|Title: Reducing false discovery rates for on-line model-checking based detection of respiratory motion artifacts Gemeinsamer Tagungsband der Workshops der Tagung Software Engineering 2016 (SE 2016), Wien, Feb. 2016|
|Written by: S.-T. Antoni and X. Ma and S. Schupp and A. Schlaefer|
|in: February 2016|
|on pages: 182-186|
Abstract: 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 CyberKnife(R). 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%.