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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Related Experiment Video

Updated: Jun 9, 2026

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
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A Novel Demography-Based Approach to Define Patient-Specific Outflow Boundary Conditions in CT-Based FFR

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.

Annals of Biomedical Engineering
|March 3, 2026
PubMed
Summary

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.

Keywords:
Computational fluid dynamicsFractional flow reserveOutflow boundary conditionPerfusion imaging

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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.