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端到端的2D/3D注册从术前MRI到手术内光学,用于骨科手术

Ping-Cheng Ku¹, Mingxu Liu², Robert Grupp²

¹Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA. pku1@jh.edu.

International journal of computer assisted radiology and surgery

|May 30, 2025

概括

这项研究引入了MRI-to-fluoroscopic图像注册框架,以改善骨科手术中的手术内可视化. 这种新的方法提高了软组织病理和骨缺陷的可见性,帮助外科医生进行复杂的手术.

科学领域:

医疗成像医学成像
整形外科手术整形外科手术
以图像为指导的干预

背景情况:

在骨科手术中软组织病理和骨缺陷的手术内可视化对传统的光镜成像具有挑战性.
准确的解剖学定位对于成功的骨科手术至关重要,特别是涉及股骨和骨盆的手术.

研究的目的:

开发和验证一个端到端的MRI到光镜图像注册框架.
通过提高关键解剖结构的可见性,增强外科医生在骨科手术期间的手术内可视化.

主要方法:

利用深度学习进行MRI细分和合成CT (sCT) 量生成.
从sCT卷中制作数字重建放射图 (DRR) 用于2D/3D记录与手术内光镜图像.
通过模拟和尸体研究验证了用于核心减压 (CD) 手术的框架,重点是股骨和骨盆区域.

主要成果:

在尸体研究中,对于大腿部区域,平均翻译登记精度为2.4 ± 1.0mm,旋转精度为1.6 ± 0.8°.
成功可视化了在传统光镜上看不到的瘤病变.
在股骨和骨盆手术的图像指导方面取得了显著的进步.

结论:

该MRI-to-fluoroscopic注册框架为骨科手术中的图像指导提供了一种新的方法,仅依赖MRI数据.
增强软组织和骨缺陷的可视化,可能减少辐射暴露.
提供了一个更安全,更有效的替代在手术期间的外科指导.

关键词:

2D/3D注册登记是什么意思核心压缩解压缩深度学习是一种深度学习.磁力共振成像细分磁力共振成像细分核磁共振成X射线的注册记录

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