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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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