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Abstract #0234

A nonlinear model for DTI reconstruction with locally low-rank regularization

Yuxin Hu1, Qiyuan Tian2, Grant Yang1, Jennifer A McNab3, Bruce Daniel3,4, and Brian Hargreaves1,3,4

1Department of Electrical Engineering, Stanford University, Stanford, CA, United States, 2Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, 3Department of Radiology, Stanford University, Stanford, CA, United States, 4Department of Bioengineering, Stanford University, Stanford, CA, United States

We developed a nonlinear model with simultaneous phase and magnitude updates for iterative multi-shot DWI reconstruction. In addition, locally low-rank regularization along the diffusion encoding direction was included in the proposed model to utilize angular correlation for DTI reconstruction. In-vivo high-resolution and high b-value images have been acquired to validate the proposed method and the proposed method significantly reduces image noise.

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