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Abstract #0679

Joint 3D parameter mapping and motion correction using a kernel low rank method with offline training

Chaoyi Zhang1, JeeHun Kim2, Hongyu Li1, Peizhou Huang3, Ruiying Liu1, Dong Liang4, Xiaoliang Zhang3, Xiaojuan Li2, and Leslie Ying1,3
1Electrical Engineering, University at Buffalo, SUNY, Buffalo, NY, United States, 2Biomedical Engineering, Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, OH, United States, 3Biomedical Engineering, University at Buffalo, SUNY, Buffalo, NY, United States, 4Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen institutes of Advanced Technology, Shenzhen, China

Magnetic resonance parameter mapping (e.g. T1, T2, T2* and T1ρ) has shown potential in quantitative assessment while the clinical applications are limited by long acquisition time especially in 3D acquisition. In our previous work, we use single-exponential model to generate off-line single-exponential training data instead of low resolution training data, which reduced the reconstruction time. In clinical use, when motion is introduced in acquisition, single-exponential model is not satisfied and the reconstruction may fail. With this motivation, this abstract alternatively reconstruct the images and correct motion in 3D parameter mapping.

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