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Self-denoising method for OCT images with single spectrogram-based deep learning.

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    A novel deep learning method uses spectrograms to denoise optical coherence tomography (OCT) images, significantly improving image quality. This self-denoising approach enhances signal-to-noise ratio and contrast with minimal computation.

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    Area of Science:

    • Biomedical Optics
    • Medical Imaging
    • Artificial Intelligence in Medicine

    Background:

    • Noise in optical coherence tomography (OCT) images limits diagnostic accuracy and further image quality enhancement.
    • Existing denoising methods may struggle with diverse noise types or require significant computational resources.

    Purpose of the Study:

    • To introduce a novel, computationally efficient, self-denoising method for OCT images using deep learning.
    • To demonstrate the effectiveness of a spectrogram-based deep learning model for customized noise reduction in OCT.

    Main Methods:

    • A single spectrogram-based deep learning model was developed, comprising fully connected, convolution, and deconvolution layers.
    • The model takes raw interference spectrograms as input and learns to predict noise, which is then subtracted from the Fourier-transformed image.
    • The method was tested on OCT images of a TiO2 phantom, an orange, and a zebrafish.

    Main Results:

    • The deep learning method effectively reduced speckle patterns and horizontal/vertical stripes in OCT images.
    • Signal-to-noise ratio (SNR) improved by 35.0 dB, and image contrast doubled compared to the label image.
    • The mean peak SNR was 26.2 dB higher than that achieved with the average denoising method.

    Conclusions:

    • The proposed spectrogram-based deep learning method offers an effective and efficient solution for denoising OCT images.
    • This approach significantly enhances image quality, paving the way for improved OCT applications in various fields.
    • The customized, low-computation denoising capability makes it adaptable to different noise characteristics.