Abstract #1038

Experimental Validation of Subject-Specific Local SAR Assessment by Deep Learning

E.F. Meliadò^1,2, A.J.E Raaijmakers^1,3, B.R. Steensma¹, A. Sbrizzi¹, P.R. Luijten¹, and C.A.T. van den Berg¹

¹Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, ²MR Code BV, Zaltbommel, Netherlands, ³Biomedical Image Analysis, Eindhoven University of Technology, Eindhoven, Netherlands

Subject-specific local SAR assessment is one of the outstanding challenges of ultra-high field MRI. In this work, we present experimental, in-vivo results on four healthy subjects of a novel deep learning approach which provides online subject-specific local SAR distributions based on measured B₁⁺ maps. Results are validated by creating a subject-specific model of each subject and calculate a reference local SAR distribution off-line by FDTD simulations. The results show that a Convolutional Neural Network (CNN) trained with synthetic MR data enables accurate subject-specific local SAR prediction during an MRI examination.

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.