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 Ying^1,3

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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