Abstract #0579

Learning-based approach for accelerated IVIM imaging in the placenta

Fan Huang¹, Shi-Ming Wang², Guohui Yan³, Zhihao Wen⁴, Yuhao Liao¹, Yi Zhang¹, Yu Zou³, and Dan Wu¹

¹Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China, ²Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan, ³Department of Radiology,Women's Hospital,School of Medicine, Zhejiang University, Hangzhou, China, ⁴Purple-river software corporation, Shenzhen, China

Q-space learning has shown its potential in accelerating Q-space sampling in diffusion MRI. This study proposed a new deep learning framework to accelerate intravoxel incoherent motion (IVIM) imaging and to estimate IVIM parameters from a small number of b values in the human placenta. The results demonstrated the feasibility of a reduced IVIM protocol using the proposed framework, which may help to accelerate the acquisition and reduce motion for placental IVIM.

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