Multimodal Catheter Imaging for Blind Spot and False Positive Detection
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Solution Overview
Problem
Existing imaging technologies, such as OCT and NIRAF, struggle with user-dependent interpretation leading to false positives, false negatives, and undefined 'blind spots' in medical images, which can result in misdiagnosis due to excessive data and lack of timely notification of uncertain image regions.
Innovation Solution
A system and method for detecting false positive, false negative, and blind spot locations in multimodality images using a multimodality catheter, which concurrently acquires and analyzes OCT and fluorescence data to identify characteristic features and display these regions with markers, allowing users to make accurate judgments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user-dependent interpretation of imaging data is used, then diagnostic flexibility is maintained, but false positives and false negatives increase due to human error and fatigue
Solution Approach 1:
The system implements automated feedback mechanisms where the processing unit continuously monitors imaging data and provides real-time notifications to users about potential false positives, false negatives, and blind spots. This feedback loop enhances diagnostic reliability by compensating for human error and fatigue while maintaining user control and interpretation flexibility.
Solution Approach 2:
The patent introduces an automated processing unit as an intermediary between the imaging system and the user. This intermediary analyzes imaging data, identifies uncertain regions, and presents processed information to the user, reducing the cognitive burden while improving diagnostic accuracy through automated detection of false positives, false negatives, and blind spots.
2Loss of information
If comprehensive imaging data is collected using multiple modalities, then diagnostic information completeness improves, but data complexity increases leading to user confusion and fatigue
Solution Approach 1:
The system extracts and isolates critical diagnostic information from comprehensive multimodal imaging data. The processing unit identifies and flags specific uncertain regions (false positives, false negatives, blind spots) rather than presenting all raw data to the user, thereby maintaining information completeness while reducing perceived complexity and user burden.
Solution Approach 2:
The patent segments comprehensive imaging data into distinct categories and regions of interest. By dividing the data into manageable components (certain regions vs. uncertain regions) and processing them separately, the system maintains complete diagnostic information while reducing overall data complexity and preventing user confusion.
3Measurement precision
If automated processing is implemented to reduce user burden, then false positive and false negative detection improves, but blind spot regions may remain undetected without additional processing
Solution Approach 1:
The system performs preliminary automated processing to identify false positives and false negatives before final user review. By pre-processing the data and flagging uncertain regions, the system improves detection accuracy while preparing a focused set of issues for user evaluation, thereby reducing the risk of missed blind spots.
Solution Approach 2:
The patent implements a feedback mechanism where the processing unit continuously monitors for blind spot regions and notifies users of areas requiring additional review. This feedback ensures that automated processing does not create blind spots, as the system actively identifies and communicates uncertain regions that need further user attention.
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
Enhances image interpretation by notifying users of potential false positives, negatives, and blind spots, reducing misdiagnosis risks through real-time, user-guided decision-making.
Implementation Method 1
OCT is a high-resolution imaging modality that uses backscattered light to produce two-dimensional (2D) and three-dimensional (3D) images of tissue microstructure in situ and in real-time
Implementation Method 2
fluorescence detects molecules specific to necrotic cores, and the intensity of near-infrared auto-fluorescence (NIRAF) is associated with plaque types
Data Source
AI summary
A system, method, and computer-readable media configured to process image data for detecting blind spot locations in multimodality images acquired by an imaging catheter. The method comprises: acquiring a multimodality image of a blood vessel, the multimodality image includes first image data co-registered with second image data collected simultaneously by scanning an inner wall of the blood vessel with light of two or more wavelengths transmitted through the catheter; analyzing the first image data to identify a characteristic feature of the blood vessel; detecting, based on the characteristic feature of the blood vessel, a potential blind spot location of the second image data in the multimodality image; and displaying on a display device the multimodality image with a marker showing the potential blind spot location of the second image data in relation to the first image data.