Title: Template-Based Cortical Surface Reconstruction with Minimal Energy Deformation Show Chinese title

Title: 基于模板的皮层表面重建与最小能量变形

Abstract: Cortical surface reconstruction (CSR) from magnetic resonance imaging (MRI) is fundamental to neuroimage analysis, enabling morphological studies of the cerebral cortex and functional brain mapping. Recent advances in learning-based CSR have dramatically accelerated processing, allowing for reconstructions through the deformation of anatomical templates within seconds. However, ensuring the learned deformations are optimal in terms of deformation energy and consistent across training runs remains a particular challenge. In this work, we design a Minimal Energy Deformation (MED) loss, acting as a regularizer on the deformation trajectories and complementing the widely used Chamfer distance in CSR. We incorporate it into the recent V2C-Flow model and demonstrate considerable improvements in previously neglected training consistency and reproducibility without harming reconstruction accuracy and topological correctness. Show Chinese abstract

Abstract: 从磁共振成像（MRI）中进行皮层表面重建（CSR）是神经影像分析的基础，使得大脑皮层的形态学研究和功能脑图谱成为可能。基于学习的CSR最近取得了进展，显著加快了处理速度，使解剖模板的变形重建在几秒内即可完成。然而，确保学习到的变形在变形能量方面是最优的，并且在训练运行中保持一致仍然是一个特殊的挑战。在本工作中，我们设计了一个最小能量变形（MED）损失，作为对变形轨迹的正则化器，并补充了CSR中广泛使用的契姆弗距离。我们将它集成到最近的V2C-Flow模型中，并在不损害重建精度和拓扑正确性的前提下，显著提高了之前被忽视的训练一致性和可重复性。