Automated Segmentation and Geometrical Modeling of the Tricuspid Aortic Valve in 3D Echocardiographic Images

Pouch, Alison M.; Wang, Hongzhi; Takabe, Manabu; Jackson, Benjamin M.; Sehgal, Chandra M.; Gorman, Joseph H.; Gorman, Robert C.; Yushkevich, Paul A.

doi:10.1007/978-3-642-40811-3_61

Cited by 18 publications

(18 citation statements)

References 15 publications

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“…For the former, we note that it has been extensively validated and known to produce accurate results when compared to the manual segmentation (Jassar et al, 2014; Pouch et al, 2015b; Pouch et al, 2013; Witschey et al, 2014). For the later, we have extensively validated the method against gold-standard in-vitro data with accuracies to ±0.2 mm (Aggarwal et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

In-vivo heterogeneous functional and residual strains in human aortic valve leaflets

Aggarwal

Pouch

Lai

et al. 2016

Journal of Biomechanics

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Residual and physiological functional strains in soft tissues are known to play an important role in modulating organ stress distributions. Yet, no known comprehensive information on residual strains exist, or non-invasive techniques to quantify in-vivo deformations for the aortic valve (AV) leaflets. Herein we present a completely non-invasive approach for determining heterogeneous strains – both functional and residual – in semilunar valves and apply it to normal human AV leaflets. Transesophageal 3D echocardiographic (3DE) images of the AV were acquired from open-heart transplant patients, with each AV leaflet excised after heart explant and then imaged in a flattened configuration ex-vivo. Using an established spline parameterization of both 3DE segmentations and digitized ex-vivo images (Aggarwal et al., 2014), surface strains were calculated for deformation between the ex-vivo and three in-vivo configurations: fully open, just-coapted, and fully-loaded. Results indicated that leaflet area increased by an average of 20% from the ex-vivo to in-vivo open states, with a highly heterogeneous strain field. The increase in area from open to just-coapted state was the highest at an average of 25%, while that from just-coapted to fully-loaded remained almost unaltered. Going from the ex-vivo to in-vivo mid-systole configurations, the leaflet area near the basal attachment shrank slightly, whereas the free edge expanded by ~10%. This was accompanied by a 10° −20° shear along the circumferential-radial direction. Moreover, the principal stretches aligned approximately with the circumferential and radial directions for all cases, with the highest stretch being along the radial direction. Collectively, these results indicated that even though the AV did not support any measurable pressure gradient in the just-coapted state, the leaflets were significantly pre-strained with respect to the excised state. Furthermore, the collagen fibers of the leaflet were almost fully recruited in the just-coapted state, making the leaflet very stiff with marginal deformation under full pressure. Lastly, the deformation was always higher in the radial direction and lower along the circumferential one, the latter direction made stiffer by the preferential alignment of collagen fibers. These results provide significant insight into the distribution of residual strains and the in-vivo strains encountered during valve opening and closing in AV leaflets, and will form an important component of the tool that can evaluate valve's functional properties in a non-invasive manner.

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Section: Discussionmentioning

confidence: 99%

In-vivo heterogeneous functional and residual strains in human aortic valve leaflets

Aggarwal

Pouch

Lai

et al. 2016

Journal of Biomechanics

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“…3) The image analysis method was applied to the 3D image of the remaining patient not included in the training set, to automatically reconstruct the AV geometry. 4) The reconstructed geometry was compared to that manually created by human experts using the point-to-mesh distance metric (18) to calculate the reconstruction error, assuming the manually created meshes as the ground truth. The point-to-mesh distance metric between two surface meshes S1 and S2 is the average distance between a point (on S1, S2) and a surface (S2, S1).…”

Section: Methodsmentioning

confidence: 99%

“…Pouch et.al. (18) proposed a method based on intensity-based image registration to estimate aortic leaflet shapes from 3D echocardiographic images requiring only three landmarks chosen by the user. Ionasec et al (19) proposed a new method for geometry reconstruction of the mitral-aortic complex from 3D CT images, utilizing a large number of aortic valve shapes (more than 600) manually delineated from 3D images to serve as training data.…”

Section: Introductionmentioning

confidence: 99%

Machine learning–based 3‐D geometry reconstruction and modeling of aortic valve deformation using 3‐D computed tomography images

Liang

Kong

Martin

et al. 2016

Numer Methods Biomed Eng

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To conduct a patient-specific computational modeling of the aortic valve, 3D aortic valve anatomic geometries of an individual patient need to be reconstructed from clinical 3D cardiac images. Currently, most of computational studies involve manual heart valve geometry reconstruction and manual FE model generation, which is both time-consuming and prone to human errors. A seamless computational modeling framework, which can automate this process based on machine learning algorithms, is desirable, as it can not only eliminate human errors and ensure the consistency of the modeling results, but also allows fast feedback to clinicians and permits a future population-based probabilistic analysis of large patient cohorts. In this study, we developed a novel computational modeling method to automatically reconstruct the 3D geometries of the aortic valve from CT images. The reconstructed valve geometries have built-in mesh correspondence, which bridges harmonically for the consequent FE modeling. The proposed method was evaluated by comparing the reconstructed geometries from ten patients to those manually created by human experts, and a mean discrepancy of 0.69 mm was obtained. Based on these reconstructed geometries, FE models of valve leaflets were developed, and aortic valve closure from end systole to mid-diastole was simulated for seven patients and validated by comparing the deformed geometries to those manually created by human experts, and a mean discrepancy of 1.57 mm was obtained. The proposed method offers great potential to streamline the computational modeling process and enables the development of a pre-operative planning system for aortic valve disease diagnosis and treatment.

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“…Now, many groups are using semiautomatic methods to segment the valve structures from clinical images through standard image processing techniques, such as intensity-based thresholding, to distinguish the valve structures from the surrounding blood pool (54, 58, 60, 61). The latest segmentation algorithms being developed can also better segment the valvular structures utilizing data from Echo (62–65), which is preferred to CT in the clinical setting. Still, the efficient transfer of volumetric imaging data into FEA remains a challenge.…”

Section: Modeling Of Valve Geometry and Dynamic Motionmentioning

confidence: 99%

Computational Modeling of Cardiac Valve Function and Intervention

Sun

Martin

Pham

2014

Annu. Rev. Biomed. Eng.

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In the past two decades, major advances have been made in the clinical evaluation and treatment of valvular heart disease owing to the advent of noninvasive cardiac imaging modalities. In clinical practice, valvular disease evaluation is typically performed on two-dimensional (2D) images, even though most imaging modalities offer three-dimensional (3D) volumetric, time-resolved data. Such 3D data offer researchers the possibility to reconstruct the 3D geometry of heart valves at a patient-specific level. When these data are integrated with computational models, native heart valve biomechanical function can be investigated, and preoperative planning tools can be developed. In this review, we outline the advances in valve geometry reconstruction, tissue property modeling, and loading and boundary definitions for the purpose of realistic computational structural analysis of cardiac valve function and intervention.

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Automated Segmentation and Geometrical Modeling of the Tricuspid Aortic Valve in 3D Echocardiographic Images

Cited by 18 publications

References 15 publications

In-vivo heterogeneous functional and residual strains in human aortic valve leaflets

In-vivo heterogeneous functional and residual strains in human aortic valve leaflets

Machine learning–based 3‐D geometry reconstruction and modeling of aortic valve deformation using 3‐D computed tomography images

Computational Modeling of Cardiac Valve Function and Intervention

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