NDLI: Accurate modeling of tagged CMR 3D image appearance characteristics to improve cardiac cycle strain estimation

Content Provider	IEEE Xplore Digital Library
Author	Nitzken, M. Beache, G. Elnakib, A. Khalifa, F. Gimel'farb, G. El-Baz, A.
Copyright Year	2012
Description	Author affiliation: Department of Computer Science, University of Auckland, New Zealand (Gimel'farb, G.) \|\| BioImaging Laboratory, Bioengineering Department, University of Louisville, KY, USA (Nitzken, M.; Elnakib, A.; Khalifa, F.; El-Baz, A.) \|\| Radiology Department, School of Medicine, University of Louisville, KY, USA (Beache, G.)
Abstract	To reduce noise within a tag line, unsharpen the tag edges in spatial domain, and amplify the tag-to-background contrast, a 3D energy minimization framework for the enhancement of tagged Cardiac Magnetic Resonance (CMR) image sequences, based on learning first- and second-order visual appearance models, is proposed. The first-order appearance modeling uses adaptive Linear Combinations of Discrete Gaussians (LCDG) to accurately approximate the empirical marginal probability distribution of CMR signals for a given sequence, and separates tag and background submodels. It is also used to classify the tag lines and the background. The second-order model considers image sequences as samples of a translation- and rotation-invariant 3D Markov-Gibbs Random Field (MGRF) with multiple pairwise voxel interactions. A 3D energy function for this model is built by using the analytical estimation of the spatio-temporal geometry and Gibbs potentials of interaction. To improve the strain estimation, by enhancing the tag and background homogeneity and contrast, the given sequence is adjusted using comparisons to the energy minimizer. Special 3D geometric phantoms, motivated by statistical analysis of the tagged CMR data, have been designed to validate the accuracy of our approach. Experiments with the phantoms and eight real data sets have confirmed the high accuracy of the functional parameters that are estimated for the enhanced tagged sequences when using popular spectral techniques, such as spectral Harmonic Phase (HARP).
Starting Page	521
Ending Page	524
File Size	941093
Page Count	4
File Format	PDF
ISBN	9781467325349
ISSN	15224880
e-ISBN	9781467325332
e-ISBN	9781467325325
DOI	10.1109/ICIP.2012.6466911
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Strain Phantoms Tracking Imaging phantoms Noise measurement Noise Harmonic analysis appearance model Tagged CMR image tag line linear combination of Gaussians Markov random field
Content Type	Text
Resource Type	Article

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