3D U-NetR: Low Dose Computed Tomography Reconstruction via Deep Learning and 3 Dimensional Convolutions

• URL: http://arxiv.org/abs/2105.14130v1
• Date: Fri, 28 May 2021 22:37:50 GMT
• Title: 3D U-NetR: Low Dose Computed Tomography Reconstruction via Deep Learning and 3 Dimensional Convolutions
• Authors: Doga Gunduzalp, Batuhan Cengiz, Mehmet Ozan Unal, Isa Yildirim
• Abstract summary: 3D U-NetR captures medically critical visual details that cannot be visualized by 2D network. More importantly, 3D U-NetR captures medically critical visual details that cannot be visualized by 2D network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduced a novel deep learning based reconstruction technique using the correlations of all 3 dimensions with each other by taking into account the correlation between 2-dimensional low-dose CT images. Sparse or noisy sinograms are back projected to the image domain with FBP operation, then denoising process is applied with a U-Net like 3 dimensional network called 3D U-NetR. Proposed network is trained with synthetic and real chest CT images, and 2D U-Net is also trained with the same dataset to prove the importance of the 3rd dimension. Proposed network shows better quantitative performance on SSIM and PSNR. More importantly, 3D U-NetR captures medically critical visual details that cannot be visualized by 2D network.

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