Inflammation Index for Adipose-Adjusted FFR Assessment
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Solution Overview
Problem
Current cardiovascular disease assessments, particularly those using Fractional Flow Reserve (FFR) calculated from CT images, face inaccuracies and variability in disease progression and treatment response due to the lack of consideration for epicardial adipose tissue, leading to inconsistent diagnostic metrics and treatment outcomes.
Innovation Solution
The development of systems and methods that identify and compute an inflammation index from medical images and geometric vascular models to assess cardiovascular disease and treatment effectiveness by incorporating adipose tissue information, improving the accuracy of FFR calculations and predicting disease progression and treatment response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If FFR is calculated from CT images using conventional methods, then the assessment can be performed non-invasively, but the accuracy of the diagnostic metric is reduced due to lack of adipose tissue consideration
Solution Approach 1:
The patent combines CT imaging data with adipose tissue volume measurements into a unified assessment framework. The system integrates epicardial adipose tissue (EAT) volume quantification with conventional FFR calculation, merging these two previously separate assessment components into a comprehensive cardiovascular disease evaluation that improves diagnostic accuracy while maintaining non-invasive methodology.
Solution Approach 2:
The patent introduces adipose tissue volume as an intermediary factor that mediates between the CT image-based FFR calculation and the actual physiological state. By incorporating EAT volume as a modifying variable in the FFR calculation formula, the system adjusts the diagnostic metric to account for the impact of adipose tissue on blood flow, thereby improving accuracy without requiring invasive measurements.
2Reliability
If conventional FFR calculation methods are used, then the assessment process remains simple, but treatment effectiveness cannot be accurately predicted due to disease progression variability
Solution Approach 1:
The patent introduces a dynamic assessment framework that accounts for disease progression variability by incorporating adipose tissue volume changes over time. The system enables longitudinal monitoring where EAT volume is measured at multiple time points, allowing the assessment to adapt to changing physiological conditions and predict treatment response more reliably as the disease evolves.
Solution Approach 2:
The patent modifies the FFR calculation by introducing adipose tissue volume as an additional parameter. The revised calculation formula incorporates EAT volume as a weighting factor that adjusts the FFR value based on the patient's specific adipose tissue characteristics. This parameter change enables more accurate prediction of treatment effectiveness by accounting for individual variations in disease progression.
3Measurement precision
If adipose tissue information is incorporated into the assessment, then disease assessment accuracy is improved, but the complexity of data processing increases
Solution Approach 1:
The patent segments the CT image analysis into distinct components: (1) identification of the epicardial adipose tissue region, (2) quantification of EAT volume, and (3) integration with FFR calculation. By dividing the complex task of adipose tissue detection into separate, manageable steps with dedicated algorithms, the system reduces the overall difficulty of measurement while maintaining high assessment accuracy.
Data Source
AI summary
Systems and methods are disclosed for assessing cardiovascular disease and treatment effectiveness based on adipose tissue. One method includes identifying a vascular bed of interest in a patient's vasculature; receiving a medical image of the patient's identified vascular bed of interest; identifying adipose tissue in the received medical image; receiving a geometric vascular model comprising a representation of the patient's identified vascular bed of interest; and computing an inflammation index associated with the geometric vascular model, using the identified adipose tissue.