Infusion Parameter Optimization via MRI Distribution Analysis
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Solution Overview
Problem
Current methods for determining the steady state volume of distribution and volume of efficacy of infused therapeutic materials in subjects, such as for brain or spinal cord therapies, face challenges in accurately predicting and achieving the desired concentration and distribution due to factors like clearance and varying convection and diffusion rates.
Innovation Solution
A method and apparatus that utilize T2-weighted Magnetic Resonance Imaging (MRI) data and computational algorithms to determine and achieve the volume of efficacy by analyzing the volume of distribution and concentration gradient of a combination material, allowing for precise infusion parameters to be set for achieving the desired therapeutic effect.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If material is infused into the subject to achieve therapeutic effect, then the volume of efficacy is achieved, but clearance removes material from the region reducing effectiveness
Solution Approach 1:
The system uses imaging data to monitor material distribution in real-time and feeds this information back to adjust infusion parameters. The processor determines volume of distribution and concentration gradient from imaging data, then uses this feedback to optimize infusion rate and duration, compensating for clearance effects dynamically throughout the infusion process.
Solution Approach 2:
The system changes infusion parameters (rate, duration, concentration) based on determined volume of distribution and concentration gradient. By adjusting these parameters according to actual material distribution measured through imaging, the system optimizes therapeutic effect while accounting for clearance variations.
2Measurement precision
If infusion parameters are set to achieve desired concentration, then volume of efficacy is achieved, but varying convection and diffusion rates cause inaccurate prediction
Solution Approach 1:
The system replaces theoretical mathematical models of convection and diffusion with actual imaging-based measurement. Instead of relying on mechanical/mathematical predictions that vary with physiological conditions, the system directly measures material distribution using T1-weighted and T2-weighted imaging, eliminating the reliability issues associated with model-based predictions.
Solution Approach 2:
The system introduces imaging data as an intermediary between infusion parameters and material distribution prediction. The imaging data serves as a mediator that directly reveals the actual concentration and volume of distribution, bypassing the need for theoretical convection-diffusion models and providing accurate measurement despite physiological variations.
3Measurement precision
If combination material is infused to determine volume of distribution, then VOE can be predicted, but the method requires multiple imaging modalities and complex analysis
Solution Approach 1:
The system combines T1-weighted and T2-weighted imaging data analysis into a unified computational framework. The processor integrates both imaging modalities to simultaneously determine volume of distribution and concentration gradient, merging what would otherwise be separate analysis processes into a single coordinated system that reduces overall complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate prediction and achievement of the volume of efficacy, ensuring effective delivery of therapeutic materials by determining the optimal infusion parameters, including flow rates and catheter placement, thereby enhancing the therapeutic outcome while minimizing unwanted clearance.
Implementation Method 1
The VOD and concentration gradient of the liquid can be determined with T2-weighted Magnetic Resonance Image data
Data Source
AI summary
A combination material can be infused into a subject and a determination can be made of a VOD and/or a convection gradient of a liquid material portion in the VOD. The combination material can be infused in the subject using selected parameters. A correlation of data relating to the liquid material can be made to a selected material to determine parameters for infusion the selected material.