Abstract #0834

SuperSurfer: Cortical surface reconstruction using super-resolution anatomical MR images synthesized by deep learning

Qiyuan Tian¹, Berkin Bilgic^1,2, Qiuyun Fan¹, Chanon Ngamsombat¹, Akshay S Chaudhari³, Ned A Ohringer¹, Yuxin Hu³, Thomas Witzel¹, Kawin Setsompop^1,2, Jonathan R Polimeni^1,2, and Susie Y Huang^1,2

¹Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States, ²Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, ³Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, CA, United States

Recent studies have shown that anatomical MR images with sub-millimeter resolution can improve the accuracy of cortical surface and thickness estimation compared to the standard 1-millimeter isotropic resolution. Here we propose a new method, entitled SuperSurfer, that synthesizes sub-millimeter anatomical MR images from standard 1-mm isotropic anatomical images using a convolutional neural network-based super-resolution approach intended for improved cortical surface reconstruction. We quantified the displacement of the reconstructed surfaces and difference in cortical thickness derived from the super-resolution and standard-resolution data and demonstrated that SuperSurfer provides improved cortical surfaces that are similar to those obtained from native sub-millimeter resolution data.

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.