MRI Magnet Shimming Using Machine Learning for Field Homogeneity
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Solution Overview
Problem
Current methods for correcting magnetic field inhomogeneities in MRI magnets are complex, time-consuming, and user-dependent, requiring specialized skills and manual adjustments, with no objective protocol for ensuring repeatable outcomes.
Innovation Solution
A machine learning algorithm is used to predict the distribution of correction elements on a grid based on measured magnetic field data, trained on a database of known cases, to simplify and accelerate the shimming process while maintaining precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If analytical methods are used to correct magnetic field inhomogeneities, then correction precision can be achieved, but the process becomes extremely complex and time-consuming
Solution Approach 1:
The patent replaces complex analytical mathematical methods with a machine learning model that has been pre-trained on magnetic field correction data. The model automatically predicts correction element distributions without requiring manual analytical calculations, thereby reducing process complexity while maintaining correction precision.
Solution Approach 2:
The machine learning model is pre-trained offline on a database of magnetic field measurements and corresponding optimal correction solutions. This preliminary training phase allows the model to quickly provide accurate correction predictions during actual shimming operations without requiring complex real-time analytical computations.
2Measurement precision
If traditional shimming methods are used, then magnetic field inhomogeneities can be corrected, but the process requires highly specialized personnel and manual adjustments
Solution Approach 1:
The machine learning model performs the correction prediction automatically based on input magnetic field measurements. The system self-determines the optimal distribution of correction elements without requiring specialized human operators to perform manual calculations or adjustments, making the process easier to operate while maintaining precision.
Solution Approach 2:
The patent replaces manual expert judgment and manual adjustment procedures with an automated machine learning system. The model processes measurements and generates correction predictions automatically, eliminating the need for highly specialized personnel to perform manual shimming operations.
3Measurement precision
If repeated corrections are performed to achieve optimal magnetic field homogeneity, then field uniformity improves, but the time required increases significantly
Solution Approach 1:
The machine learning model is pre-trained on a comprehensive database of magnetic field configurations and optimal corrections. This preliminary training allows the model to provide accurate correction predictions in a single pass without requiring multiple iterative corrections, significantly reducing the time required while maintaining field uniformity.
Solution Approach 2:
The system uses measured magnetic field data as input to the machine learning model, which then predicts the optimal correction element distribution. This feedback-based approach allows the system to determine the correct correction in one step rather than requiring repeated trial-and-error adjustments, reducing time while achieving the desired field uniformity.
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
The method allows for efficient and precise reduction of magnetic field inhomogeneities in a fraction of the time required by traditional methods, without the need for analytical equations, and provides repeatable outcomes.
Implementation Method 1
measuring the magnetic field and sampling it in a plurality of locations
Implementation Method 2
calculating the position and magnitude parameters of one or more correction elements to obtain predetermined target values of the field characteristics
Data Source
Figure 1A~1B
Figure 2
Figure 3~4
AI summary
A method for shimming a magnetic field of an MRI magnet structure is provided comprising the following steps: a) measuring the magnetic field and sampling it in a plurality of locations, with a predetermined space distribution within a predetermined volume of space permeated by the said magnetic field and delimited by a surface boundary and/or also on the said surface boundary; b) defining a grid for positioning the correction elements, depending on the magnet structure and on the correlation thereof with the field structure; c) calculating the position and magnitude parameters of one or more correction elements to obtain predetermined target values of the field characteristics, in which the step c) is carried out by a machine learning algorithm or combinations thereof, which algorithm has been trained by a database of known cases in which each record links a certain initial magnetic field of a magnetic structure to the pattern of correction elements on a positioning grid for these correction elements in the magnet structure, thereby delivering as an output a pattern of correction elements on the positioning grid which contributions to the magnetic field of the magnet structure generate a magnetic field which at least best approximates or meets the said predetermined target values of the field characteristics.