Dynamic compressive magnetic resonance imaging using a Gaussian scale mixtures model

Dynamic magnetic resonance imaging (MRI) is commonly used to observe dynamic physiological changes in tissue or to study organs with mobile structures such as the heart. In order to accurately capture spatiotemporal changes, it is desirable to have dynamic images with high temporal resolution in add...

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Bibliographic Details
Published in2011 18th IEEE International Conference on Image Processing pp. 2293 - 2296
Main Authors Yookyung Kim, Nadar, M. S., Bilgin, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2011
Subjects
Acceleration
Compressed sensing
dynamic MRI
Gaussian scale mixtures
Heuristic algorithms
Image reconstruction
Magnetic resonance imaging
Wavelet transforms
wavelets
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Summary:Dynamic magnetic resonance imaging (MRI) is commonly used to observe dynamic physiological changes in tissue or to study organs with mobile structures such as the heart. In order to accurately capture spatiotemporal changes, it is desirable to have dynamic images with high temporal resolution in addition to high spatial resolution. Due to the nature of data acquisition in current MRI systems, there exists a trade-off between temporal and spatial resolution. In this work, we present two methods for improving the spatiotemporal resolution in dynamic MRI using compressive sampling (CS). Experimental results illustrate that the proposed Bayes least squares-Gaussian scale mixtures (BLS-GSM) model-based CS algorithm compares favorably with other state-of-the-art compressive dynamic MRI techniques.
ISBN:1457713047
9781457713040
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2011.6116097