MRI Vascular Metrics for Noninvasive Lesion Hemodynamics
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Solution Overview
Problem
Conventional methods for characterizing tumor vascular features are invasive, lack sensitivity, and are limited to surface vessels, failing to provide accurate quantification of pharmacokinetic parameters and interstitial flow, which are crucial for diagnosing and treating cancer.
Innovation Solution
A non-invasive method using high spatial and temporal resolution MRI data to segment tumor-associated vessels, determine vascular metrics, and apply computational fluid dynamics models to map pressure and flow fields, providing patient-specific characterization of tumor hemodynamics and interstitial transport.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional invasive methods are used to characterize tumor vascular features, then direct measurement of vascular parameters is achieved, but patient safety is compromised and measurement sensitivity is insufficient
Solution Approach 1:
The patent replaces invasive mechanical measurement systems with non-invasive magnetic resonance imaging (MRI)-based computational fluid dynamics (CFD) modeling. The CFD model uses MRI-derived boundary conditions to simulate blood flow and interstitial transport, eliminating the need for physical insertion of sensors or probes into the tumor vasculature while maintaining measurement capability through virtual physiological modeling
Solution Approach 2:
The patent introduces a computational fluid dynamics model as an intermediary between MRI data acquisition and vascular parameter characterization. This CFD model acts as a virtual mediator that translates non-invasive MRI measurements into quantitative vascular metrics (blood flow velocity, interstitial pressure, permeability) without requiring direct physical contact with the tumor tissue
2Measurement precision
If conventional methods are used to measure interstitial flow and pharmacokinetic parameters, then some vascular metrics are obtained, but the desired sensitivity and accuracy for tumor characterization are not achieved
Solution Approach 1:
The patent segments the tumor region into distinct vascular and interstitial compartments using image processing of MRI data. This segmentation enables separate characterization of blood flow dynamics within vessels and interstitial transport in the surrounding tissue, allowing independent optimization of measurement parameters for each compartment and improving overall measurement precision
Solution Approach 2:
The patent employs parameter changes by using multiple MRI sequences (DCE-MRI for permeability, DW-MRI for diffusion coefficients) with different acquisition parameters to constrain the CFD model. By varying imaging parameters and acquiring complementary data sets, the model achieves more reliable and sensitive characterization of pharmacokinetic parameters than single-modality approaches
3Measurement precision
If high spatial and temporal resolution MRI data are acquired to improve tumor characterization, then diagnostic accuracy is enhanced, but imaging time and data complexity increase
Solution Approach 1:
The patent applies partial action by selectively acquiring MRI data at specific time points and spatial locations that are most informative for constraining the CFD model. Rather than uniformly sampling the entire tumor volume at all time points, the methodology targets critical regions and phases (e.g., contrast agent first-pass through tumor vasculature) to achieve sufficient model constraint with reduced imaging time
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
Enhances diagnostic accuracy and therapeutic planning by rigorously characterizing tumor-associated hemodynamics and interstitial transport, improving differentiation between malignant and benign lesions and optimizing treatment strategies.
Implementation Method 1
acquiring a first magnetic resonance imaging (MRI) dataset corresponding to high spatial resolution scans of the ROI, and a second MRI dataset corresponding to high temporal resolution scans of the ROI
Data Source
AI summary
Disclosed are approaches to non-invasively characterize a tumor or other lesion in a region of interest (ROI) based on various analyses of magnetic resonance imaging (MRI) data. The MRI data may correspond to ultrafast dynamic contrast enhanced MRI (DCE-MRI) and high spatial resolution DCE-MRI scans, and diffusion-weighted MRI (DW-MRI) scans of the ROI. Vasculature metrics may be determined, and tumor-associated blood flow velocity and/or tumor interstitial pressure may be obtained using the vasculature metrics as inputs to a computational fluid dynamics model. A combination of morphological vascular metrics and functional vascular metrics may be used to characterize the tumor. Malignancy, aggressiveness, treatment response, and other features of tumors or other lesions, in the breast or other regions of a patient, may be characterized through disclosed analyses of MRI data.