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Related Concept Videos

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Tissue perfusion modelling in optical coherence tomography.

Petra Stohanzlova1, Radim Kolar2

  • 1Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 61600, Brno, Czech Republic. stohanzlova@phd.feec.vutbr.cz.

Biomedical Engineering Online
|February 10, 2017
PubMed
Summary
This summary is machine-generated.

This study demonstrates advanced perfusion analysis using Optical Coherence Tomography (OCT) and a tissue phantom. The lagged mathematical model proved most effective for analyzing blood flow dynamics with contrast agents.

Keywords:
DeconvolutionImpulse responseModelOptical coherence tomographyPerfusion analysisPhantom

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Area of Science:

  • Biomedical Optics
  • Medical Imaging
  • Translational Research

Background:

  • Optical coherence tomography (OCT) is a key non-invasive imaging technique for preclinical and clinical applications, enabling "optical biopsy".
  • Functional OCT imaging, particularly perfusion imaging, is crucial for studying tissue function but advanced methods remain underexplored.
  • Perfusion analysis relies on contrast agents (e.g., nanoparticles) to alter image contrast, allowing assessment of tissue hemodynamics via concentration-intensity curves.

Purpose of the Study:

  • To develop and evaluate advanced perfusion imaging techniques using Optical Coherence Tomography (OCT).
  • To investigate the application of mathematical models and deconvolution methods for analyzing OCT-derived perfusion data.
  • To compare the efficacy of different mathematical models (exponential, gamma, lagged, LDRW) in characterizing tissue perfusion.

Main Methods:

  • A tissue-mimicking phantom was designed to measure OCT dilution curves at various flow rates (200–2000 μL/min).
  • Gold nanorods were used as an OCT contrast agent, administered as a bolus (50 μL at 5000 μL/min) during continuous OCT imaging.
  • Dilution curves were extracted and analyzed using a deconvolution method with four mathematical models to determine impulse response characteristics.

Main Results:

  • All tested mathematical models exhibited a linearly dependent parameter on flow rate, with R² values ranging from 0.4914 to 0.9996.
  • Different models provided varying insights into the examined phantom or tissue characteristics.
  • The 'lagged' model demonstrated superior performance based on minimization criteria and R² values, indicating its suitability for perfusion analysis.

Conclusions:

  • OCT, combined with mathematical modeling and deconvolution, enables advanced perfusion analysis using a tissue-mimicking phantom.
  • The three-parameter 'lagged' model is identified as the most appropriate for this application.
  • Further validation with real biological tissues is recommended to fully establish the clinical utility of this technique.