走向ICE-XRF融合:使用深度学习在2DX射线中对心内回声探测器的实时姿势估计使用深度学习
Annelies Severens1,2, Midas Meijs3, Vipul Pai Raikar4
1Biomedical Engineering, University of Technology Eindhoven, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands. a.c.e.severens@tue.nl.
International journal of computer assisted radiology and surgery
|August 18, 2025
概括
一个新的深度学习算法可以实时融合心内回声学 (ICE) 和X射线光学 (XRF) 以改善跨导管治疗,提高程序的准确性和可访问性.
科学领域:
- 医学成像医学成像
- 心血管干预包括心血管干预.
- 医学中的人工智能
背景情况:
- 膜心脏病影响着老年人群的很大一部分,许多患者无法接受传统手术.
- 过导管疗法提供了不那么侵入性的替代方案,但依赖于先进的成像指导.
- 像TEE这样的当前超声波方法有局限性,而ICE提供更好的可视化,但面临采用障碍.
研究的目的:
- 开发一种基于深度学习的算法,用于融合心内回声学 (ICE) 和X射线成像 (XRF).
- 为了提高ICE的可用性和准确性,用于跨导管干预.
- 为了克服ICE探头处理和操作员培训方面的挑战.
主要方法:
- 为ICE-XRF融合设计了一个两阶段的深度学习算法.
- 一个物体检测网络被用于ICE探测器的初始粗度检测.
- 一个回归网络执行的5度自由度 (DoF) 构成ICE探头的估计.
主要成果:
- 该框架展示了精确的探针检测和5-DoF对合成和临床数据的估计.
- 对象检测在合成数据上获得了1.00的F1得分,在临床病例中获得了高精度 (0.97) 和回忆 (0.83) 的高精度.
- 姿势估计显示中位位置误差低于0.5mm,中位旋转误差低于7.2度.
结论:
- 实时检测方法支持在程序期间无的ICE和XRF图像融合.
- 这有利于ICE在跨导管治疗中的更广泛的临床采用.
- 开发的算法提高了微创心脏干预的安全性和有效性.
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