Medical Image Diagnostic System Timing Determination
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Solution Overview
Problem
Determining the optimal timing for transitioning from a pre-scan to a main scan in X-ray CT examinations is challenging due to difficulties in setting an appropriate threshold value for signal intensity, leading to inconsistencies in image quality and diagnostic accuracy.
Innovation Solution
A medical image diagnostic system that generates a trained model using time-series images and signal intensity data from pre-scan examinations to determine the appropriate timing for transitioning to a main scan in subsequent examinations, eliminating the need for a threshold value by analyzing signal intensity trends over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a threshold value is used to determine transition timing from pre-scan to main scan, then the timing can be determined automatically, but the image quality and diagnostic accuracy become inconsistent due to difficulty in setting an optimal threshold
Solution Approach 1:
The patent changes the approach from using a fixed threshold value to using a dynamic reference value derived from actual pre-scan data. The system calculates the mean and standard deviation of signal intensities from the pre-scan images, then sets the transition timing based on these statistical parameters rather than a predetermined threshold. This adapts the determination criterion to each specific examination case, improving both automation and consistency.
Solution Approach 2:
The system uses feedback from the pre-scan images themselves to determine the main scan timing. By analyzing the actual signal intensity variations in the pre-scan data and comparing them against statistical norms, the system adjusts the transition timing dynamically. This feedback mechanism ensures that the timing determination is based on real-time data rather than fixed parameters, resolving the contradiction between automation and consistency.
2Ease of operation
If a fixed threshold value is set for signal intensity, then the determination process is simple, but it is difficult to set an optimal threshold that works for all cases
Solution Approach 1:
The patent creates a universal determination method that works across different examination cases by using statistical parameters (mean and standard deviation) rather than case-specific fixed thresholds. The system calculates these statistics from the pre-scan data itself, making the method adaptable to varying patient conditions, contrast agent types, and scan protocols while maintaining a consistent computational approach.
Solution Approach 2:
The system determines the optimal threshold automatically by using its own pre-scan data to calculate the statistical parameters. Rather than requiring external input or manual threshold setting, the system self-adjusts the determination criterion based on the actual signal intensity distribution observed during the pre-scan, making it universally applicable without case-specific calibration.
3Manufacturing precision
If manual adjustment of threshold values is performed, then optimal timing can be achieved for specific cases, but the process becomes complex and time-consuming
Solution Approach 1:
The system eliminates the need for manual threshold adjustment by automatically calculating the optimal transition timing based on statistical analysis of pre-scan data. The processing circuitry independently computes the mean and standard deviation of signal intensities and determines the timing criterion without requiring operator intervention, thereby achieving optimal timing while simplifying the overall process.
Solution Approach 2:
The patent replaces the manual mechanical process of threshold setting with an automated computational system. Instead of operators manually adjusting threshold values based on experience and trial-and-error, the system uses algorithmic processing of pre-scan data to automatically determine the optimal timing, reducing both complexity and human involvement while maintaining or improving precision.
Data Source
AI summary
A medical image diagnostic system includes processing circuitry configured (to): (a) acquire a trained model generated by using, as learning data, images or signals corresponding to a first group of time-series images acquired by performing a first pre-scan on a first patient injected with a contrast agent in a first examination, as well as timing information about timing of a transition from a first pre-scan to a first main scan in a first examination, and information about appropriateness of the timing; and (b) determine appropriate timing of a transition from a second pre-scan to a second main scan by inputting, to the trained model, images or signals corresponding to a second group of time-series images acquired by performing the second pre-scan on a second patient injected with a contrast agent in the second examination different from the first examination.