Automated Tumor Interstitial Fluid Velocity Assessment via DCE MRI
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Solution Overview
Problem
Current methods for assessing Interstitial Fluid Pressure (IFP) in solid tumors are invasive, inaccurate, and impractical for routine clinical use, and existing non-invasive methods are either too time-consuming or dependent on specific MRI protocols, making it difficult to predict drug delivery efficiency and metastasis risk effectively.
Innovation Solution
An automated, non-invasive method using Dynamic Contrast-Enhanced (DCE) imaging protocols to calculate Interstitial Fluid Velocity (IFV) and pressure in mammalian tissues, which involves image registration, contour detection, displacement calculation, and fluid flow modeling, enabling efficient assessment of tumor drug delivery and metastasis risk without requiring extensive radiological intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive needle insertion is used to measure IFP, then measurement precision is improved, but ease of operation deteriorates and loss of time increases
Solution Approach 1:
The patent replaces the mechanical invasive needle insertion method with a non-invasive MRI-based imaging system. The mechanical needle puncture is substituted by magnetic resonance imaging technology that measures IFP through contrast agent distribution and signal intensity analysis, eliminating physical intrusion while maintaining measurement capability.
Solution Approach 2:
The patent introduces contrast agents as intermediaries to indirectly measure IFP. Instead of directly measuring pressure with a needle, the contrast agent distributes in the interstitial fluid according to IFP gradients, and its distribution pattern serves as a mediator to infer the pressure values through MRI signal analysis.
2Measurement precision
If slow infusion of contrast material is used to achieve steady state, then measurement precision is improved, but loss of time deteriorates
Solution Approach 1:
The patent performs preliminary actions by acquiring multiple MRI images at different time points during the contrast agent infusion process. Instead of waiting for steady state to be achieved, the system captures the dynamic distribution process and uses kinetic modeling to calculate IFP from the time-dependent signal intensity changes, eliminating the need to wait for equilibrium.
Solution Approach 2:
The patent transitions from static steady-state measurement to dynamic kinetic measurement. By analyzing the time-dependent distribution of the contrast agent during infusion, the system extracts IFP information from the dynamic process itself, allowing measurement during the actual infusion rather than requiring completion of the steady state.
3Measurement precision
If manual analysis is used to identify points along tumor rim images, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform the complete IFP assessment workflow. The software automatically identifies tumor boundaries, tracks contrast agent distribution, performs kinetic modeling, and calculates IFP values without requiring manual intervention or expert analysis, making the system self-sufficient and scalable.
Solution Approach 2:
The patent extracts the time-consuming manual analysis steps from the workflow and replaces them with automated computational algorithms. The complex tasks of identifying tumor rim points, tracking contrast distribution, and performing kinetic modeling are extracted and automated through image processing and mathematical modeling systems.
4Ease of operation
If non-invasive MRI methods are used to assess IFP, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent implements feedback mechanisms by using the MRI signal intensity data to iteratively refine the IFP calculation. The system continuously monitors contrast agent distribution and uses the signal feedback to adjust and optimize the kinetic modeling parameters, improving measurement accuracy through iterative refinement based on actual observed data.
Data Source
AI summary
A system and method for automated characterization of solid tumors using medical imaging. The system comprises an interface that is configured to acquire data from medical imaging devices, one or more processors, and an outputting device that reports the characterization of said solid tumor. The method of automated characterization, which is implemented by the system, acquires a sequence of images from the medical imager using a Dynamic Contrast Enhanced (DCE) imaging protocol, performs image registration, detects the contour of the solid tumor, and dividing the contours to segments. For each segment, the method calculating a displacement of the contrast material, fitting the displacement to a flow model and extracting an estimation of the interstitial fluid velocity. The estimated interstitial fluid velocity of the segments provide characterization of the solid tumor and includes an assessment of the tumor interstitial fluid pressure, the tumor drug delivery efficiency, and the tumor prognostic or metastasis risk.