Background and Objective

The research is 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 pointcloud 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 3D reference model and body marker map alignment 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${}^{\circ }$ 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 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.

中文翻译：

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联合场景和对象跟踪，用于在放射治疗中进行经济高效的增强现实引导患者定位

背景与目的

研究是在增强现实 (AR) 领域进行的，用于放射治疗中的患者定位很少。我们提出了一种高效且具有成本效益的算法，用于跟踪场景和患者，通过向操作员提供视觉反馈来交互辅助患者的定位过程。据我们所知，这是第一个可用于移动交互式 AR 来指导患者定位的框架。

方法

我们提出了一种点云处理方法，结合基准标记映射算法和广义 ICP 算法，仅使用 CPU 单元即可精确高效地跟踪患者和相机。3D 参考模型和身体标记图对齐是使用有效的身体重建算法计算的。

结果

我们的定量评估表明，所提出的方法实现了 4.17 mm/0.82 的平移和旋转误差${}^{\circ }$每秒 9 帧。此外，定性结果证明了我们的算法在不同人类受试者的患者定位中的有用性。

结论

由于我们的算法使用普通笔记本电脑（没有专用 GPU）实现了相对较高的帧速率和准确度，因此它是一种非常经济高效的基于 AR 的患者定位方法。它还通过引入一个框架为其他研究人员开辟了道路，该框架可以在未来改进以提供更好的移动交互式 AR 患者定位解决方案。