Abstract #0234

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

Yuxin Hu¹, Qiyuan Tian², Grant Yang¹, Jennifer A McNab³, Bruce Daniel^3,4, and Brian Hargreaves^1,3,4

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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