Elastic Image Quality Evaluation via Fluctuation Cycle Detection
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Solution Overview
Problem
The evaluation of image quality for elastic images in ultrasonic diagnostics is hindered by fluctuations in the pressing operation, leading to inconsistent and noisy images, which complicates the diagnosis process and requires manual selection from stored images, reducing reliability and accuracy.
Innovation Solution
The method involves detecting the fluctuation cycle and feature quantities of displacement or elasticity information patterns to assess the stability of the pressing operation, allowing for the evaluation and selection of high-quality elastic images based on these metrics, using an ultrasonic diagnostic apparatus with dedicated units for displacement and elasticity calculation, and image quality evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual pressing operation is performed to obtain elastic images, then elasticity information can be acquired for diagnosis, but fluctuation in pressing operation causes noise and reduces image quality reliability
Solution Approach 1:
The system performs preliminary evaluation of image quality by detecting fluctuation cycles and calculating feature quantities before final diagnosis. This preliminary action identifies and filters out images affected by pressing fluctuations, ensuring only reliable images are selected for diagnostic purposes.
Solution Approach 2:
The system implements feedback by evaluating image quality metrics and using this information to select appropriate images for display. The evaluation results feed back into the image selection process, allowing the system to automatically choose images with minimal pressing fluctuation noise.
2Measurement precision
If multiple elastic images are stored and manually selected for diagnosis, then comprehensive evaluation is possible, but time consumption increases significantly
Solution Approach 1:
The system performs self-service by automatically evaluating image quality and selecting the most appropriate images for diagnosis without requiring manual review of all stored images. The automatic selection based on fluctuation cycle detection and feature quantity calculation reduces time loss while maintaining diagnostic accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of image selection with an automated computational system. Instead of manually reviewing and selecting images, the system uses automatic evaluation algorithms to identify and select high-quality images, substituting human time and effort with automated processing.
3Measurement precision
If conventional image quality evaluation methods are used, then some noise assessment is possible, but reliability and accuracy of evaluation remain insufficient
Solution Approach 1:
The system evaluates image quality from another dimension by analyzing fluctuation cycles and temporal patterns in the pressing operation, rather than only spatial image characteristics. This additional temporal dimension provides more comprehensive and reliable quality assessment.
Solution Approach 2:
The patent introduces new evaluation parameters including fluctuation cycle detection and feature quantity calculations that change based on pressing operation stability. These parameter changes enable more accurate and reliable differentiation between high-quality and noisy images.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the reliable and accurate evaluation of image quality, ensuring that only stable and high-quality elastic images are selected for diagnosis, reducing noise and improving the efficiency of the diagnostic process.
Implementation Method 1
an ultrasonic beam is scanned in a periodic basis on a cross-sectional surface including a region of interest... plural sets of RF signal frame data are generated by receiving and processing the ultrasonic wave which is reflected from the biological tissue
Implementation Method 2
two sets of RF signal frame data acquired with different pressure are selected... and the displacement (displacement vector) of the biological tissue is acquired between the two sets of RF signal frame data
Implementation Method 3
the distribution of elasticity information which represents the hardness or softness of the biological tissue in the respective portions of the cross-sectional surface including the region of interest is obtained on the basis of the acquired value of displacement
Data Source
AI summary
Disclosed in an ultrasonic diagnostic apparatus for appropriately evaluating the image quality of a elastic image with high reliability and accuracy, the ultrasonic diagnostic apparatus including: a probe (12) that transmits and receives ultrasonic waves to and from an object; an elasticity information calculating unit (32) that calculates elasticity information on the basis of the ultrasonic waves received by the probe (12); an elastic image constructing unit (34) that generates an elastic image on the basis of the elasticity information; an image display device (display) (26) that displays the elastic image; and an elastic image evaluating unit (40) that detects the fluctuation cycles in the elasticity information, finds the fluctuation patterns in the elasticity information for each predetermined section in the fluctuation cycles, and evaluates the stability of the elastic image on the basis of the fluctuation patterns.