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Article: An Efficient Cardiac Mapping Strategy for Radiofrequency Catheter Ablation with Active Learning

TitleAn Efficient Cardiac Mapping Strategy for Radiofrequency Catheter Ablation with Active Learning
Authors
KeywordsActive learning
Cardiac mapping
Catheter robot guidance
Learning from demonstration
Radiofrequency catheter ablation
Issue Date2017
PublisherSpringer Verlag. The Journal's web site is located at http://www.springer.com/medicine/radiology/journal/11548
Citation
International Journal of Computer Assisted Radiology and Surgery, 2017, v. 12 n. 7, p. 1199-1207 How to Cite?
AbstractObjective A major challenge in radiofrequency catheter ablation procedures is the voltage and activation mapping of the endocardium, given a limited mapping time. By learning from expert interventional electrophysiologists (operators), while also making use of an active-learning framework, guidance on performing cardiac voltage mapping can be provided to novice operators or even directly to catheter robots. Methods A learning from demonstration (LfD) framework, based upon previous cardiac mapping procedures performed by an expert operator, in conjunction with Gaussian process (GP) model-based active learning, was developed to efficiently perform voltage mapping over right ventricles (RV). The GP model was used to output the next best mapping point, while getting updated towards the underlying voltage data pattern as more mapping points are taken. A regularized particle filter was used to keep track of the kernel hyperparameter used by GP. The travel cost of the catheter tip was incorporated to produce time-efficient mapping sequences. Results The proposed strategy was validated on a simulated 2D grid mapping task, with leave-one-out experiments on 25 retrospective datasets, in an RV phantom using the Stereotaxis Niobe® remote magnetic navigation system, and on a tele-operated catheter robot. In comparison with an existing geometry-based method, regression error was reduced and was minimized at a faster rate over retrospective procedure data. Conclusion A new method of catheter mapping guidance has been proposed based on LfD and active learning. The proposed method provides real-time guidance for the procedure, as well as a live evaluation of mapping sufficiency.
Persistent Identifierhttp://hdl.handle.net/10722/243136
ISSN
2017 Impact Factor: 1.961
2015 SCImago Journal Rankings: 0.539
PubMed Central ID
ISI Accession Number ID
Errata

 

DC FieldValueLanguage
dc.contributor.authorFeng, Y-
dc.contributor.authorGuo, Z-
dc.contributor.authorDong, Z-
dc.contributor.authorZhou, XY-
dc.contributor.authorErnst, S-
dc.contributor.authorLee, SL-
dc.date.accessioned2017-08-25T02:50:31Z-
dc.date.available2017-08-25T02:50:31Z-
dc.date.issued2017-
dc.identifier.citationInternational Journal of Computer Assisted Radiology and Surgery, 2017, v. 12 n. 7, p. 1199-1207-
dc.identifier.issn1861-6410-
dc.identifier.urihttp://hdl.handle.net/10722/243136-
dc.description.abstractObjective A major challenge in radiofrequency catheter ablation procedures is the voltage and activation mapping of the endocardium, given a limited mapping time. By learning from expert interventional electrophysiologists (operators), while also making use of an active-learning framework, guidance on performing cardiac voltage mapping can be provided to novice operators or even directly to catheter robots. Methods A learning from demonstration (LfD) framework, based upon previous cardiac mapping procedures performed by an expert operator, in conjunction with Gaussian process (GP) model-based active learning, was developed to efficiently perform voltage mapping over right ventricles (RV). The GP model was used to output the next best mapping point, while getting updated towards the underlying voltage data pattern as more mapping points are taken. A regularized particle filter was used to keep track of the kernel hyperparameter used by GP. The travel cost of the catheter tip was incorporated to produce time-efficient mapping sequences. Results The proposed strategy was validated on a simulated 2D grid mapping task, with leave-one-out experiments on 25 retrospective datasets, in an RV phantom using the Stereotaxis Niobe® remote magnetic navigation system, and on a tele-operated catheter robot. In comparison with an existing geometry-based method, regression error was reduced and was minimized at a faster rate over retrospective procedure data. Conclusion A new method of catheter mapping guidance has been proposed based on LfD and active learning. The proposed method provides real-time guidance for the procedure, as well as a live evaluation of mapping sufficiency.-
dc.languageeng-
dc.publisherSpringer Verlag. The Journal's web site is located at http://www.springer.com/medicine/radiology/journal/11548-
dc.relation.ispartofInternational Journal of Computer Assisted Radiology and Surgery-
dc.rightsThe final publication is available at Springer via http://dx.doi.org/[insert DOI]-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectActive learning-
dc.subjectCardiac mapping-
dc.subjectCatheter robot guidance-
dc.subjectLearning from demonstration-
dc.subjectRadiofrequency catheter ablation-
dc.titleAn Efficient Cardiac Mapping Strategy for Radiofrequency Catheter Ablation with Active Learning-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1007/s11548-017-1587-4-
dc.identifier.pmcidPMC5509834-
dc.identifier.scopuseid_2-s2.0-85018745940-
dc.identifier.hkuros274301-
dc.identifier.hkuros292005-
dc.identifier.volume12-
dc.identifier.issue7-
dc.identifier.spage1199-
dc.identifier.epage1207-
dc.identifier.isiWOS:000405708200013-
dc.publisher.placeGermany-
dc.relation.erratumdoi:10.1007/s11548-017-1635-0-

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