MRI Data Link Encoding by Signal Importance
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Solution Overview
Problem
Existing MRI systems face a tradeoff between data link efficiency and robustness due to fixed encoding levels for control and image data, leading to suboptimal performance in high-resolution imaging.
Innovation Solution
Dynamic encoding based on signal characteristics, such as SNR or k-space location, is applied to MR signals, where critical signals are encoded with a higher level of robustness and less critical signals with a lower level, improving both efficiency and robustness of data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a higher level of encoding is applied to control data, then data link robustness is improved, but data link efficiency deteriorates
Solution Approach 1:
The patent applies different encoding levels to different portions of data based on their specific requirements. Control data receives higher-level encoding for robustness, while image data receives lower-level encoding for efficiency. This localized differentiation resolves the contradiction by tailoring encoding strength to data importance rather than applying a uniform approach.
Solution Approach 2:
The patent segments data into distinct categories (control data and image data) and applies separate encoding strategies to each segment. This segmentation allows the system to optimize encoding independently for each data type, achieving both robustness for control data and efficiency for image data simultaneously.
2Productivity
If a lower level of encoding is applied to image data, then data link efficiency is improved, but data link robustness deteriorates
Solution Approach 1:
The patent applies lower-level encoding specifically to image data portions where efficiency is prioritized, while maintaining higher-level encoding for control data. This localized quality adjustment resolves the contradiction by matching encoding strength to the specific requirements of each data type.
Solution Approach 2:
The patent segments data into control data and image data, applying differentiated encoding levels. Image data receives lower-level encoding to maximize efficiency, while control data receives higher-level encoding to maintain robustness, eliminating the need to choose between the two objectives.
3Device complexity
If fixed encoding levels are used for all data, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The patent transitions from static fixed encoding levels to dynamic adaptive encoding levels. The encoding level is dynamically selected based on data type and transmission requirements, allowing the system to adapt to different conditions while maintaining manageable complexity through automated decision-making.
Solution Approach 2:
The patent changes the encoding parameter (encoding level) based on data characteristics and transmission requirements. By adjusting this parameter dynamically, the system achieves high adaptability without requiring complex manual configuration, as the changes are driven by automated assessment of data needs.
Data Source
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AI summary
In MRI system, acquired MR signals that will have a greater impact on the quality of the final reconstructed MRI image if a data link error occurs are encoded with a higher, or more robust, level of encoding prior to being transmitted over a data communications link. Conversely, acquired MR signals that will have a lesser impact on the quality of the final reconstructed MRI image if a data link error occurs are encoded with a lower, or less robust, level of encoding prior to being transmitted over the data communications link. The overall result is improved data link robustness and efficiency for data being sent over the data link.