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Accelerated MR parameter mapping with a union of local subspaces constraint.

Sagar Mandava1,2, Mahesh B Keerthivasan1,2, Zhitao Li1,2

  • 1Department of Electrical and Computer Engineering, University of Arizona, Tucson, Arizona.

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A novel reconstruction method, MOCCO-LS, enhances multi-contrast imaging and parameter mapping. This approach improves image quality and performance, especially at high acceleration rates for T1 and T2 mapping.

Keywords:
clusteringimage reconstructionmulti-contrastparameter mappingsparsity constraintunion of subspaces constraint

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Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Computational Biology

Background:

  • Subspace constrained reconstructions rely on predetermined subspaces, facing tradeoffs between approximation error and noise amplification.
  • The model consistency constraint (MOCCO) framework offers a softer penalty by leveraging subspace models.
  • Existing methods' performance is sensitive to subspace size selection.

Purpose of the Study:

  • To introduce a new reconstruction method, MOCCO-LS, for multi-contrast imaging and parameter mapping.
  • To improve the quality of reconstructed images and parameter maps using a union of local subspaces.
  • To enhance the performance of medical imaging reconstructions, particularly under accelerated acquisition conditions.

Main Methods:

  • The MOCCO-LS method integrates a union of local subspaces (LS) approach into the MOCCO framework.
  • Spatial support constraints are coupled with the LS model for regularization of contrast signals.
  • In vivo T1 and T2 mapping of the human brain were used for evaluation, alongside Monte-Carlo simulations.

Main Results:

  • MOCCO-LS demonstrated clear improvements in multi-contrast images and parameter maps compared to MOCCO and explicit subspace methods.
  • Reconstruction quality showed significant enhancements, particularly for T2 mapping at high acceleration rates.
  • Analysis across different model orders revealed distinct optimal operating points for various reconstructions.

Conclusions:

  • The combination of union of local subspace constraints and sparsity promotion improves reconstruction quality.
  • MOCCO-LS offers superior performance for multi-contrast imaging and parameter mapping.
  • The method shows promise for accelerated MRI acquisition, enhancing diagnostic capabilities.