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使用预训练的见证传感器失真模型提高医学应用中的电磁跟踪精度。

Enhancing electromagnetic tracking accuracy in medical applications using pre-trained witness sensor distortion models.

机构信息

University College Cork, Cork, Ireland.

Tyndall National Institute, Cork, Ireland.

出版信息

Int J Comput Assist Radiol Surg. 2024 Jan;19(1):27-31. doi: 10.1007/s11548-023-02994-z. Epub 2023 Jul 27.

DOI:10.1007/s11548-023-02994-z
PMID:37501053
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10769911/
Abstract

PURPOSE

Electromagnetic tracking (EMT) accuracy is affected by the presence of surrounding metallic materials. In this work, we propose measuring the magnetic field's variation due to distortion at a witness position to localise the instrument causing distortion based on a pre-trained model and without additional sensors attached to it.

METHODS

Two experiments were performed to demonstrate possible applications of the technique proposed. In the first case, the distortion introduced by an ultrasound (US) probe was characterised and subsequently used to track the probe position on a line. In the second application, the measurement was used to estimate the distance of an interventional fluoroscopy C-arm machine and apply the correct compensation model.

RESULTS

Tracking of the US probe using the proposed method was demonstrated with millimetric accuracy. The distortion created by the C-arm caused errors in the order of centimetres, which were reduced to 1.52 mm RMS after compensation.

CONCLUSIONS

The distortion profile associated with medical equipment was pre-characterised and used in applications such as object tracking and error compensation map selection. In the current study, the movement was limited to one degree of freedom (1 DOF) and simple analytical functions were used to model the magnetic distortion. Future work will explore advanced AI models to extend the method to 6 DOF tracking using multiple witness sensors.

摘要

目的

电磁跟踪(EMT)的准确性受到周围金属材料存在的影响。在这项工作中,我们提出测量由于失真而在见证位置处的磁场变化,以便基于预训练的模型并在不附加额外传感器的情况下定位导致失真的仪器。

方法

进行了两项实验来演示所提出技术的可能应用。在第一种情况下,对超声(US)探头引入的失真进行了表征,并随后用于在线跟踪探头位置。在第二种应用中,该测量用于估计介入式荧光透视 C 臂机的距离并应用正确的补偿模型。

结果

使用所提出的方法成功演示了 US 探头的跟踪,其精度达到毫米级。C 臂造成的失真会导致厘米级的误差,经过补偿后,误差降低到 1.52mm RMS。

结论

与医疗设备相关的失真轮廓已预先进行了特征描述,并在对象跟踪和误差补偿图选择等应用中得到了应用。在当前研究中,运动限制在一个自由度(1 DOF)以内,并使用简单的分析函数来对磁场失真进行建模。未来的工作将探索先进的人工智能模型,以使用多个见证传感器将该方法扩展到 6 DOF 跟踪。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f485/10769911/f7657f113c74/11548_2023_2994_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f485/10769911/7ef321b4f7aa/11548_2023_2994_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f485/10769911/6941cb7bd8af/11548_2023_2994_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f485/10769911/74b492b1c3f9/11548_2023_2994_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f485/10769911/f7657f113c74/11548_2023_2994_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f485/10769911/7ef321b4f7aa/11548_2023_2994_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f485/10769911/6941cb7bd8af/11548_2023_2994_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f485/10769911/74b492b1c3f9/11548_2023_2994_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f485/10769911/f7657f113c74/11548_2023_2994_Fig4_HTML.jpg

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CycleGAN for interpretable online EMT compensation.CycleGAN 用于可解释的在线 EMT 补偿。
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