3D Geometric Analysis of the Pediatric Aorta in 3D MRA Follow-Up Images with Application to Aortic Coarctation

Stefan Wörz; Jens-Peter Schenk; Abdulsattar Alrajab; Hendrik von Tengg-Kobligk; Karl Rohr; Raoul Arnold

doi:10.3414/ME15-01-0104

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2016; 55(05): 455-462
DOI: 10.3414/ME15-01-0104

Original Articles

Schattauer GmbH

3D Geometric Analysis of the Pediatric Aorta in 3D MRA Follow-Up Images with Application to Aortic Coarctation

Authors

Stefan Wörz

¹University of Heidelberg, BIOQUANT, IPMB, and DKFZ Heidelberg, Department of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, Heidelberg, Germany
Jens-Peter Schenk

²Deptartment of Diagnostic and Interventional Radiology, Div. Pediatric Radiology, University of Heidelberg, Heidelberg, Germany
Abdulsattar Alrajab

²Deptartment of Diagnostic and Interventional Radiology, Div. Pediatric Radiology, University of Heidelberg, Heidelberg, Germany
Hendrik von Tengg-Kobligk

³Institute of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Universitätsspital Bern, Bern, Switzerland
Karl Rohr^*

¹University of Heidelberg, BIOQUANT, IPMB, and DKFZ Heidelberg, Department of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, Heidelberg, Germany
Raoul Arnold^*

⁴Department of Congenital Heart Disease and Pediatric Cardiology, University of Heidelberg, Heidelberg, Germany

Abstract

Summary

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.

Keywords

3D vessel segmentation - 3D model-based segmentation - cylindrical intensity model - aortic coarctation - pediatric image data - follow-up MRA - thoracic aorta

Full Text

References

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