Myocardial Fibre Architecture Estimation from Motion-Coupled MRI
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Solution Overview
Problem
Current methods for estimating myocardial fibre architecture, such as rule-based models and Diffusion Tensor Imaging (DTI), lack accuracy and fail to account for myocardial movement during the heart cycle, leading to inadequate heart models for medical research.
Innovation Solution
A method utilizing cine-MRI images to estimate myocardial fibre architecture by optimizing a joint function that combines myocardial motion and fibre architecture parameters, incorporating population-based fibre information and mechanical coupling constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based models are used to estimate myocardial fibre architecture, then the modelling process is simplified and can be applied generally, but the accuracy and personalization capability are reduced
Solution Approach 1:
The patent introduces an optimization procedure as an intermediary between the simplified rule-based model and the actual myocardial fibre architecture. This optimization procedure uses objective functions to iteratively adjust model parameters, transforming the general rule-based approach into a personalized accurate model by mediating between computational simplicity and measurement precision
Solution Approach 2:
The patent changes the parameters of the rule-based model through optimization procedures. By adjusting model parameters such as fibre orientation angles and structural characteristics to minimize objective functions, the system transforms a static general model into a dynamic personalized model that accurately reflects individual myocardial architecture while maintaining the ease of rule-based formulation
2Measurement precision
If ex-vivo cDTI is used to estimate myocardial fibre architecture, then measurement accuracy is improved, but applicability to patients is lost and population representativeness is reduced
Solution Approach 1:
The patent substitutes the mechanical/experimental cDTI measurement system with a computational optimization system. Instead of relying on physical tissue extraction and imaging, the system uses optimization procedures that compute fibre architecture from readily available clinical data, replacing the need for specialized ex-vivo measurements while maintaining accuracy
Solution Approach 2:
The patent creates a computational copy of the myocardial fibre architecture optimization process that can be applied in-vivo. By formulating the estimation as an optimization problem with appropriate objective functions, the system replicates the accuracy benefits of ex-vivo methods while being applicable to living patients and population studies
3Adaptability or versatility
If in-vivo cDTI is used to estimate myocardial fibre architecture, then patient-specific modelling is enabled, but measurement accuracy and signal-to-noise ratio are reduced
Solution Approach 1:
The patent introduces feedback mechanisms through optimization procedures that iteratively refine the fibre architecture model. The objective functions provide feedback on how well the model matches observed data, allowing the system to correct for noise and inaccuracies in in-vivo measurements, thereby maintaining personalization capability while improving measurement precision
Solution Approach 2:
The patent performs preliminary optimization and model formulation before actual measurement interpretation. By pre-defining objective functions and model structures that account for expected noise levels and anatomical constraints, the system prepares the computational framework to handle in-vivo data more accurately, improving precision before the actual measurement analysis occurs
4Device complexity
If current methods ignore myocardial movement during the heart cycle, then the modelling process is simplified, but the realism and accuracy of heart models are reduced
Solution Approach 1:
The patent transforms the static modelling approach into a dynamic one by incorporating myocardial movement through optimization procedures. The system models fibre architecture as a dynamic process that accounts for cardiac cycle variations, allowing the model to adapt to different phases of heart motion while maintaining computational tractability through systematic optimization
Data Source
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AI summary
A method of estimating muscle fibre architecture for a muscular structure is described. The method comprises receiving a plurality of images that represent the motion of the muscular structure during a motion sequence, receiving an initial model of muscle fibre architecture, receiving a model of mechanical coupling between muscular structure motion and muscle fibres, and generating a joint function using muscular structure motion as represented in the plurality of images, the initial model of muscle fibre architecture, and the model of mechanical coupling. The method further comprises applying an optimisation procedure to optimise the joint function and, from the optimisation procedure, determining a model of muscle fibre architecture consistent with the muscular structure motion indicated in the plurality of images.