Anatomic Lumen Bridge Generation for Lung Ablation Navigation
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Solution Overview
Problem
Current systems for endoluminal ablation treatments face challenges in effectively planning, navigating, and delivering treatments within anatomic lumens, particularly in achieving good contact with the lumen wall and minimizing lesion overlap.
Innovation Solution
The system comprises a processor and memory with computer-readable instructions that receive anatomic image data, segment it to identify anatomic passageways, generate bridge segments to connect gaps, and create anatomic models of diseased lungs. It also generates ablation treatment plans, provides navigation guidance, and generates treatment reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual navigation and treatment delivery is used, then operator flexibility is maintained, but contact quality with lumen wall and treatment precision deteriorate
Solution Approach 1:
The system segments the anatomic lumen into discrete three-dimensional coordinates and divides the treatment into sequential steps: navigation phase, contact verification phase, and ablation delivery phase. This segmentation allows automated precision navigation while maintaining operator control during critical treatment phases, resolving the contradiction between precision and complexity.
Solution Approach 2:
The system introduces an intermediary computational layer that processes imaging data, generates three-dimensional anatomical models, and provides navigation guidance without directly controlling the ablation device. This intermediary layer enhances treatment precision through automated modeling and navigation while leaving final treatment decisions to the operator, thereby managing system complexity.
2Measurement precision
If automated navigation system is implemented, then navigation accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-generating three-dimensional anatomical models from imaging data before the actual ablation procedure. This preliminary modeling and navigation planning enables high navigation accuracy during treatment while the actual ablation delivery remains under operator control, preventing excessive system complexity.
Solution Approach 2:
The system implements feedback mechanisms that provide real-time navigation guidance and contact verification data to the operator. This feedback loop enhances navigation accuracy by comparing actual device position with the pre-generated three-dimensional model, while the operator retains final control, thereby managing system complexity through information rather than direct automation.
3Manufacturing precision
If comprehensive anatomic modeling is performed, then treatment planning accuracy is improved, but data processing time increases
Solution Approach 1:
The system performs comprehensive anatomic modeling and three-dimensional reconstruction as preliminary actions before the ablation procedure begins. By completing this computationally intensive task in advance, the system achieves high treatment planning accuracy without delaying the actual treatment delivery, as the model is ready for immediate use during the procedure.
Solution Approach 2:
The system employs dynamic data processing that adapts the level of modeling detail based on procedural needs. During navigation phases, comprehensive three-dimensional models provide high planning accuracy, while during active treatment, the system dynamically focuses on specific anatomical regions, reducing unnecessary data processing and minimizing time loss without sacrificing required precision.
Data Source
AI summary
A system may comprise a processor and a memory having computer readable instructions stored thereon. The computer readable instructions, when executed by the processor, may cause the system to receive anatomic image data for an anatomic area and segment the anatomic image data to identify an anatomic passageway in the anatomic area. The computer readable instructions may also cause the system to identify a gap between a first segment and a second segment of the segmented anatomic image data, generate a bridge segment to bridge the gap, and generate an anatomic model of a diseased lung including the first segment, the second segment and the bridge segment.