用神经皮层图加速脑皮层刚性配准，速度比传统方法快30倍。
NC-Reg : Neural Cortical Maps for Rigid Registration
- 用连续神经网络表示皮层特征，支持任意分辨率输出。
- 实现亚度级精度（<1°），在模板配准任务中表现优异。
- 适合临床脑图像预对齐，鲁棒性强且计算高效。
我们提出神经皮层图，一种连续且紧凑的神经表征，用于替代传统的离散结构（如网格和网格）。该方法可从任意尺寸的网格中学习，并在任意分辨率下提供特征。神经皮层图支持球面上的高效优化，运行时间相比经典重心插值快达30倍（相同迭代次数下）。作为概念验证，我们提出了NC-Reg算法，一种基于神经皮层特征图、梯度下降与模拟退火策略的新型迭代刚性配准方法。通过消融实验和个体到模板配准测试，该方法达到亚度级精度（距全局最优<1°），展现出良好的鲁棒性，可作为临床场景中关键的预对齐策略。
原文摘要 · Abstract (English)
We introduce neural cortical maps, a continuous and compact neural representation for cortical feature maps, as an alternative to traditional discrete structures such as grids and meshes. It can learn from meshes of arbitrary size and provide learnt features at any resolution. Neural cortical maps enable efficient optimization on the sphere and achieve runtimes up to 30 times faster than classic barycentric interpolation (for the same number of iterations). As a proof of concept, we investigate rigid registration of cortical surfaces and propose NC-Reg, a novel iterative algorithm that involves the use of neural cortical feature maps, gradient descent optimization and a simulated annealing strategy. Through ablation studies and subject-to-template experiments, our method demonstrates sub-degree accuracy ($<1^\circ$ from the global optimum), and serves as a promising robust pre-alignment strategy, which is critical in clinical settings.
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