Medical Imaging Protocol Parameter Guidance from Historical Data
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Solution Overview
Problem
The process of adjusting medical imaging protocol parameters is inefficient and inaccurate, leading to longer scanning times and lower accuracy due to the trial-and-error method used by operators with varying skill levels.
Innovation Solution
A system and method that includes obtaining a current setting value of a target protocol parameter, determining its statistical distribution, and generating prompt information to guide adjustments based on its relative position within historical settings, using a graphical user interface to display this information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If operators manually adjust protocol parameters through trial-and-error, then they can set parameters based on their experience, but the scanning time increases and scanning efficiency decreases
Solution Approach 1:
The system performs preliminary actions by automatically determining statistical distribution characteristics of protocol parameters from historical data before the actual scanning occurs. The parameter setting recommendation is pre-calculated based on the current parameter values and their statistical distributions, so that when the operator needs to set parameters, the system can immediately provide recommended values without requiring time-consuming trial-and-error adjustments.
Solution Approach 2:
The system implements feedback by providing operators with parameter setting recommendations based on statistical analysis of historical data. The recommendation information is generated by comparing current parameter values against statistical distribution characteristics (such as mean, standard deviation, or percentile ranges) from historical protocols, giving operators immediate feedback on whether their parameter settings are within acceptable ranges or need adjustment.
2Adaptability or versatility
If operators manually adjust protocol parameters, then they can adapt to specific patient conditions, but scanning efficiency and accuracy are reduced due to the trial-and-error process
Solution Approach 1:
The system performs preliminary analysis of historical protocol data to establish statistical distribution characteristics before actual scanning. This pre-processing allows the system to quickly generate parameter recommendations for new scanning cases without requiring operators to perform time-consuming trial-and-error adjustments, thereby maintaining high scanning efficiency while still allowing for patient-specific adaptations.
Solution Approach 2:
The system introduces an intermediary layer between the operator and the scanning protocol parameters. Instead of operators directly adjusting parameters through trial-and-error, the system provides recommended parameter values as an intermediary, which operators can accept or modify based on patient-specific considerations. This intermediary significantly reduces the time and effort required for parameter adjustment while maintaining the ability to adapt to individual patient needs.
3Productivity
If the system provides automated parameter recommendations, then scanning efficiency improves, but the complexity of the system increases
Solution Approach 1:
The system uses copying by creating a database of historical protocol parameters and their statistical distribution characteristics. Instead of developing complex real-time calculation models, the system copies historical data patterns and uses them to generate recommendations for new scanning cases. This approach maintains high scanning efficiency while keeping the system relatively simple, as it relies on storing and querying historical data rather than performing complex computations during scanning.
Solution Approach 2:
The system performs all complex statistical analysis and parameter optimization calculations in advance during the preliminary action phase, before actual scanning occurs. By pre-calculating statistical distribution characteristics from historical data and storing them for quick retrieval, the system avoids the need for complex real-time computations during scanning, thereby maintaining high scanning efficiency without significantly increasing operational complexity.
Data Source
AI summary
A method for setting protocol parameters, including: obtaining a current setting value of a target protocol parameter in a scanning protocol to be verified, the current setting value being set by a user through a terminal device; determining a statistical distribution situation of historical setting values of the target protocol parameter and a relative position of the current setting value in the statistical distribution situation; and generating prompt information relating to parameter setting based on the relative position.