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

2021-05-28 22:37:50
Doga Gunduzalp, Batuhan Cengiz, Mehmet Ozan Unal, Isa Yildirim
arXiv_CV
                   
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.

Abstract (translated)

URL

https://arxiv.org/abs/2105.14130

PDF

https://arxiv.org/pdf/2105.14130.pdf

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