Ultrasound System Dynamic Parameter Adjustment via Organ Detection
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Solution Overview
Problem
Ultrasound imaging systems face challenges in obtaining optimal imaging parameters for different organs during a single scanning session, leading to suboptimal images and incorrect measurements due to the need for manual preset selection, which is time-consuming and prone to errors, especially in urgent care settings.
Innovation Solution
An ultrasound imaging system that automatically adjusts imaging parameters based on real-time organ detection using a machine-learning model, such as a neural network, to identify the type of tissue and apply the appropriate preset settings, including user- and patient-specific preferences, thereby eliminating the need for manual selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual preset selection is used, then the system can be operated with simple controls, but the scanning efficiency and time consumption are poor
Solution Approach 1:
The system automatically detects the organ type from the ultrasound image and selects the appropriate preset without requiring manual user input. The processor identifies the organ and autonomously configures imaging parameters, allowing the system to serve itself rather than relying on user expertise and time for manual selection.
Solution Approach 2:
The system pre-configures multiple organ-specific presets with optimized imaging parameters before the examination begins. When an organ is detected, the corresponding preset is immediately applied, eliminating the need for real-time manual adjustment and ensuring optimal settings are ready in advance for each organ type.
2Reliability
If multiple presets are provided for different organs, then the system can be adapted to different imaging applications, but the user may accidentally select the wrong preset leading to errors
Solution Approach 1:
The system continuously monitors the ultrasound image content, detects the organ type being imaged, and automatically selects the appropriate preset based on this feedback. This closed-loop approach ensures the correct preset is always applied by using the image content itself as feedback to guide parameter selection, eliminating guesswork and accidental misselection.
Solution Approach 2:
The processor acts as an intermediary between the user and the multiple presets. Instead of directly exposing users to the complexity of selecting from many presets, the processor automatically matches the detected organ type with the appropriate preset, mediating the interaction and shielding users from the complexity while maintaining access to multiple specialized configurations.
3Measurement precision
If manual adjustment of imaging parameters is required, then the system can be operated with basic automation, but the measurement precision and image quality are suboptimal
Solution Approach 1:
The system automatically changes multiple imaging parameters (depth, focus, frequency, gain, TGC, imaging mode) based on the detected organ type. By dynamically adjusting these parameters according to the specific organ being examined, the system achieves optimal image quality and measurement precision without requiring manual intervention, with each organ type having its optimized parameter set.
Data Source
AI summary
The present disclosure describes ultrasound imaging systems and methods for ultrasonically inspecting biological tissue. An ultrasound imaging system according to the present disclosure may be configured to automatically apply tissue-specific imaging parameter settings (312, 314) based upon the automatic identification of the type of tissue being scanned. Tissue type identification (315) may be performed automatically for the images in the live image stream (304) and thus adjustments to the imaging settings may be applied automatically by using a neural network (320) and thus dynamically during the exam obviating the need for the sonographer to manually switch presets or adjust the imaging settings when moving to different portion of the anatomy.