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从增量模拟中学习软组织变形

Nathan Lampen¹, Daeseung Kim², Xuanang Xu¹

¹Department of Biomedical Engineering and the Center for Biotechnology and Interdisciplinary Studies at Rensselaer Polytechnic Institute, Troy, New York, USA.

Medical physics

|December 6, 2024

概括

时空增量建模准确地模拟了面部软组织变形,用于手术规划. 这种深度学习方法与传统的有限元素方法相比,大大减少了计算时间.

科学领域:

生物机械建模生物机械建模
计算解剖学的计算解剖学
医疗模拟医疗模拟

背景情况:

精确的面部软组织生物机械建模对于高效的正形外科手术规划至关重要.
传统的有限元法 (FEM) 模拟由于增加步骤而耗时,阻碍了临床整合.
深度学习 (DL) 提供了潜在的加速,但在增量模拟中经常忽视时间动态.

研究的目的:

调查时空增量建模用于模拟面部软组织生物力学的有效性.
开发和评估基于DL的方法,该方法整合了空间和时间特征,以提高模拟准确性和速度.

主要方法:

实现图形神经网络 (GNN) 用于时空增量建模.
培训DL网络在17位直立手术患者的增量FEM模拟上.
将空间特征提取与时间聚合用于模拟的协同作用.

主要成果:

时空增量方法实现了0.37mm的平均精度和1.52秒的计算时间.
只有空间增量方法的结果是0.44毫米的精度和1.60秒的计算时间.
一种仅用于空间的单步方法显示0.41mm的精度,但更快的0.05s计算时间.

结论:

与仅限于空间的增量方法相比,时空增量建模显著减少了平均误差,突出了时间信息的价值.
开发的DL方法有效模拟软组织变形,大大减少模拟时间与FEM相比.
这种方法有望加速临床外科手术规划工作流程.

关键词:

深度学习是一种深度学习.面部模拟器面部模拟器增量建模增量建模增量建模神经网络的神经网络的神经网络进行外科手术的计划.

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