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    A bridged denoising convolutional neural network (BD-CNN) for photon-counting CT

    Author
    Qian, Guhan
    View/Open
    179613_Qian_rpi_0185N_11487.pdf (2.546Mb)
    Other Contributors
    Wang, Ge, 1957-; Yan, Pingkun; Intes, Xavier;
    Date Issued
    2019-05
    Subject
    Biomedical engineering
    Degree
    MS;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
    Metadata
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    URI
    https://hdl.handle.net/20.500.13015/2382
    Abstract
    Traditional monochromatic CT scanners have served as one of the most critical medical diagnostic instruments. Recently, the development of photon-counting CT brings spectral information to the otherwise black-and-white CT images, allowing the separation of materials. However, noise from a range of sources still degrades the overall image quality. Inspired by the successes of deep learning implementations in image processing, a bridged denoising convolutional neural network (BD-CNN) is proposed, aiming to reduce the noise presented in the image and enhance the overall image quality quantified by PSNR, RMSE, and SSIM. The BD-CNN is trained with a simulated labeled dataset of photon-counting CT. The results demonstrate that the denoising network improves the overall quality of the images and the noise presented in the original noisy images is decreased significantly. Compared to iterative denoising algorithms (Median filtering, BM3D), the proposed method not only achieved better results measured by the metrics, but it also demonstrates better consistency in image denoising. In conclusion, the proposed BD-CNN is a promising deep learning architecture for image denoising and can be further improved with minor adjustments. Further studies are needed for a more suitable loss function for deep learning in medical imaging and different image quality metrics optimized for comparing diagnostic value of medical images.;
    Description
    May 2019; School of Engineering
    Department
    Dept. of Biomedical Engineering;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;
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