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    Accelerated MR parameter mapping with a union of local subspaces constraint

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    Author
    Mandava, Sagar
    Keerthivasan, Mahesh B.
    Li, Zhitao
    Martin, Diego R.
    Altbach, Maria I.
    Bilgin, Ali
    Affiliation
    Univ Arizona, Dept Elect & Comp Engn
    Univ Arizona, Dept Med Imaging
    Univ Arizona, Dept Biomed Engn
    Issue Date
    2018-12
    Keywords
    multi-contrast
    parameter mapping
    clustering
    sparsity constraint
    union of subspaces constraint
    image reconstruction
    
    Metadata
    Show full item record
    Publisher
    WILEY
    Citation
    Mandava S, Keerthivasan MB, Li Z, Martin DR, Altbach MI, Bilgin A. Accelerated MR parameter mapping with a union of local subspaces constraint. Magn Reson Med. 2018;80:2744–2758. https://doi.org/10.1002/mrm.27344
    Journal
    MAGNETIC RESONANCE IN MEDICINE
    Rights
    © 2018 International Society for Magnetic Resonance in Medicine
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Purpose: A new reconstruction method for multi-contrast imaging and parameter mapping based on a union of local subspaces constraint is presented. Theory: Subspace constrained reconstructions use a predetermined subspace to explicitly constrain the relaxation signals. The choice of subspace size (K) impacts the approximation error vs noise-amplification tradeoff associated with these methods. A different approach is used in the model consistency constraint (MOCCO) framework to leverage the subspace model to enforce a softer penalty. Our proposed method, MOCCO-LS, augments the MOCCO model with a union of local subspaces (LS) approach. The union of local subspaces model is coupled with spatial support constraints and incorporated into the MOCCO framework to regularize the contrast signals in the scene. Methods: The performance of the MOCCO-LS method was evaluated in vivo on T-1 and T-2 mapping of the human brain and with Monte-Carlo simulations and compared against MOCCO and the explicit subspace constrained models. Results: The results demonstrate a clear improvement in the multi-contrast images and parameter maps. We sweep across the model order space (K) to compare the different reconstructions and demonstrate that the reconstructions have different preferential operating points. Experiments on T-2 mapping show that the proposed method yields substantial improvements in performance even when operating at very high acceleration rates. Conclusions: The use of a union of local subspace constraints coupled with a sparsity promoting penalty leads to improved reconstruction quality of multi-contrast images and parameter maps.
    Note
    12 month embargo; published online: 15 July 2018
    ISSN
    07403194
    PubMed ID
    30009531
    DOI
    10.1002/mrm.27344
    Version
    Final accepted manuscript
    Sponsors
    Technology and Research Initiative Fund (TRIF) - Improving Health and Arizona Biomedical Research Commission (ABRC) [ADHS14-082996]
    Additional Links
    http://doi.wiley.com/10.1002/mrm.27344
    ae974a485f413a2113503eed53cd6c53
    10.1002/mrm.27344
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