Magnetic Resonance Fingerprinting Dictionary Tuning for Resolution and Speed

The patent applies parameter changes by systematically varying the step size parameters for different tissue properties (T1, T2, PD) to find the optimal configuration. The fitness function evaluates different parameter combinations and identifies the target step size that achieves the best balance between image resolution and imaging velocity, resolving the contradiction between these two opposing requirements.

The patent implements dynamics by using an iterative optimization process that dynamically adjusts the dictionary parameters based on fitness evaluation. The system transitions from static, user-defined step sizes to dynamic, optimized target step sizes that are determined through multiple iterations of the fitness function, allowing the system to adaptively find the optimal balance between resolution and velocity.

The patent uses parameter changes to systematically explore different dictionary configurations by modifying the step size parameters. The fitness function evaluates how different parameter settings affect both imaging velocity and image resolution, identifying the target step size that prevents resolution deterioration while maintaining improved velocity.

The iterative optimization process dynamically adjusts dictionary parameters to find the optimal size configuration. Through multiple iterations, the system learns the appropriate balance point where imaging velocity is maximized without sacrificing image resolution, transforming the static dictionary into a dynamically optimized structure.

The patent implements feedback by introducing a fitness function that evaluates the quality of different dictionary configurations based on both image resolution and imaging velocity. This feedback mechanism guides the optimization process, allowing the system to automatically adjust step size parameters to achieve optimal performance without requiring complex user input, thus maintaining ease of manufacture while improving precision.

The system applies self-service by enabling automatic optimization of dictionary parameters through the fitness function and iterative process. Instead of requiring users to manually define optimal step sizes, the system autonomously determines the target step size that simultaneously optimizes image resolution and imaging velocity, freeing users from complex parameter tuning while achieving superior results.

The fitness function provides feedback on imaging velocity performance for different step size configurations. This feedback enables the system to automatically identify and select parameter settings that maximize imaging velocity, maintaining the simplicity of dictionary generation while dramatically improving velocity performance through data-driven optimization.

The system performs self-service optimization by automatically determining the optimal step size parameters that maximize imaging velocity. The iterative fitness evaluation process autonomously identifies velocity-optimizing configurations without requiring user expertise, maintaining ease of manufacture while achieving superior imaging velocity through intelligent self-optimization.