Reliability of Deep Learning-based MR Image Reconstruction for Cortical Segmentation, Thickness and Intracortical Myelin Mapping

Motivation: SmartSpeed AI, deep learning-based MR image reconstruction method can be used for scan acceleration, but its clinical applicability for studying brain volumetry and/or cortical myelin has not been investigated. Goal(s): This study was aimed to quantitatively evaluate the reliability for estimates of cortical thickness and myelin estimated from SmartSpeed AI reconstruction. Approach: Segmentation performance was evaluated using Dice coefficient and Hausdorff distance and the reliability of estimation for cortical thickness and myelin was assessed using intraclass correlation coefficient. Results: Comparable segmentation accuracy and reliable estimates of cortical thickness and myelin were obtained from relatively high acceleration factor with SmartSpeed AI reconstruction. Impact: SmartSpeed AI reconstruction enabled accurate cortical segmentation, and the reliable estimation of cortical thickness and intracortical myelin, suggesting the validity of its clinical applicability with reduced scan time.