Authors: SvenThomas Antoni Jonas Rinast Xintao Ma Sibylle Schupp Alexander Schlaefer
Publish Date: 2016/06/09
Volume: 11, Issue: 11, Pages: 2085-2096
Abstract
Correlation between internal and external motion is critical for respiratory motion compensation in radiosurgery Artifacts like coughing sneezing or yawning or changes in the breathing pattern can lead to misalignment between beam and tumor and need to be detected to interrupt the treatment We propose online model checking OMC a modelbased verification approach from the field of formal methods to verify that the breathing motion is regular and the correlation holds We demonstrate that OMC may be more suitable for artifact detection than the prediction errorWe established a sinusoidal model to apply OMC to the verification of respiratory motion The method was parameterized to detect deviations from typical breathing motion We analyzed the performance on synthetic data and on clinical episodes showing large correlation error In comparison we considered the prediction error of different stateoftheart methods based on least mean squares LMS normalized LMS nLMS waveletbased multiscale autoregression wLMS recursive least squares RLSpred and support vector regression SVRpredOn synthetic data OMC outperformed wLMS by at least 30 and SVRpred by at least 141 detecting 70 of transitions No artifacts were detected by nLMS and RLSpred On patient data OMC detected 23–49 of the episodes correctly outperforming nLMS wLMS RLSpred and SVRpred by up to 544 491 408 and 258 respectively On selected episodes OMC detected up to 94 of all eventsOMC is able to detect changes in breathing as well as artifacts which previously would have gone undetected outperforming prediction errorbased detection Synthetic data analysis supports the assumption that prediction is very insensitive to specific changes in breathing We suggest using OMC as an additional safety measure ensuring reliable and fast stopping of irradiation
Keywords: