Magnetic Resonance Pulse Optimization with Dynamic Gradient Grid Density
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Solution Overview
Problem
Magnetic resonance systems face issues with scan abortions due to time constraints in pulse optimization, particularly in real-time applications, where the calculation time for gradient waveforms exceeds the available time, leading to acoustic noise, high energy consumption, and hardware stress.
Innovation Solution
An apparatus and method that check the optimization time for pulse sequences and reduce the gradient grid density if it exceeds real-time limits, allowing for flexible handling of pulse optimization variants, including bypassing optimization if necessary, to prevent scan abortions by directly forwarding commands to the scanner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If pulse optimization is performed with high gradient grid density to ensure precision, then manufacturing precision is improved, but loss of time increases causing scan abortions
Solution Approach 1:
The patent implements dynamic adjustment of gradient grid density based on available time. The system checks whether optimization time exceeds real-time limits and adapts the gradient grid density accordingly, transitioning from fixed high-density optimization to flexible density selection. This resolves the contradiction by making the optimization process adaptive to time constraints while maintaining precision when possible.
Solution Approach 2:
The patent changes the parameter of gradient grid density from a fixed high value to a variable parameter that can be adjusted based on time availability. By modifying this key parameter dynamically, the system balances optimization precision against time consumption, preventing scan abortions while maintaining quality where time permits.
2Loss of time
If gradient grid density is reduced to decrease optimization time, then loss of time is reduced, but manufacturing precision deteriorates
Solution Approach 1:
The system dynamically selects gradient grid density based on time constraints rather than using a fixed low density. This allows the system to maintain high precision when time is available while reducing precision only when necessary to meet real-time deadlines, optimizing the balance between speed and quality.
Solution Approach 2:
The patent performs a preliminary check of optimization time before executing the optimization. This advance assessment allows the system to pre-determine the appropriate gradient grid density, preventing both unnecessary time consumption and unnecessary precision loss by making an informed decision before the optimization process begins.
3Productivity
If pulse optimization is bypassed to ensure real-time performance, then productivity is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system dynamically determines whether to perform optimization and at what gradient grid density based on available time. This conditional approach allows the system to maintain high productivity by bypassing optimization only when absolutely necessary, while preserving precision by performing full optimization when time permits, thus resolving the contradiction between speed and quality.
Solution Approach 2:
The patent implements a graduated approach where optimization may be performed partially (with reduced gradient grid density) rather than completely bypassed. This partial action maintains some level of precision improvement while still meeting real-time constraints, avoiding the extreme of complete optimization bypass.
Data Source
AI summary
In an apparatus and method for pulse optimization adjustment a checking is made as to whether the optimization time resulting from a calculation time for pulse optimization of a pulse sequence section for a modifiable time interval at a predefined gradient grid density, and an associated implementation time, exceeds a real time resulting from the time interval and a buffer time. The gradient grid density for pulse optimization is reduced by a factor f if the optimization time exceeds the real time.