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Deep convolutional neural network-based scatterer density and resolution estimators in optical coherence tomography.

Thitiya Seesan1,2, Ibrahim Abd El-Sadek1,3, Pradipta Mukherjee1

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Deep convolutional neural networks estimate key optical coherence tomography (OCT) parameters like scatterer density (SD) and resolution from speckle patterns. This method accurately quantifies tissue properties and shows promise for biological imaging applications.

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Area of Science:

  • Biomedical Optics
  • Medical Imaging
  • Computational Imaging

Background:

  • Optical coherence tomography (OCT) is a powerful imaging modality, but quantitative analysis of its images is challenging.
  • Key tissue parameters like scatterer density (SD) and resolution are crucial for accurate OCT interpretation.
  • Traditional methods for estimating these parameters can be complex and time-consuming.

Purpose of the Study:

  • To develop and validate deep convolutional neural network (DCNN)-based estimators for critical OCT image parameters.
  • To quantify tissue scatterer density (SD), lateral and axial resolutions, signal-to-noise ratio (SNR), and effective number of scatterers (ENS).
  • To assess the performance of DCNN estimators using both numerical simulations and experimental phantoms.

Main Methods:

  • A DCNN architecture was trained on a large dataset (1,280,000 patches) of numerically generated OCT images.
  • The DCNN analyzes speckle patterns within OCT images to estimate scatterer density, resolutions, SNR, and ENS.
  • Numerical simulations and experimental validations with Intralipid phantoms and a tumor cell spheroid were conducted.

Main Results:

  • High estimation accuracy was achieved in numerical validation, with root mean square errors below 6.15% for all parameters.
  • Experimental validation showed estimated SDs proportional to Intralipid concentrations and low average errors for resolutions (1.36% lateral, 0.68% axial).
  • The scatterer density estimator detected a reduction in SD during cell necrosis in an *in vitro* tumor spheroid.

Conclusions:

  • DCNN-based estimators provide accurate and reliable quantification of key OCT image parameters.
  • This approach offers a robust tool for analyzing OCT images in various biomedical applications.
  • The method demonstrates potential for non-invasive tissue characterization and monitoring of biological processes.