Interactive-Automatic Segmentation and Modelling of the Mitral Valve

MV-GNN: Generation of continuous geometric representations of mitral valve motion from 3D+t echocardiography

We present a geometric deep-learning method for reconstructing a temporally continuous mitral valve surface mesh from 3D transesophageal echocardiography sequences. Our approach features a supervised end-to-end deep learning architecture that combines a convolutional neural network-based voxel encoder and decoder with a graph neural network-based multi-resolution mesh decoder, all trained on sparse landmark annotations. Key elements of our methodology include a tube-shaped prototype mesh with labeled vertices, a specialized loss function to preserve the known inlet and outlet, and a rigid alignment system for anatomical landmarks. A custom term in the loss function prevents self-intersecting geometries within the valve mesh, promoting point correspondence and facilitating a continuous representation of valve anatomy over time. An ablation study evaluates the impact of different loss term configurations on model performance, highlighting the effectiveness of each individual loss term. Our Mitral Valve Graph Neural Network (MV-GNN) outperforms existing deep-learning methods on most distance metrics for the annulus and leaflets. The continuous valve motion representations generated by our approach (3D+t) exhibit distance measures comparable to our 3D solution, demonstrating its potential for analyzing mitral valve dynamics and enhancing personalized simulations for hemodynamic assessment and therapy planning.

Three-dimensional printing in modelling mitral valve interventions

Mitral interventions remain technically challenging owing to the anatomical complexity and heterogeneity of mitral pathologies. As such, multi-disciplinary pre-procedural planning assisted by advanced cardiac imaging is pivotal to successful outcomes. Modern imaging techniques offer accurate 3D renderings of cardiac anatomy; however, users are required to derive a spatial understanding of complex mitral pathologies from a 2D projection thus generating an 'imaging gap' which limits procedural planning. Physical mitral modelling using 3D printing has the potential to bridge this gap and is increasingly being employed in conjunction with other transformative technologies to assess feasibility of intervention, direct prosthesis choice and avoid complications. Such platforms have also shown value in training and patient education. Despite important limitations, the pace of innovation and synergistic integration with other technologies is likely to ensure that 3D printing assumes a central role in the journey towards delivering personalised care for patients undergoing mitral valve interventions.

Mitral valve surgery: current status and future prospects of the minimally invasive approach

Introduction: During the past five years the approach to procedural planning, operative techniques and perfusion strategies for minimally invasive mitral valve surgery (MIMVS) has evolved. With the goal to provide a maximum of patient safety the procedure has been modified according to individual patient characteristics and is largely based on preoperative imaging.Areas covered: In this review article we describe the important factors in image based therapy planning and simulation, different access strategies, the operative key-steps, a rationale use of devices, and highlight a few future developments in the field of MIMVS. Published studies were identified through pearl growing, citation chasing, a search of PubMed using the systematic review methods filter, and the authors' topic knowledge.Expert opinion: With the help of expert teams including surgeons specialized in mitral repair, anesthesiologists and perfusionists a broad spectrum of mitral valve pathologies and related pathologies can be treated with excellent functional outcomes. Avoiding procedure related complications is the key for success for any MIMVS program.

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

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.

Dynamic, patient-specific mitral valve modelling for planning transcatheter repairs

PURPOSE

Transcatheter, beating heart repair techniques for mitral valve regurgitation is a very active area of development. However, it is difficult to both simulate and predict the clinical outcomes of mitral repairs, owing to the complexity of mitral valve geometry and the influence of hemodynamics. We aim to produce a workflow for manufacturing dynamic patient-specific models to simulate the mitral valve for transcatheter repair applications.

METHODS

In this paper, we present technology and associated workflow, for using transesophageal echocardiography to generate dynamic physical replicas of patient valves. We validate our workflow using six patient datasets representing patients with unique or particularly challenging pathologies as selected by a cardiologist. The dynamic component of the models and their resultant potential as procedure planning tools is due to a dynamic pulse duplicator that permits the evaluation of the valve models experiencing realistic hemodynamics.

RESULTS

Early results indicate the workflow has excellent anatomical accuracy and the ability to replicate regurgitation pathologies, as shown by colour Doppler ultrasound and anatomical measurements comparing patients and models. Analysis of all measurements successfully resulted in t critical two-tail > t stat and p values > 0.05, thus demonstrating no statistical difference between the patients and models, owing to high fidelity morphological replication.

CONCLUSIONS

Due to the combination of a dynamic environment and patient-specific modelling, this workflow demonstrates a promising technology for simulating the complete morphology of mitral valves undergoing transcatheter repairs.