Neural Network MR Scan Parameter Optimization
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Solution Overview
Problem
Current magnetic resonance (MR) imaging systems face challenges in achieving optimal image quality due to the complexity of selecting multiple scan parameters and the limited knowledge of technicians, often resulting in sub-optimal parameter settings that degrade image quality under time constraints.
Innovation Solution
A neural network-based system that maps image quality metrics to corresponding scan parameters, allowing for the automatic generation of optimal scan settings for MR imaging devices, reducing the need for user expertise and minimizing manual configuration time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If technicians manually select scan parameters based on their knowledge, then image quality can be optimized, but the process requires high level of expertise and time which are often limited
Solution Approach 1:
The system enables self-service by allowing the MR imaging system to automatically select and optimize scan parameters without requiring technician expertise. The neural network model autonomously analyzes imaging requirements and determines optimal parameter settings, eliminating the need for manual parameter selection by technicians.
Solution Approach 2:
The patent replaces the manual mechanical process of parameter selection with an automated computational system. A neural network model substitutes the technician's knowledge and decision-making process, using machine learning algorithms to automatically determine optimal scan parameters based on imaging requirements.
2Productivity
If technicians are pressed for time, then productivity increases, but parameter selection becomes sub-optimal degrading image quality
Solution Approach 1:
The system performs preliminary action by pre-training the neural network model with extensive parameter optimization data before actual scanning. The model is prepared in advance to quickly provide optimal parameter recommendations during scanning, enabling fast automated parameter selection without compromising image quality despite time constraints.
3Manufacturing precision
If multiple scan parameters are available for selection, then image quality can be optimized, but the device complexity and difficulty of parameter selection increase
Solution Approach 1:
The patent extracts the complexity of parameter selection from the technician's task and consolidates it into the neural network model. The model internally processes the complex interactions between multiple parameters, presenting a simplified interface where technicians only need to specify basic imaging requirements while the system handles the complex parameter optimization automatically.
4Ease of operation
If technicians lack knowledge of parameter interactions, then ease of operation is maintained, but image quality is greatly degraded
Solution Approach 1:
The neural network model acts as an intermediary between the technician's simple input requirements and the complex parameter settings needed for optimal image quality. The model translates basic imaging specifications into detailed parameter configurations, bridging the knowledge gap without requiring technicians to understand complex parameter interactions.
Data Source
AI summary
Apparatus, systems, and methods for improved imaging device configuration are disclosed and described. An example apparatus includes a memory storing a first neural network that is trained to map image quality metrics to corresponding scan parameters. The example apparatus includes a processor configured to: receive specified image quality metrics; instruct the trained first neural network to generate scan parameters based on the specified image quality metrics to configure an imaging device for image acquisition; and instruct the imaging device to acquire one or more resulting images using the generated scan parameters.