Magnetic Resonance Gradient Pulse Optimization
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Solution Overview
Problem
Current magnetic resonance systems face challenges in optimizing pulse sequences due to high gradient strengths and slew rates, leading to noise exposure, increased power consumption, and hardware stress, with existing optimization methods being computationally intensive and time-consuming.
Innovation Solution
A method for optimizing pulse sequences by determining optimizable time intervals and defining gradient curves using linear functions that connect start and end values, ensuring compliance with boundary conditions, which reduces slew rates and calculation time, and can be implemented in existing systems with minimal effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If high gradient strengths and high slew rates are used to achieve strict timing specifications and short total duration, then the imaging speed and resolution are improved, but noise exposure increases and hardware stress increases
Solution Approach 1:
The patent applies parameter changes by optimizing gradient pulse parameters (amplitude, duration, shape) to achieve the required imaging performance with lower peak slew rates. The system dynamically adjusts gradient parameters within each pulse sequence to maintain timing specifications while reducing maximum slew rate values from 200 mT/m/ms to lower levels, thereby reducing noise exposure and hardware stress.
Solution Approach 2:
The patent implements dynamics through real-time optimization of gradient pulse sequences where parameters are dynamically adjusted based on boundary conditions. The optimization process dynamically modifies gradient waveforms to achieve strict timing specifications while maintaining lower slew rates, allowing the system to adapt gradient parameters during the pulse sequence execution.
2Measurement precision
If high gradient strengths and high slew rates are used to achieve strict timing specifications, then the imaging resolution is improved, but power consumption increases
Solution Approach 1:
The patent reduces power consumption by optimizing gradient pulse parameters to achieve required imaging resolution with lower peak slew rates. The optimization process adjusts gradient amplitude and duration parameters to maintain timing specifications and image quality while reducing the energy demand on gradient coils and power supply systems.
3Speed
If high gradient strengths and high slew rates are used to achieve strict timing specifications, then the imaging speed is improved, but hardware stress increases
Solution Approach 1:
The patent reduces hardware stress by optimizing gradient pulse parameters to achieve required imaging speed with lower peak slew rates. The optimization process adjusts gradient waveform parameters to maintain timing specifications while reducing mechanical and thermal stress on gradient coils, amplifiers, and the superconducting magnet system, thereby reducing helium boil-off.
4Stability of the object's composition
If spline interpolation is used to optimize gradient curves, then the gradient smoothness is improved, but calculation time increases
Solution Approach 1:
The patent applies segmentation by dividing the gradient optimization problem into discrete time intervals with specific boundary conditions. Instead of applying complex spline interpolation across the entire gradient sequence, the system segments the optimization into manageable intervals that can be processed more efficiently while maintaining gradient smoothness through boundary condition constraints.
Solution Approach 2:
The patent changes the optimization approach from complex spline interpolation to a parameter-based optimization method that adjusts gradient pulse parameters directly. This parameter change simplifies the calculation process while maintaining gradient smoothness through optimized parameter selection, significantly reducing calculation time for real-time applications.
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 allows for rapid optimization of pulse sequences with reduced slew rates and noise exposure, minimizing computational burden and hardware stress, while maintaining image quality, and is suitable for real-time applications.
Implementation Method 1
A magnetic field gradient is additionally applied with the aid of a gradient system
Implementation Method 2
Radio-frequency excitation signals (RF signals) are then emitted via a radio-frequency transmission system, which leads to the situation that the nuclear spins of specific atoms excited to resonance by this radio-frequency field are flipped
Implementation Method 3
Eddy currents with other components of the magnetic resonance tomograph (in particular the radio-frequency shield) are one reason for these noise exposures
Data Source
AI summary
In a method and a pulse sequence optimization device to determine a pulse sequence for a magnetic resonance system, a determination of a time interval in a pulse sequence that is to be optimized with regard to a gradient curve initially takes place, the pulse sequence including a number of radio-frequency pulses and a number of gradient pulses that are to be chronologically coordinated, under determination of the following boundary conditions for the gradient curve: length of the time interval, target integral of the gradient curve over the time interval, start value of the gradient curve at the beginning of the time interval, end value of the gradient curve at the end of the time interval. A gradient curve is then defined according to a function that is linear per segment by addition of a first linear function that connects the start value and the end value and a second function that is linear per segment that assumes a function value of “zero” at the beginning and end of the time interval, and that is defined so that the sum of the integral of the first linear function and of the integral of the second function corresponds to the target integral.