Thoracic 3D Model Updating for Respiratory Motion Compensation
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Solution Overview
Problem
Existing systems for generating 3D models of airways and luminal networks in the thoracic cavity during respiration fail to account for the significant movement of structures within the thoracic cavity due to respiratory cycles, limiting the assessment of respiratory health and complicating procedures like electromagnetic navigation bronchoscopy.
Innovation Solution
A system utilizing electromagnetic tracking of sensors attached to the patient's chest, generating 3D models at different tidal volumes and respiratory phases, and dynamically updating these models to compensate for respiratory motion, allowing for accurate visualization and navigation during procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If 3D models are generated from CT images during full-breath hold, then the model structure is stable and complete, but the model does not reflect the dynamic movement of thoracic structures during normal respiration
Solution Approach 1:
The system dynamically updates the 3D model based on real-time sensor positions during respiration. Instead of using a static model from full-breath hold CT images, the system continuously adjusts the model to reflect the actual positions of thoracic structures at different tidal volumes, making the model adaptive to respiratory motion phases.
Solution Approach 2:
The system uses EM sensors attached to the patient's chest to provide feedback on actual thoracic cavity movement during respiration. This feedback is used to update and refine the 3D model in real-time, ensuring the model accurately represents the dynamic positions of airways and structures at different respiratory phases.
2Measurement precision
If EM sensors are attached to the patient's chest to track movement, then dynamic respiratory motion can be detected, but the system complexity increases
Solution Approach 1:
The system uses electromagnetic sensors as intermediaries to indirectly measure thoracic structure movement. Instead of directly tracking airways and luminal networks, the system places EM sensors on the chest wall and uses the detected chest movement to infer and update the positions of internal structures through model transformation.
3Measurement precision
If the 3D model is updated in real-time during procedures, then navigation accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The system pre-generates the base 3D model from CT images before the procedure begins. This preliminary model serves as the foundation, and during the procedure, only incremental updates are made based on sensor data, reducing the computational burden during real-time navigation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides comprehensive insight into thoracic cavity movement, enhancing respiratory health assessment and improving the accuracy and safety of procedures by accounting for dynamic changes in the thoracic cavity structures during normal breathing.
Implementation Method 1
an electromagnetic field generator configured to generate an electromagnetic field
Implementation Method 2
a plurality of electromagnetic sensors attached to a patient and movable within the electromagnetic field
Data Source
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AI summary
A system for visualizing movement of structures within a patient's chest is described herein. The system includes an electromagnetic tracking system, a computing device and a display. The computing device includes a processor configured generate a 3D model of an interior of the patient, obtain positions of EM sensors for the 3D model, determine positions of the EM sensors at intervals during the respiratory cycle, determine positions of the EM sensors at maximum tidal volume and minimum tidal volume, determine differences between the positions of the EM sensors at maximum tidal volume and for the 3D model, generate a 3D model at maximum tidal volume based on the differences between the positions of the EM sensors at maximum tidal volume and for the 3D model, and store in memory the 3D model at maximum tidal volume.