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Based on respiratory movements in different body regions, it provides patient and single\/multiple tumor-specific prediction that facilitates the guiding of treatments.<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Methods<\/jats:bold><\/jats:title><jats:p>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\u2019s 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\u2019s 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.<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Results<\/jats:bold><\/jats:title><jats:p>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<jats:inline-formula><jats:alternatives><jats:tex-math>$$0.32\\pm 0.06~\\hbox {mm}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mn>0.32<\/mml:mn><mml:mo>\u00b1<\/mml:mo><mml:mn>0.06<\/mml:mn><mml:mspace\/><mml:mtext>mm<\/mml:mtext><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>and<jats:inline-formula><jats:alternatives><jats:tex-math>$$3.71\\pm 0.79~\\hbox {mm}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mn>3.71<\/mml:mn><mml:mo>\u00b1<\/mml:mo><mml:mn>0.79<\/mml:mn><mml:mspace\/><mml:mtext>mm<\/mml:mtext><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>. The overall registration RMS error was<jats:inline-formula><jats:alternatives><jats:tex-math>$$0.6\\pm 0.4~\\hbox {mm}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mn>0.6<\/mml:mn><mml:mo>\u00b1<\/mml:mo><mml:mn>0.4<\/mml:mn><mml:mspace\/><mml:mtext>mm<\/mml:mtext><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>. The best prediction errors were observed by registrations at half inhaled positions with minimum<jats:inline-formula><jats:alternatives><jats:tex-math>$$0.27\\pm 0.02~\\hbox {mm}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mn>0.27<\/mml:mn><mml:mo>\u00b1<\/mml:mo><mml:mn>0.02<\/mml:mn><mml:mspace\/><mml:mtext>mm<\/mml:mtext><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>, maximum<jats:inline-formula><jats:alternatives><jats:tex-math>$$2.90\\pm 0.72~\\hbox {mm}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mn>2.90<\/mml:mn><mml:mo>\u00b1<\/mml:mo><mml:mn>0.72<\/mml:mn><mml:mspace\/><mml:mtext>mm<\/mml:mtext><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>. The resulting accuracy satisfies most radiotherapy treatments or surgeries, e.g., for lung, liver, prostate and spine.<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Conclusion<\/jats:bold><\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1007\/s11548-020-02174-3","type":"journal-article","created":{"date-parts":[[2020,4,28]],"date-time":"2020-04-28T14:02:43Z","timestamp":1588082563000},"page":"953-962","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["respiTrack: Patient-specific real-time respiratory tumor motion prediction using magnetic tracking"],"prefix":"10.1007","volume":"15","author":[{"given":"Yusuf","family":"\u00d6zbek","sequence":"first","affiliation":[]},{"given":"Zolt\u00e1n","family":"B\u00e1rdosi","sequence":"additional","affiliation":[]},{"given":"Wolfgang","family":"Freysinger","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,28]]},"reference":[{"issue":"5","key":"2174_CR1","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1088\/0031-9155\/56\/5\/005","volume":"56","author":"I Buzurovic","year":"2011","unstructured":"Buzurovic I, Huang K, Yu Y, Podder TK (2011) A robotic approach to 4D real-time tumor tracking for radiotherapy. 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