Medical Imaging Subsystem Dynamic Ambient Compensation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional medical imaging examination apparatuses face challenges in dynamically adapting to changing ambient conditions during scans, leading to suboptimal data quality due to static compensation settings, which are complex to manage and limited in flexibility, especially for multi-slice imaging and localized excitation techniques.
Innovation Solution
A method that dynamically optimizes compensation settings in real-time by determining current ambient conditions and assigning effective volumes to each sub-sequence, allowing for flexible adjustment of control signals to optimize each sub-region, thereby improving data quality and reducing hardware costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static compensation settings are used throughout the entire scan, then the control system is simple to manage, but data quality deteriorates due to inability to adapt to changing ambient conditions
Solution Approach 1:
The patent implements dynamic compensation settings that automatically adjust during the scan based on detected ambient conditions. The control system transitions from static to dynamic operation, where compensation parameters are continuously updated to match changing environmental conditions, thereby maintaining high data quality without sacrificing operational simplicity
Solution Approach 2:
The system incorporates real-time monitoring of ambient conditions with automatic feedback loops. The control computer detects changes in ambient conditions during the scan and automatically adjusts compensation settings in response, creating a closed-loop system that maintains optimal data quality while managing complexity through automation
2Measurement precision
If dynamic optimization of compensation settings is implemented, then data quality improves, but device complexity increases
Solution Approach 1:
The control system performs self-optimization by automatically detecting ambient conditions and adjusting compensation settings without external intervention. The system serves itself by implementing the dynamic optimization algorithm internally, which improves data quality while managing complexity through autonomous operation rather than requiring complex external control mechanisms
Solution Approach 2:
The patent dynamically changes compensation parameters based on detected ambient conditions. The control computer adjusts magnetic field compensation, gradient field compensation, and other parameters in real-time, allowing the system to adapt to changing conditions and maintain high data quality without requiring permanently complex hardware configurations
3Measurement precision
If compensation settings are optimized for each sub-volume separately, then image quality improves, but computation time increases
Solution Approach 1:
The patent divides the scan volume into multiple sub-volumes or slices, each with its own optimized compensation settings. The control computer calculates and applies tailored compensation parameters for each sub-volume, improving image quality through localized optimization while managing computation time through efficient segmented processing rather than requiring optimization of the entire volume simultaneously
4Measurement precision
If high-performance hardware is used to maintain optimal conditions, then data quality improves, but hardware costs increase
Solution Approach 1:
The patent uses dynamic parameter adjustment to compensate for hardware limitations. By changing compensation parameters in real-time based on detected ambient conditions, the system can achieve high data quality with standard hardware configurations, eliminating the need for expensive high-performance hardware while maintaining optimal performance through software-based adaptation
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 flexible and optimal compensation for changing ambient conditions, enhancing image quality and reducing computation time, while allowing for more economical hardware design by compensating for suboptimal conditions dynamically.
Implementation Method 1
the examination object is positioned in the scanner in a strong homogeneous basic magnetic field, also known as the B0 field, generated by the basic field magnet system with a field strength of 0.2 Tesla to 7 Tesla or more, so that the nuclear spins in the object align along the basic magnetic field direction
Implementation Method 2
radio frequency excitation signals (RF pulses) are radiated into the examination object with suitable antennas of the radio frequency transmission system, so that the nuclear spin of particular atoms stimulated to resonance by this radio frequency field
Implementation Method 3
For spatial encoding of the scan data, rapidly switched magnetic gradient fields are overlaid on the basic magnetic field by the gradient system
Implementation Method 4
The shim system is intended to homogenize the magnetic fields
Data Source
AI summary
In a method for operating a medical imaging examination apparatus having multiple subsystems, current ambient conditions in a scan volume of the apparatus are determined and stored in a global ambient condition parameter set. A control computer starts a scan sequence according to a selected scan protocol, and sequence control data that define different functional sub-sequences for the respective subsystems are provided to the control computer. Different effective volumes are assigned to each functional sub-sequence, and respective current sub-regions in the effective volume associated with the respective sub-sequence are determined, in which a volume optimization is to take place. Control signals for the scan sequence are calculated using the sequence control data, the global ambient condition parameter set, and the determined current sub-regions of the affected volumes, in order to optimize the functional sub-sequences at least with regard to the current sub-region of the assigned effective volume.