Reducing false discovery rates for on-line model checking based detection of respiratory motion artifacts

First published in
CEUR workshop proceedings  
Number in series
1559
Volume
1559
Start Page
182
End Page
186
Citation
SE-WS 2016, software engineering workshops 2016, Gemeinsamer Tagungsband der Workshops der Tagung Software Engineering, CEUR Workshop Proceedings (1559): 182-186 (2016)
Contribution to Conference
Conference on Software Engineering, SE-WS 2016; 2nd Workshop on Fail Safety in Medical Cyber-Physical Systems (FS-MCPS)  
Publisher
RWTH Aachen
Compensating respiratory motion in radiosurgery is an important problem and can lead to a more focused dose delivered to the patient. We previously showed the negative effect of respiratory artifacts on the error of the correlation model, connecting external and internal motion, for meaningful episodes from treatments with the Accuray CyberKnifer. We applied on-line model checking, an iterative fail safety method, to respiratory motion. In this paper we vary its prediction parameter and decrease the previously rather high false discovery rate by 30.3%. In addition, we were able to increase the number of detected meaningful episodes through adaptive parameter choice by 452%.
Subjects
Event detection
Fail safety
ON-line model checking
Prediction
Radiosurgery
Respiratory motion compensation
Stereotactic body radiation therapy
DDC Class
004: Informatik
600: Technik
610: Medizin