Abstract
Cardiac MR imaging contains rich information that can be used to investigate the anatomy and function of the heart. In this paper, we demonstrate that it is possible to learn anatomical and functional information from cardiac MR imaging without explicit segmentation in order to predict clinical variables such as blood pressure with high accuracy. To learn the anatomical variations, we build manifolds of different time points across different subjects. In addition, we investigate two different approaches to incorporate motion information into a manifold, and compare these manifolds to a manifold learned from a single time point. Combining both inter- and intra-subject variation, we are able to construct accurate and reliable classifiers to predict clinical variables. Our proposed method does not require any explicit image segmentation and motion estimation and is able to predict clinical variables with good accuracy.
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Wang, H. et al. (2015). Prediction of Clinical Information from Cardiac MRI Using Manifold Learning. In: van Assen, H., Bovendeerd, P., Delhaas, T. (eds) Functional Imaging and Modeling of the Heart. FIMH 2015. Lecture Notes in Computer Science(), vol 9126. Springer, Cham. https://doi.org/10.1007/978-3-319-20309-6_11
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DOI: https://doi.org/10.1007/978-3-319-20309-6_11
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