Abstract #2431

Multi-Contrast MRI Reconstruction from Single-Channel Uniformly Undersampled Data via Deep Learning

Christopher Man^1,2, Linfang Xiao^1,2, Yilong Liu^1,2, Vick Lau^1,2, Zheyuan Yi^1,2,3, Alex T. L. Leong^1,2, and Ed X. Wu^1,2

¹Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China, ²Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China, ³Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China

This study presents a deep learning based reconstruction for multi-contrast MR data with orthogonal undersampling directions across different contrasts. It enables exploiting the rich structural similarities from multiple contrasts as well as the incoherency arose from complementary sampling. The results show that the proposed method can achieve robust reconstruction for single-channel multi-contrast MR data at R=4.

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.