Ultrasound Imaging System Automatic Parameter Adjustment
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Solution Overview
Problem
Conventional ultrasound imaging systems require manual adjustment of numerous parameters by operators, leading to time-consuming setups and difficulties in optimizing image quality due to varying patient conditions, such as weight, fat content, and tissue density.
Innovation Solution
An ultrasound imaging system that uses machine learning classifiers, like multilayer perceptron neural networks, to automatically identify anatomical regions and adaptively modify image parameters, eliminating the need for manual preset selection and optimization, by learning user preferences and adjusting settings based on received signals and image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of transducer parameters is used, then operators can optimize image settings for specific cases, but setup time increases and consistency decreases
Solution Approach 1:
The system automatically identifies the body structure being scanned and selects optimal image parameters without requiring manual operator intervention. The processor autonomously adjusts transducer settings based on the detected anatomy, eliminating the time-consuming manual tuning process while maintaining optimized image quality.
Solution Approach 2:
The system dynamically changes multiple transducer parameters (frequency, power, gain, dynamic range, persistence) based on the identified body structure. By automatically adjusting these parameters according to the specific anatomy being scanned, the system achieves optimized image quality without manual intervention.
2Measurement precision
If manual parameter optimization is required, then image quality can be tailored to specific patients, but operator expertise and time are needed
Solution Approach 1:
The system performs automatic body structure identification and parameter selection, eliminating the need for operators to manually optimize settings. The processor autonomously handles the entire parameter adjustment process based on the detected anatomy, reducing operator burden while maintaining tailored image quality.
Solution Approach 2:
The system replaces manual operator judgment and adjustment with automated computer-based identification and parameter selection. The processor uses signal analysis and machine learning algorithms to substitute for human expertise in selecting optimal imaging parameters.
3Measurement precision
If numerous transducer parameters need adjustment, then optimal images can be achieved, but the complexity of the system increases
Solution Approach 1:
The system combines multiple parameter adjustment functions into a single automated body structure identification and parameter selection process. The processor integrates the identification of anatomy type with the selection of optimal parameters, simplifying the overall system operation despite the multiple parameters involved.
Solution Approach 2:
The system automatically manages the complexity of multiple parameter adjustments by autonomously selecting and applying optimal settings based on the identified body structure. This self-service approach handles the parameter complexity internally without requiring the operator to understand or manage the individual parameters.
4Loss of time
If preset parameters are pre-programmed, then setup time is reduced, but adaptability to varying patient conditions decreases
Solution Approach 1:
The system automatically changes transducer parameters based on the identified body structure and patient-specific conditions. By dynamically adjusting frequency, power, gain, and other parameters according to the detected anatomy, the system maintains adaptability to varying patient conditions while eliminating manual setup time.
Solution Approach 2:
The system transitions from static pre-programmed presets to dynamic automatic parameter selection. The processor continuously adapts the imaging parameters based on real-time analysis of the received signals and identified body structure, providing both speed and adaptability.
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 solution significantly reduces setup time, improves exam quality and consistency, and lowers development costs by automating the calibration process, allowing users and developers to focus on system development rather than manual parameter tuning.
Implementation Method 1
Ultrasound imaging is accomplished by generating and directing ultrasonic sound waves into a material of interest
Implementation Method 2
During the receive phase, reflections generated by boundaries between dissimilar materials are received by receiving devices, such as transducers
Data Source
AI summary
Aspects of the disclosed technology provide ways to detect the object of ultrasound scanning and to automatically, load system settings and image preferences necessary to generate high quality output images. In some aspects, an ultrasound system can be configured to perform steps including receiving a selection of a first transducer, identifying a body structure or organ based on a signal received in response to an activation of the first transducer, retrieving a first set of parameters corresponding with the body structure, and configuring the first transducer based on the first set of parameters. Methods and machine-readable media are also provided.