke, H.: Preu
er, T., Rumpf, M., Telea, A., Weikard, U., van Wijk, J.: A continuous cluster-
ing method for vector fields. In: Proceedings of the conference on Visualization '00. VIS'00,
pp. 351-358. IEEE Computer Society Press, Los Alamitos, CA, USA (2000)
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