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.