Abstract
An important assessment in patients with ischemic heart disease is whether myocardial contractility may improve after treatment. The prediction of myocardial contractility improvement is generally performed under physical or pharmalogical stress conditions. In this paper, we present a technique to build a statistical model of healthy myocardial contraction using independent component analysis. The model is used to detect regions with abnormal contraction in patients both during rest and stress.
This work is supported by the Dutch Science Foundation (NWO), under an innovational research incentive grant, vernieuwingsimpuls 2001. This work was also supported in part by a grant from Fundación MAPFRE Medicina and grants TIC2002-04495-C02 from the MEyC, and FIS-PI040676 and G03/185 from ISCIII. The work of A.F. Frangi was supported in part by a Ramon y Cajal Research Fellowship from the Spanish MEyC.
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Suinesiaputra, A., Frangi, A.F., Lamb, H.J., Reiber, J.H.C., Lelieveldt, B.P.F. (2005). Automatic Prediction of Myocardial Contractility Improvement in Stress MRI Using Shape Morphometrics with Independent Component Analysis. In: Christensen, G.E., Sonka, M. (eds) Information Processing in Medical Imaging. IPMI 2005. Lecture Notes in Computer Science, vol 3565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11505730_27
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DOI: https://doi.org/10.1007/11505730_27
Publisher Name: Springer, Berlin, Heidelberg
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