We present deep-learning based multi-contrast optical coherence tomography (OCT) imaging methods for the analysis of retinal tissue properties. Two modalities, synthesizing degree-of-polarization-uniformity (syn-DOPU) and scatterer density estimator (SDE), were introduced. Syn-DOPU generates DOPU images from non-polarization sensitive OCT images, and hence eliminates the need for special hardware. SDE provides robust scatterer density estimation irrespective of measurement and ocular medium conditions. The methods were applied to age-related macular degeneration cases, and revealed the detailed abnormality of the retinal pigment epithelium. Additionally, layer and sector analyses of normal cases demonstrated positional and age-related variations of DOPU and scatterer density.
This presentation aims at classifying, mathematically modeling, and numerically simulating the intratissue activities for better understanding the dynamic optical coherence tomography (D-OCT) imaging. The intracellular motilities are classified into six types, and each type of motility is modeled by mono-direction translation, random ballistic, diffusion motions and their combination. The intracellular scatterer dynamic and D-OCT signals including logarithmic intensity variance (LIV) and complex variance were numerically simulated. These D-OCT signals increase within the velocity range of 4.5 to 270 nm/s and become almost constant for larger velocities. In addition, it was found that the shorter wavelength gives higher LIV and complex variances.
Dynamic optical coherence tomography (DOCT) is developed to evaluate the functional activities of wide spectrum of tissues. However, the relation between the DOCT signals and the intracellular motion is not fully identified yet. This unidentified relationship inhibits further dissemination of DOCT signals. In this study, we proposed a theoretical and numerical framework to understand DOCT. It includes the classification of intracellular motility, their mathematical modeling, and numerical simulation. We classified intracellular motilities into six types: active transport, passive transport, jiggling, floating of dissociated cells, migration, and flow. Then, the motilities were modeled by three physical models: flow, random ballistic and diffusion. The sample motion and it resulting time-sequential OCT images were numerically simulated. Two DOCT contrasts were computed from the OCT time-sequence: logarithmic intensity variance of OCT (LIV) and temporal variance of complex OCT signals (complex variance). We considered the random ballistic motions measured by two different probing wavelengths of 840nm and 1310nm. Tessellated pattern of low and high LIV was found in LIV images. The LIV and complex variance increase within the velocity range of 4.5 to 270nm/s, while it becomes almost constant for larger velocities. Additionally, we found that both LIV and complex variance are higher when shorter wavelength is considered. Using the proposed theoretical model, we can better understand the specific intracellular tissue activities that contribute to the high DOCT signal.
A new deep-learning-based scatterer density estimator (SDE) is demonstrated. The SDE is trained by pairs of numerically simulated OCT images and its background parameters including the scatterer density, resolutions, and signal-to-noise ratio. For this simulation, we introduced a new noise model that accurately accounts for the spatial properties of three noise types: shot, relative-intensity, and detector noise. This SDE was experimentally validated by phantom and in-vitro tumor spheroid measurements. Significantly improved accuracy was found in comparison to our old SDE being trained with a naïve noise model that does not account for the spatial noise property.
We will present a deep convolutional neural network (DCNN) based estimators for optical coherence tomography (OCT). The DCNNs analyze local OCT speckle patterns and estimate the sample’s scatterer density and OCT resolutions. This estimator is intensity invariant, i.e., it does not use the net signal strength of OCT even to estimate the scatterer density. The DCNN is trained by a huge training dataset that was generated by a simple simulator of OCT imaging. This method is validated either by scattering phantom and in vitro tumor spheroid, and good accuracies of the estimation were shown.
A multi-functional optical coherence microscopy capable of computational refocusing, tissue dynamics and birefringence imaging, and scatterer density estimation is demonstrated. It is applied to cell spheroid, ex vivo animal tissues.
Deep convolutional neural network (CNN) based estimators for optical coherence tomography (OCT) are presented. The CNN analyze local OCT speckle patterns and estimate the sample’s scatterer density and OCT resolutions. This estimator is intensity invariant, i.e., it does not use the net signal strength of OCT even to estimate the scatterer density. The CNN is trained by a huge training dataset that was generated by a simple simulator of OCT imaging. And hence, the CNN is trained without experimental datasets. The performance of CNN was evaluated by numerically generated OCT images, and good accuracies of the estimation were shown.
A convolutional neural networks (CNN) based scatterer density estimator for optical coherence tomography (OCT) is presented. In order to train the OCT, small patches of OCT speckle image were numerically generated. In this numerical image generation, the imaging parameters including the resolutions, probe power, signal-to-noise ratio, and scatterer density were randomly defined. So, the CNN was trained to estimate the imaging parameters from the generated OCT image patch. The results showed that our CNN estimator can estimate the parameters from the OCT speckle images.
In this research, the mapping of the path length difference that involves the specimen is proposed for improving qualitative phase imaging (QPI) techniques. Phase-quality images are developed using the principle of phase- shift in digital holography and then this system has been applied to investigate biological cells and tissues. In our setup a microscopic digital holographic interferometer with polarizers and a quarter wave plate has been designed for detecting cells specimen. The resulting image contains information of the thickness in any area and the refractive index of the specimen. Moreover, a quadrature – phase shifting holography (QPSH) technique is purposed. Experimental results are shown improving QPI. Merits and limitations of this method are also described.
Multilayered hyperbolic metamaterials (MHM) is proposed to create phase matching of fundamental-frequency (FF) and third-harmonic (TH) field components with a unique dispersion provided by the engineered MHM structure for third-harmonic generation in ultra-short pulse regime. In this work, we analytically study the ensuing possibilities and demonstrate that a birefringent phase-matching can be alternatively achieved with a wide range of involved material parameters and optimal engineering of MHM structure. When the phase-matched conditions is satisfied by birefringent phase-matching method, the growth rate of the TH intensity generating as a function of the nonlinear-optical interaction length to be obviously increased. This method opens new ways of improving the conversion efficiency of frequency tripling regardless of the coherence length in the bulk of a nonlinear material.
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