Endoluminal Imaging Probe Data Gap Handling
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Solution Overview
Problem
Current medical procedures face challenges in effectively integrating extraluminal imaging with endoluminal data for precise navigation and data registration during vascular catheterizations and other luminal interventions, leading to suboptimal diagnostic and therapeutic outcomes.
Innovation Solution
The development of an apparatus and method that combines extraluminal imaging with endoluminal data acquisition, utilizing a processor to designate roadmap images, identify features, map these features to a roadmap pathway, and co-register endoluminal data points with extraluminal images, enabling accurate navigation and data alignment within the lumen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extraluminal imaging is used alone for navigation during luminal interventions, then the overall anatomical context is provided, but the precision of endoluminal data registration and localization is insufficient
Solution Approach 1:
The patent combines extraluminal imaging (fluoroscopy) with endoluminal imaging (OCT, IVUS, or NIRS) to create a unified navigation system. The extraluminal roadmap provides anatomical context and navigation guidance, while endoluminal images provide high-resolution tissue characterization. The system merges these two imaging modalities by co-registering endoluminal images to the extraluminal roadmap, allowing precise localization of endoluminal findings within the broader anatomical context, thereby improving measurement precision without requiring separate independent systems
Solution Approach 2:
The imaging system is designed to perform multiple functions: it provides real-time navigation via extraluminal fluoroscopy, high-resolution tissue imaging via endoluminal probes, and automated co-registration of both datasets. The roadmap-mapping functionality and feature-identifying functionality enable the system to serve both as a navigation tool and an diagnostic imaging tool, reducing the need for separate specialized systems and improving overall system efficiency
2Reliability
If endoluminal data acquisition is performed during movement through the lumen, then comprehensive luminal coverage is achieved, but skipped imaging locations create gaps in the data set
Solution Approach 1:
The system performs preliminary roadmap creation using extraluminal fluoroscopy before endoluminal imaging. This roadmap serves as a pre-established reference framework that allows the system to later fill in skipped locations by interpolating from nearby acquired images and matching features against the roadmap, ensuring data completeness without requiring continuous imaging at every possible location
Solution Approach 2:
The system creates a virtual copy of the lumen pathway through roadmap-mapping, where key anatomical features are identified and tracked. When imaging locations are skipped, the system can reconstruct the missing data by referencing the roadmap copy and using feature-matching algorithms to estimate what the skipped images would show, maintaining data reliability while allowing faster acquisition speeds
3Measurement precision
If feature identification and roadmap mapping are performed for all images, then accurate co-registration is achieved, but processing time and computational load increase
Solution Approach 1:
The feature-identifying functionality is applied selectively rather than uniformly to all images. The system identifies key anatomical landmarks and features at critical locations along the roadmap pathway, particularly at bifurcations, lesions, or other clinically relevant sites. This localized approach maintains co-registration accuracy where it matters most while reducing overall processing time and computational requirements compared to analyzing every image in detail
4Measurement precision
If synchronized display of extraluminal and endoluminal data is provided, then navigation accuracy is improved, but system complexity and integration requirements increase
Solution Approach 1:
The extraluminal fluoroscopic roadmap serves as an intermediary framework that bridges the extraluminal and endoluminal imaging systems. By mapping endoluminal images to this intermediate roadmap representation, the system achieves synchronized display and accurate navigation without requiring direct complex integration between all system components. The roadmap acts as a common reference frame that simplifies the integration architecture
Data Source
AI summary
Apparatus and methods are described including, while an endoluminal data-acquisition device is being moved through a lumen of a subject, acquiring a plurality of endoluminal data points of the lumen using the endoluminal data-acquisition device. It is determined that, at least one location, no endoluminal data point was acquired. An output is generated using at least a portion of the plurality of endoluminal data points of the lumen acquired using the endoluminal data-acquisition device, the output including an indication that no endoluminal data point was acquired at the location. Other applications are also described.