Abstract #0750

Multishot high-resolution brain diffusion-weighted imaging using phase regularized reconstruction

Yuxin Hu^1,2, Xiaole Wang³, Evan G. Levine^1,2, Qiyuan Tian^1,2, Valentina Taviani⁴, Frank Ong⁵, Shreyas Vasanawala¹, Jennifer A McNab¹, Bruce L. Daniel^1,6, and Brian Hargreaves^1,2,6

¹Department of Radiology, Stanford University, Stanford, CA, United States, ²Department of Electrical Engineering, Stanford University, Stanford, CA, United States, ³Biomedical Engineering, Tsinghua University, Beijing, China, ⁴GE Healthcare, Menlo Park, CA, United States, ⁵Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, United States, ⁶Department of Bioengineering, Stanford University, Stanford, CA, United States

Multishot imaging has been shown to provide high resolution diffusion-weighted images (DWIs) with reduced distortion, however, significant aliasing artifacts and signal cancellation still occur due to the mismatch of the motion-induced phase between different shots. The reconstruction becomes non-convex and intractable to solve when this phase is included into the forward model. The goal of this work is to solve this problem by circumventing the challenging phase estimation step. The brain and breast examples demonstrate that this can be efficiently achieved using a locally low-rank reconstruction approach.

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