Ultrasound Imaging Parameter Tuning via Machine Learning
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Solution Overview
Problem
Current medical ultrasound imaging systems face challenges in efficiently tuning imaging parameters to produce high-quality diagnostic images across diverse patient types, anatomies, and user preferences, leading to increased examination time and operator fatigue.
Innovation Solution
A machine learning-based approach that utilizes a neural network to automatically optimize imaging parameters by learning from user interactions during patient examinations, incorporating patient, location, and user information to provide personalized settings for improved image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual tuning of imaging parameters is performed for each patient, then diagnostic image quality can be optimized, but examination time increases and operator fatigue increases
Solution Approach 1:
The system automatically selects and adjusts imaging parameters without requiring manual operator intervention. The processor autonomously processes patient information and generates optimized imaging settings, allowing the system to serve itself rather than requiring continuous human input for parameter tuning.
Solution Approach 2:
Manual parameter adjustment (mechanical/operator-based system) is replaced with an automated computational system. The processor uses algorithms to automatically determine imaging parameters based on patient information, substituting the manual mechanical adjustment process with an automated electronic decision-making system.
2Manufacturing precision
If manual tuning of imaging parameters is performed for each patient, then diagnostic image quality can be optimized, but operator fatigue increases
Solution Approach 1:
The system performs self-adjustment of imaging parameters without requiring operator intervention. The automated processor handles parameter optimization, freeing the operator from repetitive manual tuning tasks and reducing associated fatigue.
Solution Approach 2:
The manual operator-based parameter tuning system is replaced with an automated computational system. This substitution eliminates the physical and cognitive burden on operators while maintaining or improving image quality through consistent algorithmic optimization.
3Adaptability or versatility
If factory presets are used for different patient types, then some standardization is achieved, but they cannot cover the large variety of patient types and user preferences
Solution Approach 1:
The system transitions from static factory presets to dynamic, adaptive parameter selection. The processor continuously adjusts imaging parameters based on real-time patient information and characteristics, allowing the system to adapt to any patient type rather than being constrained to pre-defined categories.
Solution Approach 2:
The system automatically varies imaging parameters based on patient-specific information. Instead of selecting from fixed preset groups, the processor dynamically modifies parameters such as frequency, focus, and depth to match the specific anatomical and diagnostic requirements of each patient.
4Extent of automation
If anatomy-focused segmentation is used to set imaging parameters, then some automation is achieved, but it requires expert review for training data and does not address other patient variability
Solution Approach 1:
The system creates a universal parameter selection framework that handles multiple patient types, anatomies, and diagnostic scenarios through a single automated process. The processor uses general patient information to determine appropriate imaging parameters, eliminating the need for separate expert-curated training datasets for different anatomical segments.
Data Source
AI summary
Machine learning trains to tune settings. For training, the user interactions with the image parameters (i.e., settings) as part of ongoing examination of patients are used to establish the ground truth positive and negative examples of settings instead of relying on an expert review of collected samples. Patient information, location information, and/or user information may also be included in the training data so that the network is trained to provide settings for different situations based on the included information. During application, the patient is imaged. The initial or subsequent image is input with other information (e.g., patient, user, and/or location information) to the machine-trained network to output settings to be used for improved imaging in the situation.