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A Novel Demography-Based Approach to Define Patient-Specific Outflow Boundary Conditions in CT-Based FFR Computations
Ernest W C Lo1, Francesca Pugliese2,3,4, Leon Menezes5
1Department of Medical Physics and Biomedical Engineering, UCL EPSRC CDT for Medical Imaging, University College London, Gower Street, London, WC1E 6BT, UK.
Insights
A new method uses patient demographics to estimate microvascular resistance for computed tomography-based fractional flow reserve (CT-FFR) calculations. This approach improves accuracy over conventional methods, offering a practical enhancement for CT-FFR analysis.
Area of Science:
- Cardiovascular Imaging
- Computational Fluid Dynamics
- Medical Diagnostics
Background:
- Computed tomography-based fractional flow reserve (CT-FFR) is a valuable tool for assessing coronary artery disease.
- Current CT-FFR methods rely on assumptions for outflow boundary conditions (BCs) representing coronary microvasculature, particularly during hyperemia.
- Accurate estimation of microvascular response to hyperemia is crucial for reliable CT-FFR computations.
Purpose of the Study:
- To develop and validate a novel method for estimating patient-specific microvascular flow response (MFR) to hyperemia for CT-FFR calculations.
- To utilize routinely available patient demographic data for predicting MFR, thereby avoiding the need for additional complex imaging.
- To compare the accuracy of CT-FFR derived from the proposed demography-based MFR model against invasive FFR and conventional CT-FFR approaches.
Main Methods:
- A statistical model was developed using PET-perfusion data from 101 coronary artery disease patients to predict MFR based on demographic parameters (sex, diabetes, smoking status).
- CT-FFR computations were performed using patient-specific anatomical models and outflow BCs derived from the demography-based MFR model.
- The CT-FFR results were validated against invasive FFR measurements in an independent cohort of 10 patients.
Main Results:
- A multivariate regression model successfully predicted patient-specific MFR using sex, diabetes, and smoking status.
- CT-FFR values computed with the demography-based MFR model showed good agreement with invasive FFR (0.76 ± 0.09 vs. 0.75 ± 0.10, P=0.217).
- The proposed model demonstrated significantly improved accuracy (91%) compared to the conventional approach (82%) and was comparable to CT-FFR using perfusion data (100%).
Conclusions:
- The demography-based MFR model significantly enhances CT-FFR computation accuracy compared to conventional methods assuming average microvascular function.
- While slightly less accurate than CT-FFR with perfusion imaging, the demography-based model offers a practical advantage by not requiring additional data acquisition.
- This novel approach holds substantial potential for practical implementation in routine CT-FFR analysis, improving diagnostic capabilities.
Background And Objective:
Computed tomography-based fractional flow reserve computation (CT-FFR) is widely used in clinical practice, based on its efficacy demonstrated in many studies. However, major assumptions remain with the outflow boundary conditions (BCs) representing coronary microvasculature, especially in hyperaemia. We here propose a novel method to estimate patient-specific microvascular response to hyperaemia for CT-FFR calculations, based on patients' routinely available demographic data.
Methods:
A statistical model to predict microvascular flow response (MFR) from routinely collected patient demographic parameters was derived using PET-based perfusion data of 101 patients with coronary artery disease. CT-FFR computations were then conducted with patient-specific anatomical models and outflow BCs derived from various MFR models including the proposed approach. The FFR values were calculated for an independent test cohort of 10 patients who had undergone CT coronary angiography, CT perfusion imaging and invasive FFR measurement. Computed FFR values were compared against invasive FFR and other CT-FFR algorithms.
Results:
A multivariate regression model predicting patient-specific MFR was derived as a function of sex, diabetes and smoking status of the patient. FFR values computed using our model agreed well with the invasive FFR (0.76 ± 0.09 vs. 0.75 ± 0.10, P = 0.217). The FFRs predicted with our model were also comparable to those calculated using outflow BC tuned with patient-specific perfusion data (FFR: 0.74 ± 0.10, P = 0.233 vs. invasive FFR) and showed marked improvement over the conventional approach (FFR: 0.68 ± 0.11, P = 0.004 vs. invasive FFR). Diagnostic accuracy vs. invasive FFR were 100, 91 and 82% for CT-FFR with CTP-based MFR, demography-based MFR, and conventional approach, respectively.
Discussion:
The proposed demography-based MFR model significantly improves FFR computation accuracy compared with a typical conventional model that assumes constant, healthy and population average MFR. Although its diagnostic accuracy is slightly lower than that of CT-FFR calibrated with patient-specific perfusion imaging data (91 vs. 100%), the demography-based model offers a substantial practical advantage by not requiring additional non-standard data acquisition, such as perfusion imaging. Consequently, it shows strong potential as a practical enhancement to conventional CT-FFR algorithms.
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