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Abstract #0834

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

Qiyuan Tian1, Berkin Bilgic1,2, Qiuyun Fan1, Chanon Ngamsombat1, Akshay S Chaudhari3, Ned A Ohringer1, Yuxin Hu3, Thomas Witzel1, Kawin Setsompop1,2, Jonathan R Polimeni1,2, and Susie Y Huang1,2

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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