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How Accurately Does Transesophageal Echocardiography Identify the Mitral Valve?

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 11395)

Abstract

Mitral Valve Disease (MVD) describes a variety of pathologies that cause regurgitation of blood during the systolic phase of the cardiac cycle. Decisions in valvular disease management rely heavily on non-invasive imaging. Transesophageal echocardiography (TEE) is widely recognized as the key evaluation technique where backflow of high velocity blood can be visualized under Doppler. However, the heavy reliance on TEE segmentation for diagnosis and modelling necessitates an evaluation of the accuracy of this oft-used mitral valve imaging modality. In this pilot study, we acquire simultaneous CT and TEE images of both a silicone mitral valve phantom and an iodine-stained bovine mitral valve. We propose a pipeline to use CT as ground truth to study the relationship between TEE intensities and the underlying valve morphology. Preliminary results demonstrate that even with an optimized threshold selection based solely on TEE pixel intensities, only 40% of pixels are correctly classified as part of the valve. In addition, we have shown that emphasizing the center line rather than the boundaries of the high intensity regions in TEE provides a better representation and segmentation of the valve morphology. The root mean squared distance between the TEE and CT ground truth is 1.80 mm with intensity-based segmentation and improves to 0.81 mm when comparing the center line extracted from the segmented volumes.

Keywords

Transesophageal echocardiography Mitral valve disease Ground truth phantom validation 

Notes

Acknowledgments

The authors would like to acknowledge the follow individuals for their technical expertise: Aaron So and Jennifer Hadway for acquisition and reconstruction using the Revolution CT, Joy Dunmore-Buyze for preparation of the iodine solution and Olivia Ginty for dissection and suturing of the bovine valve.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Biomedical EngineeringWestern UniversityLondonCanada
  2. 2.Department of Medical BiophysicsWestern UniversityLondonCanada
  3. 3.Robarts Research InstituteLondonCanada

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