Accuracy of a Mitral Valve Segmentation Method Using J-Splines for Real-Time 3D Echocardiography Data

• Type of Material: Article
• Authors: Andrew W. Siefert, Georgia Institute of Technology; David A. Icenogle, Georgia Institute of Technology; Jean-Pierre Rabbah, Georgia Institute of Technology; Neelakantan Saikrishnan, Georgia Institute of Technology; Jarek Rossignac, Georgia Institute of Technology; Stamatios Lerakis, Emory University; Ajit Yoganathan, Emory University
• Language: English
• Date: 2013-06-01
• Publisher: Springer (part of Springer Nature): Springer Open Choice Hybrid Journals
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2013 Biomedical Engineering Society.
• Final Published Version (URL): http://dx.doi.org/10.1007/s10439-013-0784-8
• Title of Journal or Parent Work: Annals of Biomedical Engineering
• ISSN: 0090-6964
• Volume: 41
• Issue: 6
• Start Page: 1258
• End Page: 1268
• Grant/Funding Information: This study was supported by a research grant awarded by the National Institutes of Health (R01HL090661).
• Keywords: Computational methods; STRAIN; Science & Technology; Engineering, Biomedical; Engineering; In vitro simulation; 3-DIMENSIONAL TRANSESOPHAGEAL ECHOCARDIOGRAPHY; Medical imaging; SADDLE-SHAPED ANNULUS; Technology; Surgical planning; COMPLEXITY; IN-VITRO QUANTIFICATION; Heart valve; CHORDAL FORCE DISTRIBUTION; Mitral regurgitation
• Research Categories: Engineering, Biomedical

Abstract

Patient-specific models of the heart's mitral valve (MV) exhibit potential for surgical planning. While advances in 3D echocardiography (3DE) have provided adequate resolution to extract MV leaflet geometry, no study has quantitatively assessed the accuracy of their modeled leaflets vs. a ground-truth standard for temporal frames beyond systolic closure or for differing valvular dysfunctions. The accuracy of a 3DE-based segmentation methodology based on J-splines was assessed for porcine MVs with known 4D leaflet coordinates within a pulsatile simulator during closure, peak closure, and opening for a control, prolapsed, and billowing MV model. For all time points, the mean distance error between the segmented models and ground-truth data were 0.40 ± 0.32 mm, 0.52 ± 0.51 mm, and 0.74 ± 0.69 mm for the control, flail, and billowing models. For all models and temporal frames, 95% of the distance errors were below 1.64 mm. When applied to a patient data set, segmentation was able to confirm a regurgitant orifice and post-operative improvements in coaptation. This study provides an experimental platform for assessing the accuracy of an MV segmentation methodology at phases beyond systolic closure and for differing MV dysfunctions. Results demonstrate the accuracy of a MV segmentation methodology for the development of future surgical planning tools.