A tool to automatically analyze electromagnetic tracking data from high dose rate brachytherapy of breast cancer patients

• Published in: PloS one Vol. 12; no. 9; p. e0183608
• Main Authors: Götz, Th. I., Lahmer, G., Strnad, V., Bert, Ch, Hensel, B., Tomé, A. M., Lang, E. W.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 21.09.2017; Public Library of Science (PLoS)
• Subjects: Accuracy; Aged; Anatomy; Automation; Biology and Life Sciences; Biophysics; Brachytherapy; Brachytherapy - instrumentation; Breast cancer; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - radiotherapy; Breasts; Care and treatment; Catheters; Computed tomography; Computer and Information Sciences; Data processing; Decomposition; Diagnosis; Dosage; Electromagnetic Phenomena; Electromagnetism; Empirical analysis; Engineering and Technology; Female; Health physics; Humans; Image Processing, Computer-Assisted; Information theory; Irradiation; Learning algorithms; Machine learning; Magnetic fields; Male; Medical imaging; Medical instruments; Medicine and Health Sciences; Methods; Middle Aged; Motion; Oncology; Phantoms, Imaging; Physical Sciences; Physics; Radiation; Radiation Dosage; Radiation sources; Radiation therapy; Radiotherapy Dosage; Radiotherapy Planning, Computer-Assisted; Registration; Research and Analysis Methods; Scaling; Sensors; Signal processing; Similarity measures; Spectrum analysis; Time series; Tomography, X-Ray Computed; Tracking; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0183608

During High Dose Rate Brachytherapy (HDR-BT) the spatial position of the radiation source inside catheters implanted into a female breast is determined via electromagnetic tracking (EMT). Dwell positions and dwell times of the radiation source are established, relative to the patient's anatomy, from an initial X-ray-CT-image. During the irradiation treatment, catheter displacements can occur due to patient movements. The current study develops an automatic analysis tool of EMT data sets recorded with a solenoid sensor to assure concordance of the source movement with the treatment plan. The tool combines machine learning techniques such as multi-dimensional scaling (MDS), ensemble empirical mode decomposition (EEMD), singular spectrum analysis (SSA) and particle filter (PF) to precisely detect and quantify any mismatch between the treatment plan and actual EMT measurements. We demonstrate that movement artifacts as well as technical signal distortions can be removed automatically and reliably, resulting in artifact-free reconstructed signals. This is a prerequisite for a highly accurate determination of any deviations of dwell positions from the treatment plan.