Ultrasound Scan Parameter Selection for Reproducible Image Quality
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Solution Overview
Problem
The adjustment of ultrasound image acquisition parameters is subjective and lacks a defined process, leading to irreproducible results and suboptimal image quality, particularly in medical ultrasound imaging.
Innovation Solution
An automated image quality measurement algorithm using artificial intelligence assists in optimizing acquisition parameters by acquiring multiple images at different combinations of scan parameter values, identifying the optimal settings based on image quality metrics, and applying post-acquisition processing parameters to enhance image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple images are acquired at different combinations of parameter values to optimize image quality, then image quality improves, but examination time increases
Solution Approach 1:
The system performs preliminary automated parameter optimization by acquiring a set of images at different parameter combinations and using AI algorithms to identify optimal parameters before the main examination. This preliminary action establishes optimal scan parameters (such as focal depth, frequency, and gain) that will be used for subsequent image acquisition, thereby improving image quality without requiring multiple manual adjustments during the actual examination.
Solution Approach 2:
The system implements self-service through automated parameter selection using artificial intelligence algorithms. The ultrasound system automatically analyzes the acquired images, determines the optimal parameter combinations, and adjusts parameters without operator intervention. This self-service mechanism eliminates the need for manual parameter tuning by operators while maintaining high image quality, thus resolving the contradiction between optimization thoroughness and examination time.
2Reliability
If automated parameter selection is implemented, then reproducibility of results improves, but system complexity increases
Solution Approach 1:
The system employs feedback mechanisms where AI algorithms continuously analyze acquired images and adjust scan parameters based on image quality metrics. The system acquires images at different parameter combinations, evaluates their quality using automated algorithms, and uses this feedback to identify and select the optimal parameter set. This closed-loop feedback process ensures reproducible results by consistently selecting parameters that maximize image quality across different examinations and operators.
Solution Approach 2:
The system replaces manual parameter adjustment (mechanical/operator-based system) with automated AI-driven parameter selection (algorithm-based system). Instead of relying on operator experience and manual tuning, the system uses machine learning algorithms to automatically determine optimal parameters. This substitution reduces system complexity from the operator's perspective while improving reproducibility, as the automated system eliminates human variability in parameter selection.
3Ease of operation
If manual parameter adjustment is used, then operator expertise is required, but this leads to subjective and irreproducible results
Solution Approach 1:
The system enables self-service by automatically performing parameter optimization without requiring operator expertise. The AI algorithms independently analyze images and select optimal parameters, making the system self-sufficient in parameter selection. This eliminates the dependency on operator skill levels while ensuring consistent, objective, and reproducible results across different users and examinations.
Solution Approach 2:
The system implements automated parameter changes based on AI analysis rather than manual operator adjustment. The system dynamically modifies scan parameters (such as frequency, depth, focal zones, and gain) based on real-time image quality assessment. This automated parameter change process transforms subjective manual adjustment into objective algorithm-driven optimization, significantly improving reproducibility while maintaining ease of operation.
Data Source
AI summary
Methods and systems are provided for improving image quality of ultrasound images by jointly selecting optimal scan parameter values. In one example, a method includes acquiring a plurality of ultrasound images of an anatomical region, each ultrasound image acquired at a different combination of parameter values for a first scan parameter and a second scan parameter, selecting a first parameter value for the first scan parameter and a second parameter value for the second scan parameter based on an image quality of each image, and acquiring one or more additional ultrasound images at the first parameter value and the second parameter value.