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In Vivo Image-Based 4D Modeling of Competent and Regurgitant Mitral Valve Dynamics

  • Sp Iss: Experimental Advances in Cardiovascular Biomechanics
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Abstract

Background

In vivo characterization of mitral valve dynamics relies on image analysis algorithms that accurately reconstruct valve morphology and motion from clinical images. The goal of such algorithms is to provide patient-specific descriptions of both competent and regurgitant mitral valves, which can be used as input to biomechanical analyses and provide insights into the pathophysiology of diseases like ischemic mitral regurgitation (IMR).

Objective

The goal is to generate accurate image-based representations of valve dynamics that visually and quantitatively capture normal and pathological valve function.

Methods

We present a novel framework for 4D segmentation and geometric modeling of the mitral valve in real-time 3D echocardiography (rt-3DE), an imaging modality used for pre-operative surgical planning of mitral interventions. The framework integrates groupwise multi-atlas label fusion and template-based medial modeling with Kalman filtering to generate quantitatively descriptive and temporally consistent models of valve dynamics.

Results

The algorithm is evaluated on rt-3DE data series from 28 patients: 14 with normal mitral valve morphology and 14 with severe IMR. In these 28 data series that total 613 individual 3DE images, each 3D mitral valve segmentation is validated against manual tracing, and temporal consistency between segmentations is demonstrated.

Conclusions

Automated 4D image analysis allows for reliable non-invasive modeling of the mitral valve over the cardiac cycle for comparison of annular and leaflet dynamics in pathological and normal mitral valves. Future studies can apply this algorithm to cardiovascular mechanics applications, including patient-specific strain estimation, fluid dynamics simulation, inverse finite element analysis, and risk stratification for surgical treatment.

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Acknowledgements

This research was supported by the National Institutes of Health: EB017255 from the National Institute of Biomedical Imaging and Bioengineering, HL073021, HL142504, HL103723, HL141643, and HL142138 from the National Heart Lung and Blood Institute.

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Authors and Affiliations

  1. Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA

    A. H. Aly, A. H. Aly, N. Yushkevich, P. A. Yushkevich & A. M. Pouch

  2. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA

    A. H. Aly & A. M. Pouch

  3. Gorman Cardiovascular Research Group, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

    E. K. Lai, J. H. Gorman IV, J. H. Gorman III & R. C. Gorman

  4. University of Groningen, Groningen, Netherlands

    R. H. Stoffers

  5. Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University Medical Center, Stanford, CA, USA

    A. T. Cheung

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Contributions

All authors contributed to the study conception and design and have approved the final manuscript. The collection and analysis of human image data was approved by the Institutional Review Board at the University of Pennsylvania.

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Correspondence to A. M. Pouch.

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Aly, A.H., Aly, A.H., Lai, E.K. et al. In Vivo Image-Based 4D Modeling of Competent and Regurgitant Mitral Valve Dynamics. Exp Mech 61, 159–169 (2021). https://doi.org/10.1007/s11340-020-00656-8

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