Virtual Stent Planning Using 3D Vessel Models
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Solution Overview
Problem
Current methods for planning stent deployment in coronary arteries are manual and limited, making it challenging for cardiologists to determine optimal stent size and placement, especially in complex lesions, and requiring longer cath lab times.
Innovation Solution
The system generates a representation of the blood vessel using collected data, allowing for the planning of stent deployment by identifying optimal stent lengths and placement locations through virtual stenting and computational flow models, such as Virtual Fractional Flow Reserve (VFR).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual stent planning methods are used with longitudinal photographs and rulers, then the process is simple to operate, but the precision of stent size and placement determination is insufficient
Solution Approach 1:
The patent creates a virtual 3D copy of the blood vessel using medical imaging data (CT, MRI, angiography). This digital replica allows for precise measurement and stent planning without physical manipulation, resolving the contradiction by providing high measurement precision through computational methods while keeping the interface user-friendly.
Solution Approach 2:
The patent replaces manual mechanical measurement methods (physical rulers and photographs) with computational algorithms that automatically analyze 3D vessel models. This substitution provides superior measurement precision through automated image processing and 3D reconstruction, eliminating the limitations of manual measurement while maintaining ease of use through software interfaces.
2Productivity
If manual review of images is performed to determine stent placement, then the process is straightforward, but the time required for complex lesions is excessive
Solution Approach 1:
The patent performs preliminary 3D reconstruction and virtual stent placement simulations before actual stent deployment. The system pre-calculates optimal stent sizes, positions, and orientations by analyzing the 3D vessel model and simulating various stent configurations, allowing physicians to review multiple options quickly without time-consuming manual measurements during the procedure.
Solution Approach 2:
The automated planning system performs self-service by automatically analyzing medical images, reconstructing 3D vessel models, identifying optimal stent placement locations, and generating placement recommendations without requiring manual intervention for each measurement. This automation dramatically increases productivity for complex lesions while the system integrates seamlessly into existing workflows.
3Reliability
If longer stents are used to ensure adequate coverage, then the reliability of flow restoration is improved, but the trauma to the artery and metal exposure increase
Solution Approach 1:
The patent applies local quality by precisely matching stent dimensions to the specific geometry and pathology of each vessel segment. The 3D analysis identifies the exact length, diameter, and shape requirements for each stent based on local vessel characteristics, ensuring adequate flow restoration while minimizing stent length and metal exposure to the minimum necessary amounts.
Solution Approach 2:
The system changes parameters by optimizing stent dimensions (length, diameter, expansion ratio) based on detailed 3D vessel measurements. Rather than using standard fixed-size stents, the platform calculates optimal parameters tailored to each patient's anatomy and lesion characteristics, achieving reliable flow restoration with minimized arterial trauma and metal burden.
Data Source
AI summary
In part, the disclosure relates to determining a stent deployment location and other parameters using blood vessel data. Stent deployment can be planned such that the amount of blood flow restored from stenting relative to an unstented vessel increases one or more metrics. An end user can specify one or more stent lengths, including a range of stent lengths. In turn, diagnostic tools can generate candidate virtual stents having lengths within the specified range suitable for placement relative to a vessel representation. Blood vessel distance values such as blood vessel diameter, radius, area values, chord values, or other cross-sectional, etc. its length are used to identify stent landing zones. These tools can use or supplement angiography data and/or be co-registered therewith. Optical imaging, ultrasound, angiography or other imaging modalities are used to generate the blood vessel data.