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关于心血管解剖学和血液动力学场的隐性神经表示的准确性

Jubilee Lee¹, Daniele E Schiavazzi¹

¹Department of Applied and Computational Mathematics and Statistics University of Notre Dame, Notre Dame, United States.

Computers in biology and medicine

|February 26, 2026

概括

隐式神经表示 (INRs) 有效地压缩复杂的血液动力学数据和心血管解剖学. 这些神经场实现了高精度,为科学应用中的数据表示和压缩提供了有前途的解决方案.

科学领域:

计算流体动力学的流体动力学.
医学成像医学成像
深度学习是一种深度学习.

背景情况:

隐式神经表示 (INR) 为传统的基于voxel或网格的数据表示提供了分辨率独立和内存高效的替代方案.
在心血管建模等专业领域,INR的准确性需要进行彻底的调查.

研究的目的:

评估来自数值模拟的压缩血动力场的最先进INR的性能.
评估INRs在使用符号距离函数表示心血管解剖学的能力.

主要方法:

研究了减轻INR频谱偏差的策略,包括专门的激活函数,位置编码和非线性内核的组合.
将INR应用于胸前大动脉中现实,空间和时间变化的血液动力学场.
使用INR来表示48种不同的胸前大动脉解剖学.

主要成果:

达到大约230的压缩比用于血液动力学场,具有较低的最大绝对误差 (压力为1mmHg,速度为5-10cm/s).
在解剖学表示中表现出高精度,平均和最大绝对差异分别低于0.5毫米和1.6毫米.
SIREN,MFN-Gabor和MHE架构显示出卓越的性能.

结论:

INR提供了一种高效的方法来压缩和表示复杂的血液动力学和解剖学数据,并具有非常高的准确性.
这些发现支持INRs在特定领域的应用中的潜力,特别是在心血管建模和模拟中.
优化的INR架构和技术可以克服诸如光谱偏差之类的挑战,提高它们在科学研究中的实用性.

关键词:

模拟数据的压缩.隐含的神经表征是隐含的神经表征.神经领域的神经场.频谱偏差是一种偏差.

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