摘要翻译：
电磁跟踪(EMT)是图像引导过程中导航和可视化的核心平台技术。该技术在非视距环境中提供高跟踪精度，允许在光学跟踪不可行的位置进行仪器导航。EMT在复杂过程中的集成（通常与多模态成像相结合）正在兴起，但许多研究人员和系统设计人员已经注意到现有硬件平台的不足。EMT领域的进展包括改进跟踪系统准确性、精确度和误差补偿能力的新方法，尽管由于商业跟踪解决算法的“黑盒”性质，这种系统级的改进不能容易地纳入当前的治疗应用。本文定义了一个软件框架，允许新的EMT设计和改进成为图像引导干预的全球设计过程的一部分。为了使EMT的开发标准化，我们根据所有EMT系统共有的四个系统功能定义了一个通用的跨平台软件框架；采集、过滤、建模和求解。每个软件组件之间的接口是根据它们的输入和输出数据结构定义的。用Python编程语言实现了一个示例框架，并用开源的Anser EMT系统进行了演示。从Anser EMT的Matlab和Python实现中收集性能指标，考虑主机操作系统、硬件配置和使用的获取设置。结果表明，在Windows操作系统上使用该框架可以实现5毫秒的指示性系统延迟，而在类UNIX平台上观察到的系统性能下降。
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英文标题：
《An Open Framework Enabling Electromagnetic Tracking in Image-Guided
  Interventions》
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作者：
Herman Alexander Jaeger, Stephen Hinds and P\'adraig Cantillon-Murphy
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Electromagnetic tracking (EMT) is a core platform technology in the navigation and visualisation of image-guided procedures. The technology provides high tracking accuracy in non-line-of-sight environments, allowing instrument navigation in locations where optical tracking is not feasible. Integration of EMT in complex procedures, often coupled with multi-modal imaging, is on the rise, yet the lack of exibility in the available hardware platforms has been noted by many researchers and system designers. Advances in the field of EMT include novel methods of improving tracking system accuracy, precision and error compensation capabilities, though such system-level improvements cannot be readily incorporated in current therapy applications due to the `blackbox' nature of commercial tracking solving algorithms. This paper defines a software framework to allow novel EMT designs and improvements become part of the global design process for image-guided interventions. In an effort to standardise EMT development, we define a generalised cross-platform software framework in terms of the four system functions common to all EMT systems; acquisition, filtering, modelling and solving. The interfaces between each software component are defined in terms of their input and output data structures. An exemplary framework is implemented in the Python programming language and demonstrated with the open-source Anser EMT system. Performance metrics are gathered from both Matlab and Python implementations of Anser EMT considering the host operating system, hardware configuration and acquisition settings used. Results show indicative system latencies of 5 ms can be achieved using the framework on a Windows operating system, with decreased system performance observed on UNIX-like platforms.
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PDF链接：
https://arxiv.org/pdf/1807.11073