MRI Sub-Volume Control Using Dynamic Base Data Updates
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Solution Overview
Problem
Current medical imaging techniques face inefficiencies in combining static and dynamic adjustments of magnetic resonance apparatus settings, leading to increased measurement time and compromised data quality due to the need for repeated base data acquisition and interactions between different adjustment parameters.
Innovation Solution
A method that allows for simultaneous use of static and dynamic adjustments by saving reference values for setting parameters and updating base data based on differences between present and reference values, eliminating the need for repeated base data measurements and optimizing sub-region settings during the measurement sequence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If static adjustments are performed for the entire measurement sequence, then field homogeneity is improved, but measurement time increases due to repeated base data acquisition
Solution Approach 1:
The patent performs static adjustment once at the beginning of the measurement sequence to establish baseline field homogeneity parameters. These preliminary adjustments are stored and reused for subsequent sub-volumes, eliminating the need to repeat time-consuming base data acquisition while maintaining field quality standards.
Solution Approach 2:
The system dynamically adapts the adjustment strategy by applying static adjustments only once initially, then transitioning to dynamic parameter updates for subsequent sub-volumes. This dynamic approach allows the system to maintain field homogeneity without repeatedly acquiring base data, thus reducing measurement time while preserving image quality.
2Manufacturing precision
If dynamic adjustments are performed for each sub-volume, then image quality is improved, but device complexity increases due to coordination of multiple parameters
Solution Approach 1:
The patent divides the measurement sequence into discrete sub-volumes, each with its own optimized parameters. By segmenting the control strategy, the system can apply tailored adjustments to each sub-volume without managing the entire sequence as one complex unit, thereby improving image quality while keeping the control complexity manageable through modular organization.
Solution Approach 2:
The system applies local optimization by determining specific adjustment parameters for each sub-volume based on its unique characteristics. This local quality approach ensures that each region receives customized adjustments for optimal image quality, while the overall system complexity is reduced by handling each local region independently rather than managing global complexity.
3Productivity
If static and dynamic adjustments are combined, then measurement efficiency is improved, but parameter interactions cause adjustment conflicts
Solution Approach 1:
The patent establishes static adjustments as preliminary baseline parameters before dynamic adjustments are applied to individual sub-volumes. This preliminary action creates a stable foundation that prevents parameter conflicts, allowing the system to combine both adjustment types efficiently while maintaining reliability through a hierarchical parameter management structure.
Solution Approach 2:
The system merges static and dynamic adjustment strategies into a unified control approach. Static adjustments provide global optimization across the entire measurement sequence, while dynamic adjustments provide local optimization for each sub-volume. This combination improves measurement efficiency by leveraging both approaches without causing conflicts through integrated parameter management.
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 reduces measurement time by avoiding the need for repeated base data acquisition and allows for optimized dynamic adjustments without compromising image quality, even when acquisition geometries change, by using updated base data to calculate control signals for improved sub-volume imaging.
Implementation Method 1
the subject under examination is placed in a strong homogeneous basic magnetic field, also called the B0 field, which is generated by the basic magnetic field system and has a field strength of 0.2 Tesla to 7 Tesla and higher, with the result that the nuclear spins of the subject are oriented along the basic magnetic field
Implementation Method 2
the subject under examination is exposed to radio frequency excitation signals (RF pulses) by suitable antenna devices in the RF transmit system, causing the nuclear spins of certain atoms, which have been excited to resonance by this RF field
Implementation Method 3
Magnetic gradient fields, rapidly switched by the gradient system, are superimposed on the basic magnetic field for spatial encoding of the measurement data
Implementation Method 4
The induced nuclear spin resonances, i.e. the RF signals (also known as the magnetic resonance signals) emitted during precession of the nuclear spins, are detected by the RF reception system
Data Source
AI summary
In a method for operating a medical imaging apparatus having multiple sub-systems controlled by a controller in a coordinated manner in order to perform a measurement sequence, a number of first setting parameters are chosen to be constant for a complete measurement sequence, and after a static adjustment, a dynamic adjustment of second setting parameters, which are defined by control signals for the measurement sequence and that can vary while a measurement sequence is being performed, takes place. Base data defining underlying conditions specific to the patient to be imaged are measured for the total imaging volume in a measurement sequence. Reference values for the setting parameters that affect the underlying conditions are saved in association with the base data during the measurement sequence. If the present values of the setting parameters adjusted during the static adjustment differ from the reference values, updated base data are calculated based on the difference between the present values and the reference values, and the current base data are used for local optimization in the dynamic adjustment.