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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Error detection using an electromagnetic tracking system in multi-catheter breast interstitial brachytherapy
Siti Masitho1,2, Karoline Kallis1, Vratislav Strnad1
1Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Abstract:
The hybrid treatment delivery system (HTDS) has been proposed as a possible option for a quality assurance in the multi-catheter interstitial brachytherapy for breast cancer patients. The system, which consists out of a prototype afterloader with an integrated electromagnetic tracking (EMT) sensor and an EMT system, allows the automatic measurement of implanted catheters. To test the feasibility of the system for error detection, possible treatment planning errors and treatment delivery errors were simulated. Planning errors such as an incorrect offset value, an incorrect indexer length, tip/connector end swaps, and partial swaps, and; treatment delivery errors such as catheter shifts and catheter connection swaps were manually simulated using phantoms. An in-house Matlab routine was used to assess geometrical deviations between the dwell positions defined based on CT and EMT measurement. Additionally, the influence of implant motion on the detection ability of the system was assessed. An algorithm for the detection and specification of errors based on the error simulation results was developed. At the University Hospital Erlangen, a patient study is ongoing, where errors in patient data were analyzed using the proposed algorithm. All simulated planning errors were detected. Catheter connection swaps can be detected 100% of the time. A shift detection rate of >97% was observed for shifts larger than 1.1 mm, both in the static and the motion measurements. Catheter reconstruction uncertainties and catheter shifts <2 mm were found to be the most common treatment planning and delivery errors in patient data. HTDS proved to be a reliable method for error detection.
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