3D Vascular Central Line Determination for Implant Positioning
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Solution Overview
Problem
Current methods for predicting the final position and radial expansion of expandable implantable medical devices (IMDs) in vascular structures, such as stents or flow diverters, are not reliable due to their reliance on two-dimensional measurements and assumptions of constant circular vascular sections, leading to potential misplacement and thrombus formation risks.
Innovation Solution
A method using a gradient descent algorithm to determine a central line in three-dimensional vascular images, combined with longitudinal compression rate considerations, to accurately simulate the final position and expansion of IMDs, accounting for geometric and mechanical constraints to ensure precise apposition against vascular walls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If two-dimensional measurements are used to predict IMD final position, then the prediction process is simple, but the prediction accuracy is insufficient
Solution Approach 1:
The patent transitions from two-dimensional measurements to three-dimensional imaging (CT or MRI scans) to capture the full spatial complexity of vascular structures. This dimensional upgrade allows accurate representation of vessel tortuosity, bifurcations, and aneurysm geometries, enabling precise prediction of IMD final position and orientation after deployment.
Solution Approach 2:
The patent incorporates multiple physical parameters including vessel wall elasticity, implant material properties, deployment force characteristics, and blood flow dynamics. By considering these varying parameters rather than assuming rigid geometric constraints, the model accurately predicts how the implant will deform and settle in the actual physiological environment.
2Device complexity
If constant circular cross-section assumption is made, then the calculation is simplified, but the vascular structure morphology is not accurately represented
Solution Approach 1:
The patent divides the vascular structure into multiple cross-sectional segments along its length, with each segment's geometry independently determined from 3D imaging data. This segmentation allows the model to capture local variations in vessel shape, including elliptical cross-sections, irregular boundaries, and transitions between different geometries, while maintaining computational tractability through systematic processing of discrete segments.
Solution Approach 2:
The patent applies different geometric characteristics to different locations along the vessel. Each cross-sectional segment is modeled with its specific shape properties (circular, elliptical, irregular) rather than forcing a uniform circular assumption throughout. This local quality approach ensures accurate representation of anatomical variations such as vessel tapering, bending, and aneurysm formation at specific sites.
3Productivity
If implant expansion is not accurately predicted, then the deployment process is faster, but misplacement and thrombus formation risks increase
Solution Approach 1:
The patent performs virtual deployment simulations before the actual clinical procedure using patient-specific 3D vascular models. The simulation predicts the implant's final position, expansion diameter, and apposition quality against vessel walls, allowing clinicians to optimize implant selection and deployment parameters in advance. This preliminary virtual testing prevents misplacement and thrombus formation risks during the actual procedure while maintaining efficient clinical workflow.
4Measurement precision
If 3D imaging and gradient descent algorithm are used to determine central line, then the longitudinal axis representation is more accurate, but the computational complexity increases
Solution Approach 1:
The patent replaces manual or simple geometric methods for determining the vessel central line with a gradient descent optimization algorithm. This computational approach iteratively refines the central line estimation by minimizing the distance to the actual vessel wall boundaries derived from 3D imaging data, achieving high precision in representing the longitudinal axis even in highly tortuous vessels, while the computational burden is managed through efficient algorithm implementation.
Data Source
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Figure 3b
AI summary
The invention relates to a method for determining the deployed position of an implant in the form of an expandable medical device, which method, starting from a three-dimensional image of a region of interest comprising the vascular structure, comprises the following steps: determining a central line of the vascular structure, positioning the IMD in an initial position around the central line, simulating the final position of the IMD after deployment, depending on the stresses exerted on the IMD by the walls of the vascular structure, the determination of the central line consisting in placing points in such a way as to minimize a fluid transit time along said points between an inlet point and an outlet point, the transit time being minimized by using a gradient descent algorithm, the simulation of the final position of the IMD taking into account a rate of longitudinal compression intended to be applied to the IMD during the implantation of the latter. Figure 2