Meeting Banner
Abstract #2458

Accelerating Parameter Mapping with a Locally Low Rank Constraint

Tao Zhang1, John M. Pauly1, Ives R. Levesque2

1Electrical Engineering, Stanford University, Stanford, CA, United States; 2Radiology, Stanford University, Stanford, CA, United States

Parameter mapping can provide intrinsic tissue information to detect pathological changes. Previous studies have shown that compressed sensing with a low-rank constraint can be used to accelerate the lengthy scan time required in parameter mapping. In this work, a locally low rank constraint is applied to parameter mapping. As examples, inversion recovery T1 mapping and multiple-echo T2 mapping are studied. Reconstruction with a locally low rank constraint can provide better accuracy and precision than that with a global low rank constraint.

Keywords

accelerate accelerated accelerating acceleration accuracy accurate achieve acquisition added additional applied approximately becomes best better block blocks bottom boundaries boundary brain called cardiac characterized chosen close column compartment compressed constraint context convex cortex curve dataset deficient define detect dimension distributed distribution done doped dynamic echoes electrical engineering error especially estimation even examples experiment formulated full fully function generally geometric geometrically global gray improve intrinsic inversion inversions john known lengthy limited local locally mapping maps matrix mixed near norm normalized notably nuclear objective onto operator original parametric partial partitioned pathological phantom pixel precision problem produces projection promoted proposed radiology rank recently reconstructed reconstruction recovery reformats reformatted representing retrospectively root sampled sampling scanned scanner scenarios sensing separability series sets smaller smallest smooth solve sonata space spacing sparsity spin square studied subject tissue transform usually ventricles water wavelet white yields