RF Pulse Design via Reinforcement Learning for MRI Magnetization
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Solution Overview
Problem
Designing RF pulses for MRI that accurately generate desired magnetization profiles is challenging due to the non-linearity of the system, especially when requirements include narrow transition bands, short RF duration, restricted energy deposition, and insensitivity to system imperfections, with existing methods like small tip angle and linear class large tip angle approximations leading to errors and distortions in magnetization profiles.
Innovation Solution
Employing a reinforcement learning strategy to design RF pulse sequences by simulating the effects of RF pulses and gradients using a Bloch simulator, where a processor identifies a sequence of RF pulses that minimizes the difference between the desired and computed magnetization, using a reinforcement machine-learnt classifier to select subsequent pulses until the desired threshold is met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional RF pulse design methods (small tip angle approximation, linear class large tip angle approximation) are used, then the design process is simplified, but errors and distortions in magnetization profiles occur
Solution Approach 1:
The patent replaces conventional approximation-based mathematical methods with a deep neural network-based computational approach. The DNN is trained to directly predict RF pulse parameters from desired magnetization profiles, eliminating the need for iterative Bloch simulations and approximation methods, thereby achieving both high accuracy and computational efficiency.
Solution Approach 2:
The patent creates a learned model (DNN) that copies the complex nonlinear relationship between RF pulses and magnetization profiles by training on extensive simulation data. Once trained, the DNN can rapidly predict accurate RF pulse parameters without requiring repeated Bloch simulations, effectively copying the behavior of the complex physical system.
2Manufacturing precision
If narrow transition bands and short RF duration are required, then image quality improves, but the complexity of RF pulse design increases significantly
Solution Approach 1:
The patent replaces complex iterative optimization procedures with a trained deep neural network that directly predicts RF pulse parameters. The DNN learns to satisfy multiple constraints (narrow transition bands, short duration, energy limits) simultaneously during training, eliminating the need for complex post-design optimization.
Solution Approach 2:
The patent transforms the RF pulse design problem from solving differential equations to a parameter prediction problem. The DNN takes desired magnetization profile parameters as input and directly outputs optimal RF pulse parameters, changing the problem domain from continuous differential equations to discrete parameter optimization.
3Manufacturing precision
If iterative Bloch simulation methods are used to design RF pulses, then accurate magnetization profiles can be achieved, but the design time and computational resources increase
Solution Approach 1:
The patent performs preliminary training of the deep neural network using extensive Bloch simulations to learn the underlying physics and optimal pulse designs. Once trained, the DNN can rapidly predict RF pulse parameters without requiring repeated iterative simulations, effectively pre-computing the complex relationships for future rapid prediction.
Solution Approach 2:
The patent creates a learned model (DNN) that copies the complex nonlinear relationship between RF pulses and magnetization profiles by training on extensive simulation data. Once trained, the DNN can rapidly predict accurate RF pulse parameters without requiring repeated Bloch simulations, effectively copying the behavior of the complex physical system.
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
This approach enables the generation of accurate and efficient RF pulse sequences that produce precise magnetization profiles, reducing errors and distortions, thereby improving the quality of MRI images with clearer visualization and optimal contrast.
Implementation Method 1
The change in magnetization and gradient fields may be solved by Bloch equations
Implementation Method 2
A RF pulse is applied. The pulse causes the magnetization to change
Implementation Method 3
Once the RF signal is removed, the nuclei realign themselves such that the net magnetic moment returns. The return to equilibrium is referred to as relaxation. During relaxation, the nuclei lose energy by emitting a RF signal
Data Source
AI summary
Systems and methods are provided for automatically designing RF pulses using a reinforcement machine-learnt classifier. Data representing an object and a selected outcome is accessed. A reinforcement learnt method identifies the RF pulse sequence that generates a result within a predefined value of the selected outcome. An MRI scanner images the object using the RF pulse sequence.