Accelerated MR parameter mapping with a union of local subspaces constraint
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MOCCOLS_MRM_paper.pdf
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Final Accepted Manuscript
Affiliation
Univ Arizona, Dept Elect & Comp EngnUniv Arizona, Dept Med Imaging
Univ Arizona, Dept Biomed Engn
Issue Date
2018-12Keywords
multi-contrastparameter mapping
clustering
sparsity constraint
union of subspaces constraint
image reconstruction
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WILEYCitation
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.27344Journal
MAGNETIC RESONANCE IN MEDICINERights
© 2018 International Society for Magnetic Resonance in MedicineCollection 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 2018ISSN
07403194PubMed ID
30009531Version
Final accepted manuscriptSponsors
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.27344ae974a485f413a2113503eed53cd6c53
10.1002/mrm.27344
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