Open AccessImproving in vivo human cerebral cortical surface reconstruction using data-driven super-resolution
Author(s) -
Qiyuan Tian,
Berkin Bilgic̦,
Qiuyun Fan,
Cha Ngamsombat,
Natalia Zaretskaya,
Nina E Fultz,
Ned A. Ohringer,
Akshay Chaudhari,
Yuxin Hu,
Thomas Witzel,
Kawin Setsompop,
Jon̈athan R. Polimeni,
Susie Y. Huang
Publication year - 2020
Publication title -
cerebral cortex
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.694
H-Index - 250
eISSN - 1460-2199
pISSN - 1047-3211
DOI - 10.1093/cercor/bhaa237
Subject(s) - image resolution , millimeter , resolution (logic) , computer science , isotropy , artificial intelligence , convolutional neural network , magnetic resonance imaging , matching (statistics) , computer vision , pattern recognition (psychology) , nuclear magnetic resonance , optics , physics , mathematics , statistics , medicine , radiology
Accurate and automated reconstruction of the in vivo human cerebral cortical surface from anatomical magnetic resonance (MR) images facilitates the quantitative analysis of cortical structure. Anatomical MR images with sub-millimeter isotropic spatial resolution improve the accuracy of cortical surface and thickness estimation compared to the standard 1-millimeter isotropic resolution. Nonetheless, sub-millimeter resolution acquisitions require averaging multiple repetitions to achieve sufficient signal-to-noise ratio and are therefore long and potentially vulnerable to subject motion. We address this challenge by synthesizing sub-millimeter resolution images from standard 1-millimeter isotropic resolution images using a data-driven supervised machine learning-based super-resolution approach achieved via a deep convolutional neural network. We systematically characterize our approach using a large-scale simulated dataset and demonstrate its efficacy in empirical data. The super-resolution data provide improved cortical surfaces similar to those obtained from native sub-millimeter resolution data. The whole-brain mean absolute discrepancy in cortical surface positioning and thickness estimation is below 100 μm at the single-subject level and below 50 μm at the group level for the simulated data, and below 200 μm at the single-subject level and below 100 μm at the group level for the empirical data, making the accuracy of cortical surfaces derived from super-resolution sufficient for most applications.