DC FieldValueLanguage
dc.contributor.authorAntoni, Sven-Thomas-
dc.contributor.authorRinast, Jonas-
dc.contributor.authorMa, Xintao-
dc.contributor.authorSchupp, Sibylle-
dc.contributor.authorSchlaefer, Alexander-
dc.date.accessioned2020-03-26T13:47:32Z-
dc.date.available2020-03-26T13:47:32Z-
dc.date.issued2016-06-09-
dc.identifier.citationInternational Journal of Computer Assisted Radiology and Surgery 11 (11): 2085-2096 (2016)de_DE
dc.identifier.issn1861-6429de_DE
dc.identifier.urihttp://hdl.handle.net/11420/5517-
dc.description.abstractObjective 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 model-based 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 error. Materials and methods: We 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 state-of-the-art methods based on least mean squares (LMS; normalized LMS, nLMS; wavelet-based multiscale autoregression, wLMS), recursive least squares (RLSpred) and support vector regression (SVRpred). Results: On 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 events. Conclusion: OMC is able to detect changes in breathing as well as artifacts which previously would have gone undetected, outperforming prediction error-based 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.en
dc.language.isoende_DE
dc.publisherSpringerde_DE
dc.relation.ispartofInternational journal of computer assisted radiology and surgeryde_DE
dc.subjectinternal–external correlationde_DE
dc.subjectmodel checkingde_DE
dc.subjectpredictionde_DE
dc.subjectradiosurgeryde_DE
dc.subjectrespirationde_DE
dc.subject.ddc500: Naturwissenschaftende_DE
dc.subject.ddc570: Biowissenschaften, Biologiede_DE
dc.subject.ddc610: Medizinde_DE
dc.titleOnline model checking for monitoring surrogate-based respiratory motion tracking in radiation therapyde_DE
dc.typeArticlede_DE
dc.type.diniarticle-
dcterms.DCMITypeText-
tuhh.abstract.englishObjective 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 model-based 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 error. Materials and methods: We 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 state-of-the-art methods based on least mean squares (LMS; normalized LMS, nLMS; wavelet-based multiscale autoregression, wLMS), recursive least squares (RLSpred) and support vector regression (SVRpred). Results: On 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 events. Conclusion: OMC is able to detect changes in breathing as well as artifacts which previously would have gone undetected, outperforming prediction error-based 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.de_DE
tuhh.publisher.doi10.1007/s11548-016-1423-2-
tuhh.publication.instituteProzess- und Anlagentechnik V-4de_DE
tuhh.publication.instituteSoftwaresysteme E-16de_DE
tuhh.publication.instituteMedizintechnische Systeme E-1de_DE
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.type.driverarticle-
dc.type.casraiJournal Article-
tuhh.container.issue11de_DE
tuhh.container.volume11de_DE
tuhh.container.startpage2085de_DE
tuhh.container.endpage2096de_DE
local.status.inpressfalsede_DE
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextNo Fulltext-
item.creatorOrcidAntoni, Sven-Thomas-
item.creatorOrcidRinast, Jonas-
item.creatorOrcidMa, Xintao-
item.creatorOrcidSchupp, Sibylle-
item.creatorOrcidSchlaefer, Alexander-
item.openairetypeArticle-
item.languageiso639-1en-
item.creatorGNDAntoni, Sven-Thomas-
item.creatorGNDRinast, Jonas-
item.creatorGNDMa, Xintao-
item.creatorGNDSchupp, Sibylle-
item.creatorGNDSchlaefer, Alexander-
item.grantfulltextnone-
crisitem.author.deptMedizintechnische Systeme E-1-
crisitem.author.deptSoftwaresysteme E-16-
crisitem.author.deptSoftwaresysteme E-16-
crisitem.author.deptSoftwaresysteme E-16-
crisitem.author.deptMedizintechnische Systeme E-1-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
Appears in Collections:Publications without fulltext
Show simple item record

Page view(s)

23
Last Week
0
Last month
9
checked on May 28, 2020

Google ScholarTM

Check

Add Files to Item

Note about this record

Export

Items in TORE are protected by copyright, with all rights reserved, unless otherwise indicated.