MRI Scan Parameter Recommendations from Reference Datasets
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Solution Overview
Problem
Existing MRI scan parameter adjustments are often dependent on the experience of the operator, leading to potential degradation in image quality and increased variability in results, especially for less experienced operators.
Innovation Solution
A medical system and method that utilizes a recommendation module to provide adjustment recommendations for MRI scan parameters based on a plurality of reference datasets, considering both physiological target types and physical parameters of the subject, to optimize scan times and image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If standard scan parameters are used for MRI data acquisition, then the imaging process is simple and fast, but the image quality and diagnostic value may be insufficient
Solution Approach 1:
The system automatically analyzes the raw MRI data and generates optimized scan parameters without requiring manual intervention from operators. The computational system self-adjusts the scan parameters based on the acquired data characteristics, eliminating the need for expert operators to manually tune parameters while achieving high image quality
Solution Approach 2:
The patent replaces the manual mechanical adjustment process (operators physically adjusting scan parameters) with an automated computational system that uses algorithms to optimize parameters. This substitution of human expertise with computational intelligence resolves the contradiction by providing expert-level parameter optimization without requiring experienced operators
2Manufacturing precision
If scan parameters are adjusted by experienced operators, then image quality improves, but the process becomes time-consuming and less accessible to less experienced operators
Solution Approach 1:
The system performs automatic parameter optimization without requiring operator intervention, thereby eliminating the time loss associated with manual adjustment processes while maintaining high image quality through algorithmic optimization
Solution Approach 2:
The system pre-processes the acquired MRI data to identify optimal scan parameters before final image reconstruction. This preliminary analysis and optimization step automatically determines the best parameters based on the actual data characteristics, achieving high image quality without time-consuming manual adjustment
3Adaptability or versatility
If manual adjustment of scan parameters is performed, then customization for specific cases is possible, but the risk of decreasing result quality increases for less experienced operators
Solution Approach 1:
The system analyzes the actual MRI data acquired and uses this feedback to automatically adjust and optimize scan parameters. This closed-loop feedback mechanism ensures that parameter customization is based on actual data quality metrics rather than operator intuition, thereby maintaining high reliability and consistency of results while preserving adaptability to different cases
Solution Approach 2:
The patent replaces manual parameter adjustment with an automated computational system that uses algorithms to determine optimal parameters for each specific case. This substitution eliminates the variability and potential errors introduced by human operators while maintaining full customization capability through data-driven optimization
Data Source
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AI summary
Disclosed herein is a medical system (100) comprising a computational system (402) and memory (404) storing machine-executable instructions (410) and a recommendation module (412). The recommendation module (412) is configured, using a plurality of reference datasets (320) of a plurality of reference magnetic resonance imaging data acquisitions, to output an adjustment recommendation (414) for an adjustment of at least some initial scan parameters of a first subset (302) of a definition dataset (300). The definition dataset (300) defines a magnetic resonance imaging data acquisition to be executed. An execution of the machine-executable instructions (410) causes the computational system (402) to receive the definition dataset (300), to provide the adjustment recommendation (414) using the definition dataset (300) and the recommendation module (412), and to adjust the at least some initial scan parameters of the first subset (302) using the adjustment recommendation (414).