Medical University of Innsbruck, Innsbruck, Austria.
Int J Comput Assist Radiol Surg. 2020 Jun;15(6):953-962. doi: 10.1007/s11548-020-02174-3. Epub 2020 Apr 28.
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.
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.
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.
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.
介绍一种基于磁跟踪技术的术中实时呼吸肿瘤运动预测系统。它基于不同身体区域的呼吸运动，提供患者和单个/多个肿瘤的特异性预测，有助于治疗指导。
定制的仿体患者模型模拟类似于人体的呼吸周期，同时应用定制的传感器支架概念在患者表面找到最佳的传感器数量及其最佳可能放置位置，以用于实时手术导航和内部肿瘤的运动预测。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以在术前阶段进行离线预测，以提高实时预测的准确性。
在所有内部目标位置，使用三种不同的配准模式（在完全/半吸气和完全呼气位置）对两种评估方法进行了定量评估：静态方法通过停止模拟呼吸来评估准确性，动态方法则在持续呼吸模式下进行。两种方法的总体均方根误差（RMS）均在[公式：见文本]和[公式：见文本]之间。总体注册 RMS 误差为[公式：见文本]。通过在半吸气位置进行注册，观察到最佳的预测误差，最小[公式：见文本]，最大[公式：见文本]。由此产生的准确性满足大多数放疗治疗或手术的要求，例如肺部、肝脏、前列腺和脊柱。
提出了该系统，旨在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生和患者常用方法的已知技术和人为限制。
