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Abstract #4075

Optimization of Radial Diffusion Imaging on a 0.16T Low-Field MRI

Xuhao Dong1, jinxuan Lyu1, Bin Zhang1, Qiao Liu1, Rongqin Hou2, Chunxia Chen2, Haifeng Wang1, Dong Liang1, and Yihang Zhou1
1Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, ShenZhen, China, 2Hebei Huiren Medical Equipment Technology Co., Ltd., Baoding, China

Synopsis

Keywords: Diffusion Acquisition, Diffusion Acquisition, Low-field MRI, L+S decomposition, diffusion reconstruction

Motivation: Low-field MRI systems face challenges in diffusion imaging due to limited gradient strength and switching rates. Overcoming these limits is essential to broaden low-field MRI applications.

Goal(s): Develop an advanced, gradient-efficient diffusion imaging sequence and reconstruction algorithm for low-field MRI, aimed at minimizing artifacts and improving image clarity under hardware limitations.

Approach: A radial k-space acquisition sequence with Gaussian-based gridding and WOA-optimized iterative L+S decomposition was designed to balance energy distribution and enhance image clarity.

Results: This work overcamed limitations of gradient strength and switching rates, enabling diffusion imaging in low-field MRI and demonstrating clear human brain imaging on a 0.16T resistive system.

Impact: This work could expand low-field MRI applications, facilitating access to reliable diffusion imaging in primary care settings. Further validation across varied populations may support its broader adoption in resource-limited healthcare environments.

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