Cortical Surface Reconstruction from 2D MRI with Segmentation-Constrained Super-Resolution and Representation Learning

Wenxuan Wu, Ruowen Qu, Dongzi Shi, Tong Xiong, Xiangmin Xu, Xiaofen Xing, Xin Zhang

Abstract: Cortical surface reconstruction typically relies on high-quality 3D brain MRI to establish the structure of cortex, playing a pivotal role in unveiling neurodevelopmental patterns. However, clinical challenges emerge due to elevated costs and prolonged acquisition times, often resulting in low-quality 2D brain MRI. To optimize the utilization of clinical data for cerebral cortex analysis, we propose a two-stage method for cortical surface reconstruction from 2D brain MRI images. The first stage employs segmentation-constrained MRI super-resolution, concatenating the super-resolution (SR) model and cortical ribbon segmentation model to emphasize cortical regions in the 3D images generated from 2D inputs. In the second stage, two encoders extract features from the original and super-resolution images. Through a shared decoder and the mask-swap module with multi-process training strategy, cortical surface reconstruction is achieved by mapping features from both the original and super-resolution images to a unified latent space. Experiments on the developing Human Connectome Project (dHCP) dataset demonstrate a significant improvement in geometric accuracy over the leading-SR based cortical surface reconstruction methods, facilitating precise cortical surface reconstruction from 2D images. The code is open-sourced at: https://github.com/SCUT-Xinlab/CSR-from-2D-MRI.