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高效的EndoNeRF重建及其用于数据驱动的外科模拟的应用.

Yuehao Wang1, Bingchen Gong1, Yonghao Long1

  • 1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.

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概括
此摘要是机器生成的。

这项研究引入了一种使用神经辐射场 (NeRF) 来实现现实的3D手术场景重建的新框架. 这种方法通过创建高质量的软组织模型进行模拟来增强外科培训和规划.

关键词:
3D重建重建的3D重建在NeRFRF中使用NeRF.机器人手术是一种机器人手术.手术模拟器手术模拟器

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科学领域:

  • 医疗模拟 医疗模拟
  • 计算机图形 计算机图形
  • 手术技术 手术技术

背景情况:

  • 现实的手术场景建模对于规划和培训至关重要.
  • 现有的方法难以准确地重建软组织形状和纹理.
  • 高质量的可变形软组织模型很少用于外科模拟.

研究的目的:

  • 通过使用神经辐射场 (NeRF) 进行高质量的外科重建的数据驱动框架.
  • 通过精确的软组织建模,实现现实的外科模拟.
  • 解决医疗保健中高保真软组织模型的短缺问题.

主要方法:

  • 为手术场景开发了一个基于NeRF的快速3D重建方法.
  • 实现了一个闭网提取算法,用于自动化模拟环境的创建.
  • 在捕捉变形和细粒度细节方面表现优于传统方法.

主要成果:

  • 在手术场景3D重建中验证了卓越的性能和效率.
  • 成功地利用重建的软组织进行有限元素方法 (FEM) 和材料点方法 (MPM) 模拟.
  • 在数据驱动的外科模拟中证明了实际应用.

结论:

  • 提出了一个新的基于NeRF的重建框架,优化模拟.
  • 促进高质量的3D手术软组织模型的高效创建.
  • 通过模拟展示了受益于临床任务的潜力,例如通过模拟进行外科教育.