首次直接从扩散MRI重建皮层表面，提升精度与效率。
DDCSR: A Novel End-to-End Deep Learning Framework for Cortical Surface Reconstruction from Diffusion MRI
- 端到端框架跳过传统配准，直接从dMRI生成表面表示
- 在多个数据集上实现更高精度与更快重建速度
- 适用于不同扫描设备和人群，泛化能力强
扩散MRI（dMRI）在研究脑白质连接中起关键作用。皮层表面重建（CSR）是纤维束追踪和多模态MRI分析中的核心任务，通常需依赖解剖T1加权图像，并通过跨模态配准映射至dMRI空间。然而，由于dMRI分辨率低且存在图像畸变，跨模态配准面临巨大挑战。本文提出一种全新端到端深度学习框架DDCSR，首次实现仅从dMRI数据直接完成皮层表面重建。DDCSR包含两个核心模块：(1) 隐式学习模块，预测体素级中间表面表示；(2) 显式学习模块，生成3D网格表面。相比多种基线及先进方法，所提DDCSR显著提升重建精度与效率。此外，即使在不同dMRI采集方案与人群间，仍表现出强泛化能力。
原文摘要 · Abstract (English)
Diffusion MRI (dMRI) plays a crucial role in studying brain white matter connectivity. Cortical surface reconstruction (CSR), including the inner whiter matter (WM) and outer pial surfaces, is one of the key tasks in dMRI analyses such as fiber tractography and multimodal MRI analysis. Existing CSR methods rely on anatomical T1-weighted data and map them into the dMRI space through inter-modality registration. However, due to the low resolution and image distortions of dMRI data, inter-modality registration faces significant challenges. This work proposes a novel end-to-end learning framework, DDCSR, which for the first time enables CSR directly from dMRI data. DDCSR consists of two major components, including: (1) an implicit learning module to predict a voxel-wise intermediate surface representation, and (2) an explicit learning module to predict the 3D mesh surfaces. Compared to several baseline and advanced CSR methods, we show that the proposed DDCSR can largely increase both accuracy and efficiency. Furthermore, we demonstrate a high generalization ability of DDCSR to data from different sources, despite the differences in dMRI acquisitions and populations.
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