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dc.contributor.authorMandava, Sagar
dc.contributor.authorKeerthivasan, Mahesh B.
dc.contributor.authorLi, Zhitao
dc.contributor.authorMartin, Diego R.
dc.contributor.authorAltbach, Maria I.
dc.contributor.authorBilgin, Ali
dc.date.accessioned2019-02-22T22:04:11Z
dc.date.available2019-02-22T22:04:11Z
dc.date.issued2018-12
dc.identifier.citationMandava 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.27344en_US
dc.identifier.issn07403194
dc.identifier.pmid30009531
dc.identifier.doi10.1002/mrm.27344
dc.identifier.urihttp://hdl.handle.net/10150/631739
dc.description.abstractPurpose: 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.en_US
dc.description.sponsorshipTechnology and Research Initiative Fund (TRIF) - Improving Health and Arizona Biomedical Research Commission (ABRC) [ADHS14-082996]en_US
dc.language.isoenen_US
dc.publisherWILEYen_US
dc.relation.urlhttp://doi.wiley.com/10.1002/mrm.27344en_US
dc.rights© 2018 International Society for Magnetic Resonance in Medicineen_US
dc.subjectmulti-contrasten_US
dc.subjectparameter mappingen_US
dc.subjectclusteringen_US
dc.subjectsparsity constrainten_US
dc.subjectunion of subspaces constrainten_US
dc.subjectimage reconstructionen_US
dc.titleAccelerated MR parameter mapping with a union of local subspaces constrainten_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Dept Elect & Comp Engnen_US
dc.contributor.departmentUniv Arizona, Dept Med Imagingen_US
dc.contributor.departmentUniv Arizona, Dept Biomed Engnen_US
dc.identifier.journalMAGNETIC RESONANCE IN MEDICINEen_US
dc.description.note12 month embargo; published online: 15 July 2018en_US
dc.description.collectioninformationThis 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.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleMagnetic Resonance in Medicine
dc.source.volume80
dc.source.issue6
dc.source.beginpage2744
dc.source.endpage2758


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