用最小能量约束提升脑皮层重建的稳定性和可复现性
Template-Based Cortical Surface Reconstruction with Minimal Energy Deformation
- 引入最小能量变形损失，正则化形变轨迹
- 在保持精度和拓扑正确性的前提下显著提升训练一致性
- 适合需要高可复现性脑结构分析的研究者
从磁共振成像（MRI）中进行脑皮层表面重建（CSR）是神经影像分析的基础，支持大脑皮层形态学研究和功能脑图谱构建。基于学习的CSR方法已大幅加速重建过程，可在数秒内通过解剖模板的形变完成。然而，如何确保学习到的形变在能量上最优且训练结果一致仍是难题。本文设计了最小能量变形（MED）损失，作为形变轨迹的正则项，补充广泛使用的Chamfer距离。将其融入近期V2C-Flow模型，在不损害重建精度与拓扑正确性的前提下，显著提升了训练的一致性和可复现性。
原文摘要 · Abstract (English)
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.
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