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RAD-UNet: a Residual, Attention-Based, Dense UNet for CT Sparse Reconstruction

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Abstract

To suppress the streak artifacts in images reconstructed from sparse-view projections in computed tomography (CT), a residual, attention-based, dense UNet (RAD-UNet) deep network is proposed to achieve accurate sparse reconstruction. The filtered back projection (FBP) algorithm is used to reconstruct the CT image with streak artifacts from sparse-view projections. Then, the image is processed by the RAD-UNet to suppress streak artifacts and obtain high-quality CT image. Those images with streak artifacts are used as the input of the RAD-UNet, and the output-label images are the corresponding high-quality images. Through training via the large-scale training data, the RAD-UNet can obtain the capability of suppressing streak artifacts. This network combines residual connection, attention mechanism, dense connection and perceptual loss. This network can improve the nonlinear fitting capability and the performance of suppressing streak artifacts. The experimental results show that the RAD-UNet can improve the reconstruction accuracy compared with three existing representative deep networks. It may not only suppress streak artifacts but also better preserve image details. The proposed networks may be readily applied to other image processing tasks including image denoising, image deblurring, and image super-resolution.

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Funding

This work was supported in part by the Natural Science Foundation of China under grant 62071281, by the Central Guidance on Local Science and Technology Development Fund Project under grant YDZJSX2021A003, and by the Research Project Supported by Shanxi Scholarship Council of China under grant 2020-008.

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Authors and Affiliations

  1. School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China

    Zhiwei Qiao & Congcong Du

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  1. Zhiwei Qiao
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Congcong Du and Zhiwei Qiao. The first draft of the manuscript was written by Congcong Du and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Zhiwei Qiao.

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Qiao, Z., Du, C. RAD-UNet: a Residual, Attention-Based, Dense UNet for CT Sparse Reconstruction. J Digit Imaging 35, 1748–1758 (2022). https://doi.org/10.1007/s10278-022-00685-w

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  • DOI: https://doi.org/10.1007/s10278-022-00685-w

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