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Verfasst von:Wörz, Stefan [VerfasserIn]   i
 Schenk, Jens-Peter [VerfasserIn]   i
 Alrajab, Abdulsattar [VerfasserIn]   i
 Tengg-Kobligk, Hendrik von [VerfasserIn]   i
 Rohr, Karl [VerfasserIn]   i
 Arnold, Raoul [VerfasserIn]   i
Titel:3D geometric analysis of the pediatric aorta in 3D MRA follow-up images with application to aortic coarctation
Verf.angabe:Stefan Wörz, Jens-Peter Schenk, Abdulsattar Alrajab, Hendrik von Tengg-Kobligk, Karl Rohr, Raoul Arnold
Jahr:2016
Umfang:8 S.
Teil:volume:55
 year:2016
 number:05
 pages:455-462
 extent:8
Fussnoten:Elektronische Reproduktion der Druckausgabe ; Gesehen am 10.09.2021
Titel Quelle:Enthalten in: Methods of information in medicine
Ort Quelle:Stuttgart : Thieme, 1962
Jahr Quelle:2016
Band/Heft Quelle:55(2016), 05, Seite 455-462
ISSN Quelle:2511-705X
Abstract:Background: Coarctation of the aorta is one of the most common congenital heart diseases. Despite different treatment opportunities, long-term outcome after surgical or interventional therapy is diverse. Serial morphologic follow-up of vessel growth is necessary, because vessel growth cannot be predicted by primer morphology or a therapeutic option. Objectives: For the analysis of the long-term outcome after therapy of congenital diseases such as aortic coarctation, accurate 3D geometric analysis of the aorta from follow-up 3D medical image data such as magnetic resonance angiography (MRA) is important. However, for an objective, fast, and accurate 3D geometric analysis, an automatic approach for 3D segmentation and quantification of the aorta from pediatric images is required. Methods: We introduce a new model-based approach for the segmentation of the thoracic aorta and its main branches from follow-up pediatric 3D MRA image data. For robust segmentation of vessels even in difficult cases (e.g., neighboring structures), we propose a new extended parametric cylinder model that requires only relatively few model parameters. Moreover, we include a novel adaptive background-masking scheme used for least-squares model fitting, we use a spatial normalization scheme to align the segmentation results from follow-up examinations, and we determine relevant 3D geometric parameters of the aortic arch. Results: We have evaluated our proposed approach using different 3D synthetic images. Moreover, we have successfully applied the approach to follow-up pediatric 3D MRA image data, we have normalized the 3D segmentation results of follow-up images of individual patients, and we have combined the results of all patients. We also present a quantitative evaluation of our approach for four follow-up 3D MRA images of a patient, which confirms that our approach yields accurate 3D segmentation results. An experimental comparison with two previous approaches demonstrates that our approach yields superior results. Conclusions: From the results, we found that our approach is well suited for the quantification of the 3D geometry of the aortic arch from follow-up pediatric 3D MRA image data. In future work, this will enable to investigate the long-term outcome of different surgical and interventional therapies for aortic coarctation.
DOI:doi:10.3414/ME15-01-0104
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag: https://doi.org/10.3414/ME15-01-0104
 Volltext: http://www.thieme-connect.de/DOI/DOI?10.3414/ME15-01-0104
 DOI: https://doi.org/10.3414/ME15-01-0104
Datenträger:Online-Ressource
Sprache:eng
K10plus-PPN:1769995595
Verknüpfungen:→ Zeitschrift

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