Model-Based Image Reconstruction for Dynamic Cardiac Perfusion MRI from Sparse Data

The paper presents a novel approach for dynamic magnetic resonance imaging (MRI) cardiac perfusion image reconstruction from sparse k-space data. It formulates the reconstruction problem in an inverse-methods setting. Relevant prior information is incorporated via a parametric model for the perfusio...

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Bibliographic Details
Published in:2006 International Conference of the IEEE Engineering in Medicine and Biology Society 2006, p.936-941
Main Authors: Awate, S.P., DiBella, E.V.R., Tasdizen, T., Whitaker, R.T.
Format: Article
Language:English
Subjects:
Heart beat
Image reconstruction
Kinetic theory
Magnetic resonance imaging
Myocardium
Neoplasms
Parametric statistics
Sampling methods
Signal resolution
Spatial resolution
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Summary:The paper presents a novel approach for dynamic magnetic resonance imaging (MRI) cardiac perfusion image reconstruction from sparse k-space data. It formulates the reconstruction problem in an inverse-methods setting. Relevant prior information is incorporated via a parametric model for the perfusion process. This wealth of prior information empowers the proposed method to give high-quality reconstructions from very sparse k-space data. The paper presents reconstruction results using both Cartesian and radial sampling strategies using data simulated from a real acquisition. The proposed method produces high-quality reconstructions using 14% of the k-space data. The model-based approach can potentially greatly benefit cardiac myocardial perfusion studies as well as other dynamic contrast-enhanced MRI applications including tumor imaging
ISSN:1557-170X
DOI:10.1109/IEMBS.2006.260363