Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/25086
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dc.contributor.authorBamps, Kobe-
dc.contributor.authorCuypers, Céline-
dc.contributor.authorCLAESEN, Luc-
dc.contributor.authorKOOPMAN, Pieter-
dc.date.accessioned2017-10-23T14:22:46Z-
dc.date.available2017-10-23T14:22:46Z-
dc.date.issued2017-
dc.identifier.citationWang, Lipo; Zhou, Mei; Sun, Li; Qiu, Song; Liu, Hongying (Ed.). Proceedings 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics CISP-BMEI 2017, IEEE,p. 4B-1-4B-6-
dc.identifier.isbn9781538619377-
dc.identifier.urihttp://hdl.handle.net/1942/25086-
dc.description.abstractAtrial fibrillation is a cardiac arrhythmia that causes irregular contraction of the atria. It is caused by parasitic electric signals via the pulmonary veins. Current treatment methods involve point-by-point RF ablation, cryoablation or laser balloon ablation. Ablation creates a circumferential lesion resulting in an electrical isolation of the pulmonary veins, thereby disabling the parasitic signals causing atrial fibrillation. During ablation there is a danger that the right phrenic nerve (PN) is damaged, having a serious impact on the respiratory capability of the patient. This paper presents new automatic image processing methods and algorithms to identify and localize the right phrenic nerve starting from high quality CT scans. Nerve tissue is nearly undetectable from CT scan images. To select the most probable locations of PN candidates, a new algorithm: EXSAC is proposed. Based on CT scans of 27 test cases, the PN could be automatically identified in 89% of the cases, in comparison to the manual localization by a heart surgeon.-
dc.description.sponsorshipThis research was conducted in part by the financial support from “Heart Center Hasselt”.-
dc.language.isoen-
dc.publisherIEEE-
dc.rights2017 IEEE-
dc.subject.otherphrenic nerve; atrial fibrillation; pulmonary vein isolation; laser balloon ablation; cardiac arrythmia; DICOM, CT scan; medical image processing; RANSAC-
dc.titleCT-based Automatic Identification and Localization of the Right Phrenic Nerve-
dc.typeProceedings Paper-
local.bibliographicCitation.authorsWang, Lipo-
local.bibliographicCitation.authorsZhou, Mei-
local.bibliographicCitation.authorsSun, Li-
local.bibliographicCitation.authorsQiu, Song-
local.bibliographicCitation.authorsLiu, Hongying-
local.bibliographicCitation.conferencedate14-16/10/2017-
local.bibliographicCitation.conferencename2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics( CISP-BMEI 2017)-
local.bibliographicCitation.conferenceplaceShanghai, China-
dc.identifier.epage4B-6-
dc.identifier.spage4B-1-
local.bibliographicCitation.jcatC1-
local.publisher.placeNew York-
dc.relation.references[1] C. Cuypers, K. Bamps, “Automated identification and reconstruction of the right phrenic nerve on computed tomography”, Master’s Thesis Engineering Technology, UHasselt-KULeuven Belgium, June 2016. [2] Mathworks, “MathWorks - Makers of MATLAB and Simulink”, https://nl.mathworks.com/. [Accessed: 06-Apr-2017]. [3] Hamilton Cardiology Associates, “Atrial Fibrillation – Hamilton Cardiology Associates – New Jersey’s Leading Board Certified Cardiologists.”, https://www.hcahamilton.com/atrial-fibrillation. [Accessed: 24-May-2017]. [4] Osmosis, “Atrial fibrillation (A-fib, AF) - causes, symptoms, treatment & pathology”, 2016. [5] StopAfib.org, “Atrial Fibrillation Catheter Ablation Technology—Balloon Catheters”, 2012. http://www.stopafib.org/catheter-ablation/technology-balloon.cfm. [Accessed: 05-Apr-2017]. [6] G.