RF Transmit Assembly Configuration for MRI Systems
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Solution Overview
Problem
Current MRI RF transmit chains face challenges in optimizing RF pulse sequences and configuration parameters due to complex interdependencies and non-linear relationships among numerous parameters, making manual ad-hoc configuration impractical and inaccurate.
Innovation Solution
A method utilizing a machine learning module that applies different algorithms for optimizing RF transmit assembly configuration, including pulse generation, digital pre-distortion, and impedance matching, based on current or desired operating conditions, to determine optimal configuration parameters and automate the operation of the RF transmit assembly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual ad-hoc configuration of RF transmit assembly parameters is used, then operator control and flexibility are maintained, but configuration accuracy and optimization quality deteriorate due to complex interdependencies among numerous parameters
Solution Approach 1:
The system performs self-configuration by automatically determining optimal configuration parameters for the RF transmit assembly. The determination unit autonomously analyzes operating conditions and computes configuration parameters without requiring manual operator intervention, enabling the system to serve itself in the configuration process while maintaining high accuracy despite complex parameter interdependencies
Solution Approach 2:
The patent replaces manual mechanical configuration processes with automated computational determination. Instead of operators manually adjusting parameters, a determination unit using computational algorithms automatically calculates optimal configuration parameters based on operating conditions, substituting human mechanical adjustment with automated electronic determination
2Manufacturing precision
If the number of configuration parameters is increased to improve optimization quality, then configuration accuracy improves, but device complexity and computational requirements worsen
Solution Approach 1:
The system transforms the configuration problem by changing parameters from manual adjustment values to computationally determined optimal values. The determination unit calculates configuration parameters as functions of operating conditions, transforming the complexity from manual parameter management to automated computational optimization that can handle numerous parameters efficiently
Solution Approach 2:
The determination unit acts as an intermediary between operating conditions and configuration parameters. It mediates the complex relationship by automatically computing optimal configuration parameters based on current operating conditions, eliminating the need for operators to directly manage complex parameter interdependencies while maintaining high optimization quality
3Productivity
If automated configuration is implemented to reduce operator intervention, then productivity and consistency improve, but system complexity increases
Solution Approach 1:
The RF transmit assembly performs self-configuration through automated determination of configuration parameters. The system serves itself by automatically analyzing operating conditions and determining optimal parameters without external operator intervention, thereby improving productivity and consistency while the automation complexity is contained within the determination unit
Solution Approach 2:
The determination unit serves multiple functions: it analyzes operating conditions, computes configuration parameters, and adapts to different operating scenarios. This multi-functionality consolidates what would otherwise require separate systems into a single universal determination unit, improving productivity without proportionally increasing overall system complexity
Data Source
AI summary
The present disclosure relates to a method for configuring a radio frequency, RF, transmit assembly (200) for a magnetic resonance imaging system (300) for acquiring magnetic resonance imaging data from a subject within an imaging zone using an RF pulse sequence, the RF transmit assembly (200) comprising an RF amplifier (215) and a transmit coil (213), wherein the RF transmit assembly (200) is configurable with a set of configuration parameters. The method comprises: providing (3001) operating conditions of the RF transmit assembly, the operating conditions being indicative of at least: a property of the RF pulse sequence and a measurable parameter that influences the RF pulse sequence property when operating the RF transmit assembly using the RF pulse sequence; using (3003) a predefined machine learning model for determining, for the operating conditions, at least part of the set of configuration parameters and associated values; configuring (3005) the RF transmit assembly (200) in accordance with the determined configuration parameters and associated values.