Abstract #2168

Physics-guided self-supervised learning for retrospective T1 and T2 mapping from conventional weighted brain MRI

Shihan Qiu^1,2, Anthony G. Christodoulou^1,2, Pascal Sati^1,3, Yibin Xie¹, and Debiao Li^1,2

¹Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ²Department of Bioengineering, UCLA, Los Angeles, CA, United States, ³Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, United States

Synopsis

Keywords: Quantitative Imaging, RelaxometryQuantitative MRI directly measures tissue physical parameters, but has limited clinical adoption due to additional scan time and specialized sequence requirements. Supervised deep learning methods were developed to estimate relaxation maps from conventional weighted images. However, paired weighted images and quantitative maps required for training are hard to obtain. In this work, a physics-guided self-supervised learning approach was developed to estimate T1 and T2 maps from conventional weighted images. Using the Bloch equations to decode the estimated maps back to weighted images and enforcing similarity in the image space, the approach realized label-free training and provided maps comparable to references.

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.