Literature DB >> 31598891

Combining position-based dynamics and gradient vector flow for 4D mitral valve segmentation in TEE sequences.

Lennart Tautz1,2, Lars Walczak3,4, Joachim Georgii3, Amer Jazaerli4, Katharina Vellguth4, Isaac Wamala5, Simon Sündermann6,5,7, Volkmar Falk6,5,7,8, Anja Hennemuth3,4.   

Abstract

PURPOSE: For planning and guidance of minimally invasive mitral valve repair procedures, 3D+t transesophageal echocardiography (TEE) sequences are acquired before and after the intervention. The valve is then visually and quantitatively assessed in selected phases. To enable a quantitative assessment of valve geometry and pathological properties in all heart phases, as well as the changes achieved through surgery, we aim to provide a new 4D segmentation method.
METHODS: We propose a tracking-based approach combining gradient vector flow (GVF) and position-based dynamics (PBD). An open-state surface model of the valve is propagated through time to the closed state, attracted by the GVF field of the leaflet area. The PBD method ensures topological consistency during deformation. For evaluation, one expert in cardiac surgery annotated the closed-state leaflets in 10 TEE sequences of patients with normal and abnormal mitral valves, and defined the corresponding open-state models.
RESULTS: The average point-to-surface distance between the manual annotations and the final tracked model was [Formula: see text]. Qualitatively, four cases were satisfactory, five passable and one unsatisfactory. Each sequence could be segmented in 2-6 min.
CONCLUSION: Our approach enables to segment the mitral valve in 4D TEE image data with normal and pathological valve closing behavior. With this method, in addition to the quantification of the remaining orifice area, shape and dimensions of the coaptation zone can be analyzed and considered for planning and surgical result assessment.

Entities:  

Keywords:  Echocardiography; Mitral valve; Position-based dynamics; Segmentation; Tracking

Mesh:

Year:  2019        PMID: 31598891     DOI: 10.1007/s11548-019-02071-4

Source DB:  PubMed          Journal:  Int J Comput Assist Radiol Surg        ISSN: 1861-6410            Impact factor:   2.924


  16 in total

1.  Automatic mitral annulus tracking in volumetric ultrasound using non-rigid image registration.

Authors:  Henri De Veene; Philippe B Bertrand; Natasa Popovic; Pieter M Vandervoort; Piet Claus; Matthieu De Beule; Brecht Heyde
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2015

2.  Patient-specific mitral leaflet segmentation from 4D ultrasound.

Authors:  Robert J Schneider; Neil A Tenenholtz; Douglas P Perrin; Gerald R Marx; Pedro J del Nido; Robert D Howe
Journal:  Med Image Comput Comput Assist Interv       Date:  2011

3.  Semi-automated segmentation and quantification of mitral annulus and leaflets from transesophageal 3-D echocardiographic images.

Authors:  Miguel Sotaquira; Mauro Pepi; Laura Fusini; Francesco Maffessanti; Roberto M Lang; Enrico G Caiani
Journal:  Ultrasound Med Biol       Date:  2014-10-22       Impact factor: 2.998

4.  User-dependent variability in mitral valve segmentation and its impact on CFD-computed hemodynamic parameters.

Authors:  Katharina Vellguth; Jan Brüning; Lennart Tautz; Franziska Degener; Isaac Wamala; Simon Sündermann; Ulrich Kertzscher; Titus Kuehne; Anja Hennemuth; Volkmar Falk; Leonid Goubergrits
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-06-19       Impact factor: 2.924

5.  Extraction of open-state mitral valve geometry from CT volumes.

Authors:  Lennart Tautz; Mathias Neugebauer; Markus Hüllebrand; Katharina Vellguth; Franziska Degener; Simon Sündermann; Isaac Wamala; Leonid Goubergrits; Titus Kuehne; Volkmar Falk; Anja Hennemuth
Journal:  Int J Comput Assist Radiol Surg       Date:  2018-08-03       Impact factor: 2.924

6.  Patient-specific modeling and quantification of the aortic and mitral valves from 4-D cardiac CT and TEE.

Authors:  Razvan Ioan Ionasec; Ingmar Voigt; Bogdan Georgescu; Yang Wang; Helene Houle; Fernando Vega-Higuera; Nassir Navab; Dorin Comaniciu
Journal:  IEEE Trans Med Imaging       Date:  2010-05-03       Impact factor: 10.048

7.  Modeling the Myxomatous Mitral Valve With Three-Dimensional Echocardiography.

Authors:  Alison M Pouch; Benjamin M Jackson; Eric Lai; Manabu Takebe; Sijie Tian; Albert T Cheung; Y Joseph Woo; Prakash A Patel; Hongzhi Wang; Paul A Yushkevich; Robert C Gorman; Joseph H Gorman
Journal:  Ann Thorac Surg       Date:  2016-08-01       Impact factor: 4.330

8.  Personalized mitral valve closure computation and uncertainty analysis from 3D echocardiography.

Authors:  Sasa Grbic; Thomas F Easley; Tommaso Mansi; Charles H Bloodworth; Eric L Pierce; Ingmar Voigt; Dominik Neumann; Julian Krebs; David D Yuh; Morten O Jensen; Dorin Comaniciu; Ajit P Yoganathan
Journal:  Med Image Anal       Date:  2016-05-17       Impact factor: 8.545

9.  Signal dropout correction-based ultrasound segmentation for diastolic mitral valve modeling.

Authors:  Wenyao Xia; John Moore; Elvis C S Chen; Yuanwei Xu; Olivia Ginty; Daniel Bainbridge; Terry M Peters
Journal:  J Med Imaging (Bellingham)       Date:  2018-02-09

10.  Spatiotemporal Segmentation and Modeling of the Mitral Valve in Real-Time 3D Echocardiographic Images.

Authors:  Alison M Pouch; Ahmed H Aly; Eric K Lai; Natalie Yushkevich; Rutger H Stoffers; Joseph H Gorman; Albert T Cheung; Joseph H Gorman; Robert C Gorman; Paul A Yushkevich
Journal:  Med Image Comput Comput Assist Interv       Date:  2017-09-04
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  1 in total

Review 1.  Intraoperative transesophageal echocardiography following mitral valve repair: a systematic review.

Authors:  Raffael Zamper; Agya Prempeh; Ivan Iglesias; Ashraf Fayad
Journal:  Braz J Anesthesiol       Date:  2022-03-14
  1 in total

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