Patient-specific, dynamic models of hypoplastic left heart syndrome tricuspid valves for simulation and planning

Boone, Nora; Nam, Hannah H.; Moore, John; Carnahan, Patrick; Ginty, Olivia K.; Herz, Christian; Lassó, András; Jolley, Matthew A.; Chen, Elvis C. S.; Peters, Terry M.

doi:10.1117/12.2549745

Cited by 3 publications

(3 citation statements)

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“…TV failure remains a critical factor in both the quality of life and survival of patients with HLHS ( 1 ). Rapid, accurate creation of valve models remains a roadblock to the application of quantitative modeling and simulation of the tricuspid valve in children with HLHS ( 6 , 14 , 16 , 17 ). To the best of our knowledge, this is the first description of a DL-based approach to the segmentation of TV leaflets from 3DE, and to the segmentation of valves in children with congenital heart disease.…”

Section: Discussionmentioning

confidence: 99%

“…In the setting of these challenges, the investigation of automatic and semi-automatic methods for the segmentation of atrioventricular valves from 3DE of children has been limited ( 17 , 18 ). Atlas-based segmentation has shown promising results for segmenting the adult mitral valve from TEE images and, in a small series, was demonstrated to be feasible, but less accurate for the segmentation of TV in HLHS from TTE images ( 19 ).…”

Section: Introductionmentioning

confidence: 99%

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

Herz

Pace

Lassó

et al. 2021

Front. Cardiovasc. Med.

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Herz

Pace

Lassó

et al. 2021

Front. Cardiovasc. Med.

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“…In 2020, the models were then inserted in a heart phantom simulator, and the behavior was compared with patient-specific data before and after mitral valve repair. The same workflow was then presented in the case of tricuspid valve preoperative simulation and planning [114]. In this case, TTE images were employed.…”

Section: Cardiovascular Systemmentioning

confidence: 99%

Soft robotics for physical simulators, artificial organs and implantable assistive devices

et al. 2023

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In recent years, soft robotics technologies enabled the development of a new generation of biomedical devices. The combination of elastomeric materials with tunable properties and muscle-like motions paved the way toward more realistic phantoms and innovative soft active implants as artificial organs or assistive mechanisms. This review collects the most relevant studies in the field, giving some insights about their distribution in the past ten years, and their level of development, and opening a discussion about the most commonly employed materials and actuating technologies. The reported results show some promising trends, highlighting that the soft robotics approach can help replicate specific material characteristics, in the case of static or passive organs, but also reproduce peculiar natural motion patterns for the realization of dynamic phantoms or implants. At the same time, some important challenges still need to be addressed. However, by joining forces with other research fields and disciplines, it will be possible to get one step closer to the development of complex, active, self-sensing and deformable structures able to replicate as closely as possible the typical properties and functionalities of our natural body organs.

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3D localization of vena contracta using Doppler ICE imaging in tricuspid valve interventions

et al. 2022

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Purpose Tricuspid valve (TV) interventions face the challenge of imaging the anatomy and tools because of the ‘TEE-unfriendly’ nature of the TV. In edge-to-edge TV repair, a core step is to position the clip perpendicular to the coaptation gap. In this study, we provide a semi-automated method to localize the VC from Doppler intracardiac echo (ICE) imaging in a tracked 3D space, thus providing a pre-mapped location of the coaptation gap to assist device positioning. Methods A magnetically tracked ICE probe with Doppler imaging capabilities is employed in this study for imaging three patient-specific TVs placed in a pulsatile heart phantom. For each of the valves, the ICE probe is positioned to image the maximum regurgitant flow for five cardiac cycles. An algorithm then extracts the regurgitation imaging and computes the exact location of the vena contracta on the image. Results Across the three pathological, patient-specific valves, the average distance error between the detected VC and the ground truth model is $$({1.22 \pm 2.00})$$ ( 1.22 ± 2.00 ) mm. For each of the valves, one case represented the outlier where the algorithm misidentified the vena contracta to be near the annulus. In such cases, it is recommended to retake the five-second imaging data. Conclusion This study presented a method for ultrasound-based localization of vena contracta in 3D space. Mapping such anatomical landmarks has the potential to assist with device positioning and to simplify tricuspid valve interventions by providing more contextual information to the interventionalists, thus enhancing their spatial awareness. Additionally, ICE can be used to provide live US and Doppler imaging of the complex TV anatomy throughout the procedure.

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Patient-specific, dynamic models of hypoplastic left heart syndrome tricuspid valves for simulation and planning

Cited by 3 publications

References 0 publications

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

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

Soft robotics for physical simulators, artificial organs and implantable assistive devices

3D localization of vena contracta using Doppler ICE imaging in tricuspid valve interventions

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