MRI Data Link Encoding Based on K-Space Signal Importance
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Solution Overview
Problem
Current MRI systems face a tradeoff between data link efficiency and robustness due to fixed encoding levels for control and image data, which affects the quality of reconstructed images.
Innovation Solution
Adaptive encoding method that assigns higher robust encoding to MR signals with greater impact on image quality and lower robust encoding to those with lesser impact, based on signal characteristics such as SNR or k-space location, to optimize data link efficiency and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 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 types 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 criticality rather than applying uniform encoding.
Solution Approach 2:
The patent dynamically adjusts encoding levels based on data type and transmission conditions. The encoding level is not fixed but adapts according to the specific data being transmitted, allowing the system to optimize between robustness and efficiency in real-time based on the transmission requirements.
2Productivity
If 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 that are less critical to overall image quality. By identifying which data portions can tolerate higher error rates, the system achieves better efficiency without significantly compromising robustness where it matters most.
Solution Approach 2:
The patent applies encoding selectively rather than uniformly. Not all image data requires the same level of protection, so the system applies encoding only where necessary and at the appropriate level, avoiding excessive encoding that would reduce efficiency without providing additional benefit.
3Device complexity
If fixed encoding levels are used for all data, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The patent implements a dynamic encoding system that automatically adjusts encoding levels based on data type, transmission conditions, and quality requirements. This dynamic adaptation maintains relatively simple device architecture while achieving high adaptability through software-controlled parameter adjustment.
Solution Approach 2:
The patent changes encoding parameters based on the specific data being transmitted. By modifying encoding levels, data types, and error correction parameters according to the transmission requirements, the system achieves high adaptability without requiring complex hardware changes.
Data Source
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.