Evidence map›Paper›PMID 32347464›Full record

ArticleInternational journal of computer assisted radiology and surgery2020

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

Yusuf Özbek, Zoltán Bárdosi, Wolfgang Freysinger

Abstract read
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Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Yusuf ÖzbekMedical University of Innsbruck, Innsbruck, Austria. yusuf.oezbek@student.i-med.ac.at.
Zoltán BárdosiMedical University of Innsbruck, Innsbruck, Austria.
Wolfgang FreysingerMedical University of Innsbruck, Innsbruck, Austria.

Funding

Medizinische Universität Innsbruck D-153110-019-014
6 · The paper itself

Abstract

purposeAn 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.

methodsA 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.

resultsTwo 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.

conclusionThe 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.

Indexed as

MotionRespirationAlgorithmsFour-Dimensional Computed TomographyHumansOrgan MotionPhantoms, ImagingMagnetic trackingPrediction optimizationReal-time tumor trackingRespiratory motion

Identifiers

PMID32347464
PMCPMC7303076

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.