Geometry Sensitivity Workflow for Reliable Quantity Calculations
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Solution Overview
Problem
Existing workflows in medical imaging and related fields fail to adequately address regions of geometric models that exhibit high sensitivity to uncertainty, impacting the computation of quantities of interest, necessitating a need for guided workflows that focus attention on these regions.
Innovation Solution
Systems and methods utilize geometry sensitivity information to guide workflows by determining geometric models, quantities of interest, and sensitivity information, generating workflows that highlight and potentially correct regions of high sensitivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional workflows are used without geometry sensitivity analysis, then the workflow process is simple and fast, but regions of high sensitivity are not identified, leading to potential inaccuracies in quantity of interest calculations
Solution Approach 1:
The geometric model is divided into multiple subdivisions or regions, and sensitivity analysis is performed on each region independently. This allows the workflow to focus computational resources on specific high-sensitivity regions rather than uniformly processing the entire model, thereby improving measurement precision where needed while managing overall complexity.
Solution Approach 2:
Geometry sensitivity analysis is performed as a preliminary step before the main quantity of interest calculation. By identifying high-sensitivity regions in advance, the workflow can prioritize these regions for more detailed analysis or correction, ensuring that the most critical areas are addressed first to improve overall calculation accuracy.
2Reliability
If geometry sensitivity analysis is performed on the entire model, then all regions are evaluated for sensitivity, but this increases computational time and resource requirements
Solution Approach 1:
Instead of applying uniform sensitivity analysis across the entire geometric model, the workflow identifies and focuses analysis on specific local regions that exhibit high sensitivity. This local approach maintains reliability by ensuring that critical regions are thoroughly evaluated while avoiding unnecessary computational expenditure on low-sensitivity areas.
Solution Approach 2:
The workflow performs sensitivity analysis on a subset of the model rather than the complete model. By selecting only the most relevant or sensitive regions for detailed analysis, the system achieves sufficient reliability for the quantity of interest calculation without the excessive computational time required for full-model analysis.
3Measurement precision
If high-sensitivity regions are identified and corrected, then the accuracy of quantity of interest calculations improves, but additional manual intervention and correction steps are required
Solution Approach 1:
The workflow incorporates feedback mechanisms where sensitivity analysis results automatically inform subsequent processing steps. High-sensitivity regions identified through analysis trigger automatic corrective actions or prompt targeted user intervention, creating a closed-loop system that improves accuracy while minimizing unnecessary manual operations in low-sensitivity areas.
Solution Approach 2:
The system automatically identifies and flags high-sensitivity regions without requiring manual specification by the user. The workflow self-adjusts to focus computational resources and user attention on critical areas, thereby improving measurement precision while maintaining ease of operation through automated guidance rather than requiring comprehensive manual intervention.
Data Source
AI summary
Systems and methods are disclosed for using geometry sensitivity information for guiding workflows in order to produce reliable models and quantities of interest. One method includes determining a geometric model associated with a target object; determining one or more quantities of interest; determining sensitivity information associated with one or more subdivisions of the geometric model and the one or more quantities of interest; and generating, using a processor, a workflow based on the sensitivity information.