Intraluminal Image Frame Confirmation During Deep Breathing
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Solution Overview
Problem
Intraluminal imaging during deep breathing can lead to inaccurate identification of vessel constriction regions due to vein contraction and expansion, resulting in incorrect treatment decisions such as misplacement or incorrect sizing of stents.
Innovation Solution
A system that confirms correct identification of candidate reference and target frames by instructing the patient to breathe deeply while the imaging catheter is stationary, selecting frames with the greatest vein size during the breathing cycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If intraluminal imaging is performed during normal breathing, then the imaging procedure can be completed efficiently, but the vein contraction and expansion leads to inaccurate identification of constriction regions
Solution Approach 1:
The system utilizes the periodic nature of deep breathing cycles to capture multiple images at different phases of vein expansion and contraction. By acquiring images systematically during inhalation and exhalation phases, the system transforms the harmful physiological motion into a structured periodic sampling process that enables accurate identification of constriction regions independent of breathing phase.
Solution Approach 2:
The system performs preliminary processing by identifying candidate reference frames and candidate target frames before final classification. It preemptively captures images during deep breathing cycles, selects frames representing maximum and minimum vein sizes, and prepares these candidate frames for subsequent verification, ensuring accurate measurements are made from pre-selected representative frames.
2Measurement precision
If images are acquired while the catheter is stationary during deep breathing, then accurate reference and target frame confirmation is achieved, but the imaging time increases
Solution Approach 1:
Instead of continuously imaging throughout the entire deep breathing cycle, the system acquires images selectively at key phases - specifically at inhalation and exhalation points where vein size extremes occur. This partial sampling approach captures sufficient information for accurate frame identification without requiring continuous imaging, thereby reducing total imaging time while maintaining measurement precision.
Solution Approach 2:
The system extracts and isolates the most informative frames from the breathing cycle - specifically identifying candidate reference frames at maximum vein size and candidate target frames at minimum vein size. By extracting only these critical frames for detailed analysis rather than processing all acquired images, the system minimizes time investment while maximizing identification accuracy.
3Reliability
If candidate frames are verified through deep breathing cycles, then correct classification of healthy and constricted regions is ensured, but the complexity of the procedure increases
Solution Approach 1:
The system implements feedback mechanisms where candidate reference frames and candidate target frames are verified through their response to deep breathing cycles. The system monitors changes in vein size during breathing, compares measured dimensions against expected patterns, and uses this feedback to confirm or reject candidate frame classifications. This automated feedback loop ensures reliable classification without requiring complex manual verification procedures.
Solution Approach 2:
The system performs self-verification by automatically analyzing the breathing-induced size changes of candidate frames and classifying them as reference or target frames based on predetermined criteria. The imaging system itself provides the verification service by comparing frame characteristics across breathing phases, eliminating the need for external manual verification and reducing overall procedural complexity while maintaining high reliability.
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
Ensures accurate classification of healthy and constricted vessel regions, enabling proper treatment selection by ensuring correct frame classification and measurement.
Implementation Method 1
The transducers emit ultrasonic energy. Ultrasonic waves are partially reflected by discontinuities in tissue structures (such as various layers of the vessel wall), red blood cells, and other features of interest. Echoes from the reflected waves are received by the transducer
Data Source
AI summary
A system includes a processor circuit that receives intraluminal images obtained by an intraluminal imaging device during movement through a patient's body lumen. The processor circuit outputs, to a display, a visual representation of user guidance in response to the processor circuit identifying, among the intraluminal images, a candidate intraluminal image. The user guidance includes stopping the movement and instructing the patient to initiate deep breathing. The processor circuit receives additional intraluminal images obtained by the intraluminal imaging device while the movement is stopped and the patient is deep breathing. The processor circuit determines if a shape and/or size of the body lumen changes in the additional intraluminal images. The processor circuit accepts or rejects the candidate intraluminal image based on if the shape and/or size of the body lumen changes. The processor circuit outputs, to the display, a visual representation corresponding to accepting or rejecting the candidate intraluminal image.