-B. Chierchia et al., “Impact on Clinical Outcome of Premature Interruption of Cryoenergy Delivery Due to Phrenic Nerve Palsy During Second Generation Cryoballoon Ablation for Paroxysmal Atrial Fibrillation”, J. Cardiovasc. Electrophysiol., vol. 26, no. 9, pp. 950–955, Sep. 2015. [7] S. R. Dukkipati et al., “Pulmonary Vein Isolation Using a Visually Guided Laser Balloon Catheter: The First 200-Patient Multicenter Clinical Experience”, Circ. Arrhythmia Electrophysiol., vol. 6, no. 3, pp. 467–472, Jun. 2013. [8] prof. dr. J. W. S. prof. dr. J.B.M. Kuks, Klinische Neurologie. 2012. [9] S. L. Aquino, G. R. Duncan, and A. L. Hayman, “Nerves of the Thorax: Atlas of Normal and Pathologic Findings”, RadioGraphics, vol. 21, pp. 1275–1281, 2001. [10] B. Schmidt, K. R. J. Chun, F. Ouyang, A. Metzner, M. Antz, and K.-H. Kuck, “Three-dimensional reconstruction of the anatomic course of the right phrenic nerve in humans by pace mapping”, Hear. Rhythm, vol. 5, no. 8, pp. 1120–1126, 2008. [11] R. Horton et al., “Locating the right phrenic nerve by imaging the right pericardiophrenic artery with computerized tomographic angiography: Implications for balloon-based procedures”, Hear. Rhythm, vol. 7, no. 7, pp. 937–941, 2010. [12] Rafael C. Gonzalez, Richard E.Woods, and S. L. Eddins, “Digital image processing using Matlab”, Ceit.Aut.Ac.Ir. p. 609, 2009. [13] MathWorks, “Edge Detection - MATLAB & Simulink”, https://nl.mathworks.com/discovery/edge-detection.html. [Accessed: 07-Apr-2017]. [14] R. Schnabel, R. Wahl, and R. Klein, “Efficient RANSAC for Point-Cloud Shape Detection”, Comput. Graph. Forum, vol. 26, no. 2, pp. 214–226, Jun. 2007. [15] J. D. Foley, M. A. Fischler, and R. C. Bolles, “Graphics and Image Processing Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography”, 1981. [16] NEMA, “Digital Imaging and Communications in Medicine”, http://dicom.nema.org/. [Accessed: 07-Apr-2017]. [17] D. R. Varma, “Managing DICOM images: Tips and tricks for the radiologist.”, Indian J. Radiol. Imaging, vol. 22, no. 1, pp. 4–13, Jan. 2012. [18] MathWorks, “Read DICOM image - MATLAB dicomread - MathWorks Benelux”, https://nl.mathworks.com/help/images/ref/dicomread.html. [Accessed: 06-Apr-2017]. [19] B. (Philips) Revet, “DICOM Cook Book for Implementations in Modalities”, vol. 14, 1997. [20] R. Kumar, “Diffusion Filtering for Image Denoising - File Exchange - MATLAB Central”, 2010. http://nl.mathworks.com/matlabcentral/fileexchange/28112-diffusion-filtering-for-image-denoising?focused=5167065&tab=function. [Accessed: 12-May-2017]. [21] Eli Billauer, “peakdet: Peak detection using MATLAB”, 2012. [Online]. Available: http://billauer.co.il/peakdet.html. [Accessed: 24-Mar-2017] .-
local.type.refereedRefereed-
local.type.specifiedProceedings Paper-
dc.identifier.doi10.1109/CISP-BMEI.2017.8302179-
dc.identifier.isi000464407100280-
local.bibliographicCitation.btitleProceedings 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics CISP-BMEI 2017-
item.contributorBamps, Kobe-
item.contributorCuypers, Céline-
item.contributorCLAESEN, Luc-
item.contributorKOOPMAN, Pieter-
item.fullcitationBamps, Kobe; Cuypers, Céline; CLAESEN, Luc & KOOPMAN, Pieter (2017) CT-based Automatic Identification and Localization of the Right Phrenic Nerve. In: Wang, Lipo; Zhou, Mei; Sun, Li; Qiu, Song; Liu, Hongying (Ed.). Proceedings 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics CISP-BMEI 2017, IEEE,p. 4B-1-4B-6.-
item.accessRightsRestricted Access-
item.fulltextWith Fulltext-
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