Imaging Confidence Assessment for Dynamic Functional Maps
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Solution Overview
Problem
Dynamic functional imaging acquisitions often result in incorrect or misinterpreted functional maps due to improper coordination of imaging quality parameters, such as region of interest coverage, timing, and patient movement, leading to potential incorrect medical diagnoses and treatments.
Innovation Solution
A system and method to automatically assess the confidence in dynamic functional imaging acquisitions by determining quantitative imaging quality factors, including accuracy, location, and timing, and comparing these factors to confidence benchmark values associated with specific diagnostic tasks or treatment decisions, to generate a confidence value indicative of the imaging data's reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dynamic functional imaging acquisitions are performed without automated quality assessment, then imaging data can be obtained, but the confidence in the data for medical diagnosis and treatment decisions cannot be reliably evaluated
Solution Approach 1:
The imaging system performs self-assessment of its own output quality by automatically evaluating imaging parameters (region of interest coverage, timing, patient movement) and generating confidence values without requiring external manual evaluation, enabling the system to self-diagnose and self-regulate
Solution Approach 2:
The system incorporates feedback mechanisms where imaging quality indicators are continuously monitored and fed back into the acquisition process, allowing real-time adjustment of imaging parameters and providing confidence values that inform subsequent imaging decisions and protocol modifications
2Reliability
If automated assessment of imaging data confidence is implemented, then reliability of medical decisions can be improved, but the complexity of the imaging system increases
Solution Approach 1:
The assessment system is segmented into distinct functional modules that evaluate specific imaging quality parameters independently (region of interest coverage, timing accuracy, patient movement), with each module producing specific quality indicators that can be processed separately before being integrated into the overall confidence assessment
Solution Approach 2:
The system transforms complex imaging data into simplified quantitative parameters (imaging quality indicators) that can be easily compared against benchmark values, converting difficult-to-evaluate imaging quality into measurable, comparable metrics that drive the confidence assessment
3Manufacturing precision
If imaging quality parameters are not properly coordinated, then imaging acquisitions can be performed, but incorrect or misinterpreted functional maps result
Solution Approach 1:
The system performs preliminary assessment of imaging quality parameters during the acquisition process itself, evaluating region of interest coverage, timing, and patient movement before final functional maps are generated, allowing corrective actions to be taken while the imaging process is still active
Solution Approach 2:
Real-time feedback is provided on imaging quality parameters during the acquisition process, allowing dynamic adjustment of imaging protocols to ensure proper coordination of parameters and prevent generation of inaccurate functional maps before they are finalized
4Measurement precision
If manual evaluation of imaging data quality is performed, then assessment accuracy can be maintained, but time consumption increases
Solution Approach 1:
The imaging system automatically evaluates its own output quality by processing imaging parameters and generating confidence values without requiring manual intervention, performing the assessment function that would otherwise require human expertise through automated algorithms
Solution Approach 2:
Manual evaluation processes are replaced with automated computational systems that use algorithms to assess imaging quality indicators, substituting human expert judgment with machine-based automated assessment that operates continuously without time loss
Data Source
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AI summary
A system and method are provided to automatically assess a confidence in imaging data based on a proposed diagnostic task or treatment decision, by determining one or more imaging quality indicators relating to the imaging data corresponding to a confidence of the proposed diagnostic task or treatment decision, comparing those imaging quality indicators with confidence benchmark values, and determining a confidence value indicative of the confidence in the imaging data for purposes of performing the proposed diagnostic task or making the proposed treatment decision.