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  An eigenvalue approach for the automatic scaling of unknowns in model-based reconstructions: Application to real-time phase-contrast flow MRI.

Tan, Z., Hohage, T., Kalentev, O., Joseph, A. A., Wang, X., Voit, D., et al. (2017). An eigenvalue approach for the automatic scaling of unknowns in model-based reconstructions: Application to real-time phase-contrast flow MRI. NMR in Biomedicine, 30(12): e3835. doi:10.1002/nbm.3835.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002E-1E2E-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-5203-F
Genre: Journal Article

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 Creators:
Tan, Z.1, Author              
Hohage, T., Author
Kalentev, O.1, Author              
Joseph, A. A.1, Author              
Wang, X.1, Author              
Voit, D.1, Author              
Merboldt, K. D.1, Author              
Frahm, J.1, Author              
Affiliations:
1Biomedical NMR Research GmbH, MPI for biophysical chemistry, Max Planck Society, ou_578634              

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Free keywords: cardiovascular blood flow; flow quantification; model-based reconstruction; nonlinear inverse reconstruction; real-time MRI; scaling of unknowns
 Abstract: The purpose of this work is to develop an automatic method for the scaling of unknowns in model-based nonlinear inverse reconstructions and to evaluate its application to real-time phase-contrast (RT-PC) flow magnetic resonance imaging (MRI). Model-based MRI reconstructions of parametric maps which describe a physical or physiological function require the solution of a nonlinear inverse problem, because the list of unknowns in the extended MRI signal equation comprises multiple functional parameters and all coil sensitivity profiles. Iterative solutions therefore rely on an appropriate scaling of unknowns to numerically balance partial derivatives and regularization terms. The scaling of unknowns emerges as a self-adjoint and positive-definite matrix which is expressible by its maximal eigenvalue and solved by power iterations. The proposed method is applied to RT-PC flow MRI based on highly undersampled acquisitions. Experimental validations include numerical phantoms providing ground truth and a wide range of human studies in the ascending aorta, carotid arteries, deep veins during muscular exercise and cerebrospinal fluid during deep respiration. For RT-PC flow MRI, model-based reconstructions with automatic scaling not only offer velocity maps with high spatiotemporal acuity and much reduced phase noise, but also ensure fast convergence as well as accurate and precise velocities for all conditions tested, i.e. for different velocity ranges, vessel sizes and the simultaneous presence of signals with velocity aliasing. In summary, the proposed automatic scaling of unknowns in model-based MRI reconstructions yields quantitatively reliable velocities for RT-PC flow MRI in various experimental scenarios.

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Language(s): eng - English
 Dates: 2017-09-282017-12
 Publication Status: Published in print
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 Rev. Method: Peer
 Identifiers: DOI: 10.1002/nbm.3835
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Title: NMR in Biomedicine
Source Genre: Journal
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Pages: 11 Volume / Issue: 30 (12) Sequence Number: e3835 Start / End Page: - Identifier: -