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Learning a Global Descriptor of Cardiac Motion from a Large Cohort of 1000+ Normal Subjects Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Prediction of Clinical Information from Cardiac MRI Using Manifold Learning

Authors : Haiyan Wang, Wenzhe Shi, Wenjia Bai, Antonio M. Simoes Monteiro de Marvao, Timothy J. W. Dawes, Declan P. O’Regan, Philip Edwards, Stuart Cook, Daniel Rueckert

Published in: Functional Imaging and Modeling of the Heart

Publisher: Springer International Publishing

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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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previous chapter Principal Component Analysis for the Classification of Cardiac Motion Abnormalities Based on Echocardiographic Strain and Strain Rate Imaging
next chapter Revealing Differences in Anatomical Remodelling of the Systemic Right Ventricle
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Metadata
Title
Prediction of Clinical Information from Cardiac MRI Using Manifold Learning
Authors
Haiyan Wang
Wenzhe Shi
Wenjia Bai
Antonio M. Simoes Monteiro de Marvao
Timothy J. W. Dawes
Declan P. O’Regan
Philip Edwards
Stuart Cook
Daniel Rueckert
Copyright Year
2015
Book
Functional Imaging and Modeling of the Heart

Print ISBN: 978-3-319-20308-9

Electronic ISBN: 978-3-319-20309-6

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-20309-6_11

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