Abstract
The present paper aims at quantifying the evolution of a given motion pattern under cardiac resynchronization therapy (CRT). It builds upon techniques for population-based cardiac motion quantification (statistical atlases, for inter-sequence spatiotemporal alignment and the definition of normal/abnormal motion). Manifold learning is used on spatiotemporal maps of myocardial motion abnormalities to represent a given abnormal pattern and to compare any individual to that pattern. The methodology was applied to 2D echocardiographic sequences in a 4-chamber view from 108 subjects (21 healthy volunteers and 87 CRT candidates) at baseline, with pacing ON, and at 12 months follow-up. Experiments confirmed that recovery of a normal motion pattern is a necessary but not sufficient condition for CRT response.
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Duchateau, N. et al. (2013). Manifold Learning Characterization of Abnormal Myocardial Motion Patterns: Application to CRT-Induced Changes. In: Ourselin, S., Rueckert, D., Smith, N. (eds) Functional Imaging and Modeling of the Heart. FIMH 2013. Lecture Notes in Computer Science, vol 7945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38899-6_53
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DOI: https://doi.org/10.1007/978-3-642-38899-6_53
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