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基于多模态图像融合的手术机器人软件系统开发:研究方案

Development of a software system for surgical robots based on multimodal image fusion: study protocol.

作者信息

Yuan Shuo, Chen Ruiyuan, Zang Lei, Wang Aobo, Fan Ning, Du Peng, Xi Yu, Wang Tianyi

机构信息

Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.

出版信息

Front Surg. 2024 Jun 6;11:1389244. doi: 10.3389/fsurg.2024.1389244. eCollection 2024.

Abstract

BACKGROUND

Surgical robots are gaining increasing popularity because of their capability to improve the precision of pedicle screw placement. However, current surgical robots rely on unimodal computed tomography (CT) images as baseline images, limiting their visualization to vertebral bone structures and excluding soft tissue structures such as intervertebral discs and nerves. This inherent limitation significantly restricts the applicability of surgical robots. To address this issue and further enhance the safety and accuracy of robot-assisted pedicle screw placement, this study will develop a software system for surgical robots based on multimodal image fusion. Such a system can extend the application range of surgical robots, such as surgical channel establishment, nerve decompression, and other related operations.

METHODS

Initially, imaging data of the patients included in the study are collected. Professional workstations are employed to establish, train, validate, and optimize algorithms for vertebral bone segmentation in CT and magnetic resonance (MR) images, intervertebral disc segmentation in MR images, nerve segmentation in MR images, and registration fusion of CT and MR images. Subsequently, a spine application model containing independent modules for vertebrae, intervertebral discs, and nerves is constructed, and a software system for surgical robots based on multimodal image fusion is designed. Finally, the software system is clinically validated.

DISCUSSION

We will develop a software system based on multimodal image fusion for surgical robots, which can be applied to surgical access establishment, nerve decompression, and other operations not only for robot-assisted nail placement. The development of this software system is important. First, it can improve the accuracy of pedicle screw placement, percutaneous vertebroplasty, percutaneous kyphoplasty, and other surgeries. Second, it can reduce the number of fluoroscopies, shorten the operation time, and reduce surgical complications. In addition, it would be helpful to expand the application range of surgical robots by providing key imaging data for surgical robots to realize surgical channel establishment, nerve decompression, and other operations.

摘要

背景

手术机器人因其能够提高椎弓根螺钉置入的精度而越来越受欢迎。然而,目前的手术机器人依赖单模态计算机断层扫描(CT)图像作为基线图像,其可视化仅限于椎骨结构,排除了椎间盘和神经等软组织结构。这种固有的局限性严重限制了手术机器人的适用性。为了解决这个问题并进一步提高机器人辅助椎弓根螺钉置入的安全性和准确性,本研究将开发一种基于多模态图像融合的手术机器人软件系统。这样的系统可以扩展手术机器人的应用范围,如手术通道建立、神经减压及其他相关手术。

方法

首先,收集纳入研究的患者的影像数据。使用专业工作站建立、训练、验证和优化CT和磁共振(MR)图像中椎骨分割、MR图像中椎间盘分割、MR图像中神经分割以及CT和MR图像配准融合的算法。随后,构建一个包含椎骨、椎间盘和神经独立模块的脊柱应用模型,并设计一种基于多模态图像融合的手术机器人软件系统。最后,对该软件系统进行临床验证。

讨论

我们将开发一种基于多模态图像融合的手术机器人软件系统,该系统不仅可应用于机器人辅助置钉,还可用于手术入路建立、神经减压等手术。该软件系统的开发具有重要意义。首先,它可以提高椎弓根螺钉置入、经皮椎体成形术、经皮后凸成形术等手术的准确性。其次,它可以减少透视次数,缩短手术时间,并减少手术并发症。此外,通过为手术机器人提供关键影像数据以实现手术通道建立、神经减压等手术,有助于扩大手术机器人的应用范围。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/72b0/11187239/0242583953a9/fsurg-11-1389244-g001.jpg

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