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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.