Accelerated MR parameter mapping with a union of local subspaces constraint
AffiliationUniv Arizona, Dept Elect & Comp Engn
Univ Arizona, Dept Med Imaging
Univ Arizona, Dept Biomed Engn
union of subspaces constraint
MetadataShow full item record
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.27344
JournalMAGNETIC RESONANCE IN MEDICINE
Rights© 2018 International Society for Magnetic Resonance in Medicine
Collection InformationThis 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 firstname.lastname@example.org.
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.
Note12 month embargo; published online: 15 July 2018
VersionFinal accepted manuscript
SponsorsTechnology and Research Initiative Fund (TRIF) - Improving Health and Arizona Biomedical Research Commission (ABRC) [ADHS14-082996]