Stitched X-Ray Roadmaps for Intravascular Data Co-Registration
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Solution Overview
Problem
In intraluminal medical imaging, co-registration of intravascular data with external x-ray images is challenging due to the lack of a single contrast-filled x-ray frame serving as a roadmap, especially in peripheral vasculature where vessels require multiple external images, making navigation and data mapping difficult.
Innovation Solution
A system for co-registering intravascular data with multiple external x-ray images by stitching together these images to create a roadmap image, using geometric and mathematical techniques, and tracking the intravascular device's position within a low-dose fluoroscopy image stream, employing transformation matrices and landmark detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple external x-ray images are used to map long peripheral vessels, then the coverage area increases, but the complexity of co-registration increases due to lack of single roadmap frame
Solution Approach 1:
The system segments the vascular mapping task by dividing it into multiple overlapping x-ray image sections. Each section is processed and co-registered independently, then integrated into a complete vascular map. This allows coverage of long vessels while managing complexity through divide-and-conquer approach.
Solution Approach 2:
The system introduces an intermediary co-registration process that uses detected landmarks (such as vessel bifurcations, stenoses, or implanted devices) as reference points to align multiple x-ray images. This intermediary alignment mechanism bridges the gap between separate images, enabling accurate co-registration without requiring a single comprehensive roadmap frame.
2Area of stationary object
If multiple external x-ray images are stitched together, then the vascular coverage improves, but the time required for image processing and alignment increases
Solution Approach 1:
The system performs preliminary detection of landmarks and features in each x-ray image before the actual co-registration process. By pre-identifying reference points such as vessel bifurcations, stenoses, or implanted devices, the system prepares alignment data in advance, significantly reducing the time required for the actual image stitching and co-registration operations.
3Adaptability or versatility
If manual judgment is used for co-registration, then flexibility is maintained, but reproducibility and precision decrease
Solution Approach 1:
The system implements automated feedback mechanisms that detect landmarks, calculate alignment transformations, and adjust image co-registration based on quantitative criteria. This automated feedback loop ensures consistent, reproducible results while maintaining the ability to incorporate manual adjustments when needed, thus preserving flexibility while improving precision.
Data Source
AI summary
Disclosed is a medical imaging system, including a processor circuit configured for communication with an x-ray imaging device movable relative to a patient and an intravascular catheter or guidewire sized and shaped for positioning within a blood vessel of the patient, wherein the processor circuit is configured to receive a first angiographic image of a first length of the vessel and a second angiographic image of a second length of the vessel, wherein the first image is obtained at a first position and the second angiographic image is obtained at a second position. The processor is further configured to generate a roadmap image of a combined length of the blood vessel by combining the first image and the second image, and to receive intravascular data associated with the blood vessel, and to co-register the intravascular data to corresponding locations in the roadmap image; and output the roadmap image and a graphical representation of the intravascular data at the corresponding locations in the roadmap image.