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.

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