respiTrack: Patient-specific real-time respiratory tumor motion prediction using magnetic tracking

Abstract

Abstract 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 $$0.32\pm 0.06~\hbox {mm}$$0.32±0.06mm and $$3.71\pm 0.79~\hbox {mm}$$3.71±0.79mm. The overall registration RMS error was $$0.6\pm 0.4~\hbox {mm}$$0.6±0.4mm. The best prediction errors were observed by registrations at half inhaled positions with minimum $$0.27\pm 0.02~\hbox {mm}$$0.27±0.02mm, maximum $$2.90\pm 0.72~\hbox {mm}$$2.90±0.72mm. 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.