3D Angiography Variant Detection for Accurate Vessel Evaluation
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Solution Overview
Problem
Existing methods for evaluating three-dimensional angiography datasets of blood vessel trees, such as coronary artery trees, fail to accurately account for non-pathological anatomical variants, which can complicate diagnostic and treatment planning and influence deep learning-based evaluations.
Innovation Solution
A computer-implemented method for evaluating angiography datasets that automatically determines the presence of anatomical variants by comparing structural features of the blood vessel tree to reference information, using centerline analysis and anatomical atlas datasets, and provides variant information for downstream applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated evaluation algorithms are used to assess anatomical variants, then productivity is improved, but measurement precision deteriorates due to inability to accurately distinguish pathological from non-pathological variants
Solution Approach 1:
The system performs preliminary classification of anatomical variants into pathological and non-pathological categories before detailed evaluation. By pre-defining characteristic patterns and templates of known anatomical variants, the system can quickly screen and categorize variants, improving automation while maintaining precision through established reference standards
Solution Approach 2:
The system introduces an intermediary classification layer that bridges automated detection and final diagnostic interpretation. This intermediary system uses predefined templates and characteristic patterns to mediate between raw imaging data and clinical decisions, enhancing both automation and measurement precision through structured intermediate representation
2Reliability
If comprehensive anatomical variant information is included in evaluation, then reliability is improved, but device complexity increases due to multiple comparison processes
Solution Approach 1:
The evaluation system is segmented into modular components: data acquisition module, preprocessing module, variant detection module, classification module, and reporting module. Each module handles specific tasks independently, allowing comprehensive evaluation while managing complexity through functional decomposition and modular architecture
Solution Approach 2:
The system employs universal templates and characteristic patterns that can be applied across different types of anatomical variants and imaging modalities. This multi-functional approach allows the same evaluation framework to handle diverse variants (e.g., aortic arch variants, coronary artery variants) without requiring separate specialized systems for each case
3Productivity
If deep learning-based algorithms are applied without anatomical variant detection, then productivity is improved, but measurement precision deteriorates due to altered blood flow patterns in variants
Solution Approach 1:
The system performs preliminary detection and classification of anatomical variants before applying deep learning-based hemodynamic analysis. By identifying variants such as aortic arch configurations and coronary artery anomalies in advance, the system can adjust processing parameters and select appropriate algorithms, ensuring both rapid processing and accurate hemodynamic measurements
Solution Approach 2:
The system dynamically changes processing parameters based on detected anatomical variants. When variants are identified, the system adjusts hemodynamic calculation parameters, boundary conditions, and algorithm selection to account for altered blood flow patterns, maintaining measurement precision while preserving processing efficiency through parameter optimization rather than complete reprocessing
Data Source
AI summary
A computer-implemented method for evaluating a three-dimensional angiography dataset of a blood vessel tree of a patient, comprises determining a variant information describing a belonging to at least one anatomical variant class of a plurality of anatomical variant classes relating to anatomical variants of the blood vessel tree based on a comparison of angiography information of the angiography dataset to reference information describing at least one of the anatomical variant classes.