NDLI: Improved Signal-to-Noise Ratio in Parallel Coronary Artery Magnetic Resonance Angiography using Graph Cuts based Bayesian Reconstruction

Content Provider	IEEE Xplore Digital Library
Author	Singh, G. Nguyen, T. Kressler, B. Spincemaille, P. Raj, A. Zabih, R. Wang, Y.
Copyright Year	2006
Description	Author affiliation: Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY (Singh, G.)
Abstract	High resolution 3D coronary artery MR angiography is time-consuming and can benefit from accelerated data acquisition provided by parallel imaging techniques without sacrificing spatial resolution. Currently, popular maximum likelihood based parallel imaging reconstruction techniques such as the SENSE algorithm offer this advantage at the cost of reduced signal-to-noise ratio (SNR). Maximum a posteriori (MAP) reconstruction techniques that incorporate globally smooth priors have been developed to recover this SNR loss, but they tend to blur sharp edges in the target image. The objective of this study is to demonstrate the feasibility of employing edge-preserving Markov random field priors in a MAP reconstruction framework, which can be solved efficiently using a graph cuts based optimization algorithm. The preliminary human study shows that our reconstruction provides significantly better SNR than the SENSE reconstruction performed by a commercially available scanner for navigator gated steady state free precession 3D coronary magnetic resonance angiography images (n = 4)
Sponsorship	IEEE EMB
Starting Page	703
Ending Page	706
File Size	166498
Page Count	4
File Format	PDF
ISBN	1424400325
ISSN	1557170X
DOI	10.1109/IEMBS.2006.260300
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Signal to noise ratio Arteries Magnetic resonance Angiography Bayesian methods Image reconstruction Spatial resolution High-resolution imaging Magnetic resonance imaging Image resolution
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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