Tao Zhang1, John M Pauly, Ives R Levesque. 1. Department of Electrical Engineering, Magnetic Resonance Systems Research Laboratory, Stanford University, Stanford, California, USA.
Abstract
PURPOSE: To accelerate MR parameter mapping using a locally low rank (LLR) constraint, and the combination of parallel imaging and the LLR constraint. THEORY AND METHODS: An LLR method is developed for MR parameter mapping and compared with a globally low rank method in a multiecho spin-echo T2 mapping experiment. For acquisition with coil arrays, a combined LLR and parallel imaging method is proposed. The proposed method is evaluated in a variable flip angle T1 mapping experiment and compared with the LLR method and parallel imaging alone. RESULTS: In the multiecho spin-echo T2 mapping experiment, the LLR method is more accurate than the globally low rank method for acceleration factors 2 and 3, especially for tissues with high T2 values. Variable flip angle T1 mapping is achieved by acquiring datasets with 10 flip angles, each dataset accelerated by a factor of 6, and reconstructed by the proposed method with a small normalized root mean square error of 0.025. CONCLUSIONS: The LLR method is likely superior to the globally low rank method for MR parameter mapping. The proposed combined LLR and parallel imaging method has better performance than the two methods alone, especially with highly accelerated acquisition.
PURPOSE: To accelerate MR parameter mapping using a locally low rank (LLR) constraint, and the combination of parallel imaging and the LLR constraint. THEORY AND METHODS: An LLR method is developed for MR parameter mapping and compared with a globally low rank method in a multiecho spin-echo T2 mapping experiment. For acquisition with coil arrays, a combined LLR and parallel imaging method is proposed. The proposed method is evaluated in a variable flip angle T1 mapping experiment and compared with the LLR method and parallel imaging alone. RESULTS: In the multiecho spin-echo T2 mapping experiment, the LLR method is more accurate than the globally low rank method for acceleration factors 2 and 3, especially for tissues with high T2 values. Variable flip angle T1 mapping is achieved by acquiring datasets with 10 flip angles, each dataset accelerated by a factor of 6, and reconstructed by the proposed method with a small normalized root mean square error of 0.025. CONCLUSIONS: The LLR method is likely superior to the globally low rank method for MR parameter mapping. The proposed combined LLR and parallel imaging method has better performance than the two methods alone, especially with highly accelerated acquisition.
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