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Visualization of Number of Tricuspid Valve Leaflets Using Three-Dimensional Transthoracic Echocardiography.

ABSTRACT:

SUBMITTER: Karagodin I

PROVIDER: S-EPMC8026547 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Comprehensive Two-Dimensional Interrogation of the Tricuspid Valve Using Knowledge Derived from Three-Dimensional Echocardiography.

Project description:Accurate identification of tricuspid valve (TV) leaflets by two-dimensional (2D) transthoracic echocardiography is difficult because of variability in the intersection between the imaging plane and leaflets. Using information obtained from multiplanar reconstruction (MPR) of three-dimensional (3D) data sets, the investigators sought to define "novel" 2D views that would allow targeted interrogation of TV leaflets using 2D transthoracic echocardiography.Images of the TV in the standard 2D views (apical four chamber, right ventricular focused, right ventricular inflow, and parasternal short axis) and 3D data sets were acquired from the same probe position in 106 adults. Three-dimensional MPR was used to determine which leaflet combination was seen in the 2D image: anterior and septal, anterior and posterior, anterior alone, or posterior and septal. Using this analysis, 2D landmarks were identified to define nonstandard TV views tailored to depict specific leaflets. Two-dimensional images in these views and 3D data sets were then prospectively collected in 54 additional patients. Three independent readers analyzed these 2D views to determine TV leaflet combinations, and their interpretation was compared with 3D MPR-derived reference.Three-dimensional MPR views made it possible to define six nonstandard 2D views on the basis of anatomic clues and landmarks, which consistently depicted all the aforementioned leaflet combinations. When these six views were prospectively tested, the agreement of TV leaflet identification against 3D MPR was excellent (? = 0.88, ? = 0.93, and ? = 0.98).The nonstandard 2D views defined in this study allow accurate TV leaflet identification and may thus be useful when localization of TV leaflet pathology is clinically important.

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Segmentation of Tricuspid Valve Leaflets From Transthoracic 3D Echocardiograms of Children With Hypoplastic Left Heart Syndrome Using Deep Learning.

Project description:Hypoplastic left heart syndrome (HLHS) is a severe congenital heart defect in which the right ventricle and associated tricuspid valve (TV) alone support the circulation. TV failure is thus associated with heart failure, and the outcome of TV valve repair are currently poor. 3D echocardiography (3DE) can generate high-quality images of the valve, but segmentation is necessary for precise modeling and quantification. There is currently no robust methodology for rapid TV segmentation, limiting the clinical application of these technologies to this challenging population. We utilized a Fully Convolutional Network (FCN) to segment tricuspid valves from transthoracic 3DE. We trained on 133 3DE image-segmentation pairs and validated on 28 images. We then assessed the effect of varying inputs to the FCN using Mean Boundary Distance (MBD) and Dice Similarity Coefficient (DSC). The FCN with the input of an annular curve achieved a median DSC of 0.86 [IQR: 0.81-0.88] and MBD of 0.35 [0.23-0.4] mm for the merged segmentation and an average DSC of 0.77 [0.73-0.81] and MBD of 0.6 [0.44-0.74] mm for individual TV leaflet segmentation. The addition of commissural landmarks improved individual leaflet segmentation accuracy to an MBD of 0.38 [0.3-0.46] mm. FCN-based segmentation of the tricuspid valve from transthoracic 3DE is feasible and accurate. The addition of an annular curve and commissural landmarks improved the quality of the segmentations with MBD and DSC within the range of human inter-user variability. Fast and accurate FCN-based segmentation of the tricuspid valve in HLHS may enable rapid modeling and quantification, which in the future may inform surgical planning. We are now working to deploy this network for public use.

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