PURPOSE An intraoperative real-time respiratory tumor motion prediction system with magnetic tracking technology is presented. Based on respiratory movements in different body regions, it provides patient and single/multiple tumor-specific prediction that facilitates the guiding of treatments. METHODS A custom-built phantom patient model replicates the respiratory cycles similar to a human body, while the custom-built sensor holder concept is applied on the patient's surface to find optimum sensor number and their best possible placement locations to use in real-time surgical navigation and motion prediction of internal tumors. Automatic marker localization applied to patient's 4D-CT data, feature selection and Gaussian process regression algorithms enable off-line prediction in the preoperative phase to increase the accuracy of real-time prediction. RESULTS Two evaluation methods with three different registration patterns (at fully/half inhaled and fully exhaled positions) were used quantitatively at all internal target positions in phantom: The statical method evaluates the accuracy by stopping simulated breathing and dynamic with continued breathing patterns. The overall root mean square error (RMS) for both methods was between [Formula: see text] and [Formula: see text]. The overall registration RMS error was [Formula: see text]. The best prediction errors were observed by registrations at half inhaled positions with minimum [Formula: see text], maximum [Formula: see text]. The resulting accuracy satisfies most radiotherapy treatments or surgeries, e.g., for lung, liver, prostate and spine. CONCLUSION The built system is proposed to predict respiratory motions of internal structures in the body while the patient is breathing freely during treatment. The custom-built sensor holders are compatible with magnetic tracking. Our presented approach reduces known technological and human limitations of commonly used methods for physicians and patients.

中文翻译：

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respiTrack：使用磁跟踪的患者特定实时呼吸道肿瘤运动预测。

目的提出一种具有磁跟踪技术的术中实时呼吸道肿瘤运动预测系统。根据不同身体部位的呼吸运动，它可以提供患者和单个/多个肿瘤特异性预测，从而有助于指导治疗。方法定制的幻影患者模型复制与人体相似的呼吸周期，而定制的传感器支架概念应用于患者表面，以找到最佳传感器数量及其在实时外科手术中可能使用的最佳放置位置内部肿瘤的导航和运动预测。自动标记定位应用于患者的4D-CT数据，特征选择和高斯过程回归算法可在术前阶段进行离线预测，以提高实时预测的准确性。结果在幻象的所有内部目标位置上，定量使用了两种具有三种不同注册方式（在完全/半吸入和完全呼出位置）的评估方法：静态方法通过停止模拟呼吸和动态模式（连续呼吸）来评估准确性。两种方法的总均方根误差（RMS）在[公式：参见文本]和[公式：参见文本]之间。总注册RMS错误为[公式：参见文本]。通过在最小吸入量（公式：参见文字），最大吸入量（公式：参见文字）的一半处进行配准观察到最佳的预测误差。所得到的准确性可以满足大多数放射治疗或手术的要求，例如肺，肝，前列腺和脊柱。结论提出的构建系统可预测患者在治疗过程中自由呼吸时体内内部结构的呼吸运动。定制的传感器支架与磁跟踪兼容。我们提出的方法减少了医师和患者常用方法的已知技术和人为限制。