用纤维扫描内镜进行高速光学连贯弹性学深度学习
IEEE transactions on medical imaging
|March 3, 2025
概括
一个新的微型纤维扫描内镜能够快速地进行局部组织弹性图,用于最少的侵入性手术. 深度学习处理实现实时弹性映射,与传统方法相比,误差大大降低.
科学领域:
- 生物医学工程 生物医学工程
- 医疗成像医学成像
- 软组织力学 软组织力学
背景情况:
- 组织硬度对于诊断软组织病理至关重要.
- 目前用于组织硬的临床成像方法往往不适合在手术内使用.
- 最少侵入性手术需要实时,局部化的机械性能评估.
研究的目的:
- 开发一个微型纤维扫描内镜,用于实时弹性图.
- 实施深度学习 (DL) 管道来处理复杂的波场数据.
- 为了在干预过程中实现快速和局部的弹性估计.
主要方法:
- 一个小型的纤维扫描内镜,用于形扫描模式 (5.05kHz时间频率) 的优化探针设计.
- 一个基于深度学习的时空网络在幻影数据上进行了端到端的训练,用于弹性估计.
- 复杂,分散的波场在多个频率和方向的成像.
主要成果:
- 与传统的相位跟踪相比,DL方法在弹性估计中的平均绝对误差显著降低 (3D中的4.48kPa与19.75kPa).
- 实时弹性映射通过局部化和强大的估计来证明.
- 在ex-vivo猪组织中证明了可行性.
结论:
- 开发的纤维扫描内镜和DL管道为实时手术内组织弹性图形提供了一个有前途的解决方案.
- 这项技术可以提高诊断准确度,并在最少侵入性手术过程中提供指导.
- 需要在临床环境中进一步验证.
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