论文标题

具有成本效益的增强现实的联合场景和对象跟踪辅助放射治疗中的患者定位

Joint Scene and Object Tracking for Cost-Effective Augmented Reality Assisted Patient Positioning in Radiation Therapy

论文作者

Sarmadi, Hamid, Muñoz-Salinas, Rafael, Berbís, M. Álvaro, Luna, Antonio, Medina-Carnicer, R.

论文摘要

背景和客观:在增强现实(AR)领域进行的用于放射治疗中患者定位的研究很少。我们提出了一种有效且具有成本效益的算法,用于跟踪现场,并通过向操作员提供视觉反馈,以交互性地帮助患者的定位过程。据我们所知,这是可以用于指导患者定位的移动交互式AR的第一个框架。方法:我们提出了一种点云处理方法,该方法与基准标记算法算法结合使用,并且广义的ICP算法跟踪患者,并且仅使用CPU单元精确有效地摄像机。使用有效的身体重建算法计算出3D参考模型和身体标记图之间的比对。结果:我们的定量评估表明,所提出的方法在9 fps时达到了4.17 mm/0.82 ver的翻译和旋转误差。此外,定性结果证明了我们的算法在患者对不同受试者的定位中的有用性。结论:由于我们的算法使用常规笔记本电脑(不使用专用GPU)实现了相对较高的帧速率和准确性,因此这是一种非常具有成本效益的基于AR的患者定位方法。它也通过引入一个可以改进的框架来为其他研究人员提供更好的移动互动AR患者定位解决方案,从而为其他研究人员打开了道路。

BACKGROUND AND OBJECTIVE: The research done in the field of Augmented Reality (AR) for patient positioning in radiation therapy is scarce. We propose an efficient and cost-effective algorithm for tracking the scene and the patient to interactively assist the patient's positioning process by providing visual feedback to the operator. Up to our knowledge, this is the first framework that can be employed for mobile interactive AR to guide patient positioning. METHODS: We propose a point cloud processing method that combined with a fiducial marker-mapper algorithm and the generalized ICP algorithm tracks the patient and the camera precisely and efficiently only using the CPU unit. The alignment between the 3D reference model and body marker map is calculated employing an efficient body reconstruction algorithm. RESULTS: Our quantitative evaluation shows that the proposed method achieves a translational and rotational error of 4.17 mm/0.82 deg at 9 fps. Furthermore, the qualitative results demonstrate the usefulness of our algorithm in patient positioning on different human subjects. CONCLUSION: Since our algorithm achieves a relatively high frame rate and accuracy employing a regular laptop (without the usage of a dedicated GPU), it is a very cost-effective AR-based patient positioning method. It also opens the way for other researchers by introducing a framework that could be improved upon for better mobile interactive AR patient positioning solutions in the future.

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