NDLI: Longitudinal strain estimation in incompressible cylindrical tissues from magnetic resonance imaging

Content Provider	IEEE Xplore Digital Library
Author	Qi Wei Pai, D.K.
Copyright Year	2009
Description	Author affiliation: Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC V6T1Z4, Canada and Department of Computer Science, Rutgers University, 110 Frenlinghuysen Road, Piscataway, NJ 08854, USA (Qi Wei) \|\| Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC V6T1Z4, Canada (Pai, D.K.)
Abstract	In this paper, we present a simple approach for estimating the average longitudinal strains from models reconstructed from medical images. It can be used for many incompressible generalized cylindrical tissues, such as tendons, ligaments, and fusiform muscles; the major deformation directions of these soft tissues are along the longitudinal axes. The method is especially useful when pre- and post-deformation tissue correspondences are difficult to establish directly from images for various reasons, such as insufficient image resolution, homogenous image intensity, and noise. Incompressibility, which is accepted as a good approximation for soft tissues, is exploited as a constraint on the tissue deformation. Experiments with Magnetic Resonance Imaging (MRI) of tissue phantoms and computer simulations show that the method is accurate and practical even in the presence of noise. Finally, we demonstrate the usefulness of our approach on studying extraocular muscle deformation.
Starting Page	7159
Ending Page	7163
File Size	893771
Page Count	5
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5335349
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Magnetic field induced strain Magnetic resonance imaging Muscles Biological tissues Magnetic noise Image reconstruction Biomedical imaging Tendons Ligaments Image resolution
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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