ArticleInternational journal of computer assisted radiology and surgery2020
respiTrack: Patient-specific real-time respiratory tumor motion prediction using magnetic tracking.
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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Who cites it
4 citing papers in PubMed.
- Data-Driven Volumetric Computed Tomography Image Generation From Surface Structures Using a Patient-Specific Deep Leaning Model.International journal of radiation oncology, biology, physics · 2025Article
- Toward Intraoperative Visual Intelligence: Real-Time Surgical Instrument Segmentation for Enhanced Surgical Monitoring.Healthcare (Basel, Switzerland) · 2024Article
- Development of AI-driven prediction models to realize real-time tumor tracking during radiotherapy.Radiation oncology (London, England) · 2022Article
- Automated fiducial marker detection and localization in volumetric computed tomography images: a three-step hybrid approach with deep learning.Journal of medical imaging (Bellingham, Wash.) · 2021Article
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Authors and funding
3 authors.
Funding
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.
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