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Title
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Partial discreteness : a novel prior for magnetic resonance image reconstruction
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Author
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Abstract
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| An important factor influencing the quality of magnetic resonance (MR) images is the reconstruction method that is employed, and specifically, the type of prior knowledge that is exploited during reconstruction. In this work, we introduce a new type of prior knowledge, partial discreteness, where a small number of regions in the image are assumed to be homogeneous and can be well represented by a constant magnitude. In particular, we mathematically formalize the partial discreteness property based on a Gaussian Mixture Model (GMM) and derive a partial discreteness image representation that characterizes the salient features of partially discrete images: a constant intensity in homogeneous areas and texture in heterogeneous areas. The partial discreteness representation is then used to construct a novel prior dedicated to the reconstruction of partially discrete MR images. The strength of the proposed prior is demonstrated on various simulated and real k-space data-based experiments with partially discrete images. Results demonstrate that PD performs competitively with state-of-the-art reconstruction methods, being flexible and easy to implement. |
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Language
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English
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Source (journal)
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IEEE transactions on medical imaging / Institute of Electrical and Electronics Engineers [New York, N.Y.] - New York, N.Y.
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Publication
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New York, N.Y.
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2017
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ISSN
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0278-0062
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DOI
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10.1109/TMI.2016.2645122
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Volume/pages
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36
:5
(2017)
, p. 1041-1053
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ISI
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000400869700001
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Pubmed ID
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28026759
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Full text (Publisher's DOI)
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Full text (open access)
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