Muscle Volume Profiling via Height-Mass Normalization
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Solution Overview
Problem
Current methods for assessing muscle adaptation in athletes are limited by their focus on individual muscles or small groups, failing to provide a comprehensive understanding of muscle hypertrophy patterns across the entire lower limb, which is crucial for understanding athletic performance.
Innovation Solution
A method and system that acquire image data from multiple muscles, calculate height-mass normalized muscle volumes, and identify deviations from reference values to detect muscle abnormalities or patterns, using rapid non-Cartesian MRI and image processing for comprehensive profiling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If imaging modalities are used to measure muscle volume, then measurement precision is improved, but time consumption increases
Solution Approach 1:
The patent segments the lower limb into multiple discrete muscle groups (quadriceps, hamstrings, calves, etc.) and measures each separately using MRI imaging. This segmentation allows comprehensive coverage of all muscles while maintaining efficient processing by focusing on specific anatomical regions rather than attempting to measure the entire limb as one unit.
Solution Approach 2:
The patent transforms raw MRI image data into standardized muscle volume parameters through automated image processing algorithms. By changing the parameter representation from complex image data to simplified volumetric measurements, the system achieves both high measurement precision and reduced computation time for clinical applications.
2Quantity of substance
If comprehensive muscle assessment across entire lower limb is conducted, then measurement completeness is improved, but device complexity increases
Solution Approach 1:
The patent employs a universal MRI imaging protocol that can assess all lower limb muscles through a single standardized scanning procedure. The same imaging device and processing algorithm serve multiple functions: capturing images of different muscle groups, calculating volumes for various muscle types, and generating comprehensive profiles that cover the entire lower limb, thereby reducing the need for multiple specialized devices.
Solution Approach 2:
The patent creates standardized reference profiles of normal muscle volumes that serve as templates for comparison. These reference copies allow clinicians to assess patient muscle volumes by comparing against established norms, simplifying the interpretation process and reducing the complexity of real-time analysis while maintaining comprehensive assessment capabilities.
3Adaptability or versatility
If muscle volume data is normalized by height and mass, then comparability across subjects is improved, but calculation complexity increases
Solution Approach 1:
The patent transforms raw muscle volume measurements into normalized parameters by dividing volume data by subject height and mass. This parameter transformation enables direct comparison between subjects of different sizes and demographics while the automated calculation process handles the mathematical complexity, making the normalization straightforward for clinical use.
Solution Approach 2:
The patent implements a feedback mechanism where normalized muscle volume data is continuously compared against reference ranges and population norms. This feedback loop allows the system to automatically identify deviations from normal patterns and provide interpretive guidance, reducing the need for manual analysis and simplifying the overall assessment process despite the added normalization calculations.
Data Source
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AI summary
Some aspects of the present disclosure relate to identifying and profiling muscle patterns. In one embodiment, a method includes acquiring image data associated with a selected muscle or group of muscles of one or more subjects and determining, based on the image data, muscle volume of the selected muscle or group of muscles. The method also includes calculating, based on the muscle volume and the height and mass of the one or more subjects, a height-mass normalized muscle volume for the selected muscle or group of muscles, and determining a deviation of the height-mass normalized muscle volume of the selected muscle or group of muscles from a mean value of muscle volume associated with a corresponding reference muscle or reference group of muscles. The method also includes identifying, based on the deviation, a muscle abnormality or absence of a muscle abnormality in the selected muscle or group of muscles.