1. Articles from Kensuke Oikawa

    1-3 of 3

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

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

      We present deep convolutional neural network (DCNN)-based estimators of the tissue scatterer density (SD), lateral and axial resolutions, signal-to-noise ratio (SNR), and effective number of scatterers (ENS, the number of scatterers within a resolution volume). The estimators analyze the speckle pattern of an optical coherence tomography (OCT) image in estimating these parameters. The DCNN is trained by a large number (1,280,000) of image patches that are fully numerically generated in OCT imaging simulation. Numerical and experimental validations were performed. The numerical validation shows good estimation accuracy as the root mean square errors were 0.23%, 3.65 ...

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      Mentions: University of Tsukuba

    2. Computational multi-directional optical coherence tomography for visualizing the microstructural directionality of the tissue

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      Computational multi-directional optical coherence tomography for visualizing the microstructural directionality of the tissue

      We demonstrate computational multi-directional optical coherence tomography (OCT) to assess the directional property of tissue microstructure. This method is the combination of phase-sensitive volumetric OCT imaging and post-signal processing. The latter comprises of two steps. The first step is an intensity-directional analysis, which determines the dominant en face fiber orientations. The second step is the phase-directional imaging, which reveals the sub-resolution depth-orientation of the microstructure. The feasibility of the method was tested by assessing muscle and tendon samples. Stripe patterns with several sizes were visualized in the phase-directional images. In order to interpret these images, the muscle and tendon structures ...

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      Mentions: University of Tsukuba

    3. Bulk-phase-error correction for phase-sensitive signal processing of optical coherence tomography

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      Bulk-phase-error correction for phase-sensitive signal processing of optical coherence tomography

      We present a numerical phase stabilization method for phase-sensitive signal processing of optical coherence tomography (OCT). This method removes the bulk phase error caused by the axial bulk motion of the sample and the environmental perturbation during volumetric acquisition. In this method, the partial derivatives of the phase error are computed along both fast and slow scanning directions, so that the vectorial gradient field of the phase error is given. Then, the phase error is estimated from the vectorial gradient field by a newly developed line integration method; a smart integration path method. The performance of this method was evaluated ...

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      Mentions: University of Tsukuba
    1-3 of 3
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