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Deep learning-based digital subtraction angiography image generation

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Digital subtraction angiography (DSA) is a powerful technique for diagnosing cardiovascular disease. In order to avoid image artifacts caused by patient movement during imaging, we take deep learning-based methods to generate DSA image from single live image without the mask image.

Methods

Conventional clinical DSA datasets are acquired with a standard injection protocol. More than 600 sequences obtained from more than 100 subjects were used for head and leg experiments. Here, the residual dense block (RDB) is adopted to generate DSA image from single live image directly, and RDBs can extract high-level features by dense connected layers. To obtain better vessel details, a supervised generative adversarial network strategy is also used in the training stage.

Results

The human head and leg experiments show that the deep learning methods can generate DSA image from single live image, and our method can do better than other models. Specifically, the DSA image generating with our method contains less artifact and is suitable for diagnosis. We use metrics including PSNR, SSIM and FSIM, which can reach 23.731, 0.877 and 0.8946 on the head dataset and 26.555, 0.870 and 0.9284 on the leg dataset.

Conclusions

The experiment results show the model can extract the vessels from the single live image, thus avoiding the image artifacts obtained by subtracting the live image and the mask image. And our method has a better performance than other methods we have tried on this task.

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Funding

This work was supported in part by the State’s Key Project of Research and Development Plan under Grant 2017YFA0104302, Grant 2017YFC0109202 and 2017YFC0107900, the National Natural Science Foundation under Grant 81530060 and 61871117.

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Authors and Affiliations

  1. Laboratory of Image Science and Technology, Southeast University, Nanjing, 210096, China

    Yufeng Gao, Yu Song, Xiangrui Yin & Yang Chen

  2. Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, 210096, China

    Yufeng Gao, Yu Song, Xiangrui Yin & Yang Chen

  3. Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing, 400044, China

    Weiwen Wu

  4. INSA de Rennes, 35000, Rennes, France

    Lu Zhang

  5. Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Hefei, 230022, China

    Wanyin Shi

Authors
  1. Yufeng Gao
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  2. Yu Song
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  3. Xiangrui Yin
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  4. Weiwen Wu
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  5. Lu Zhang
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  6. Yang Chen
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  7. Wanyin Shi
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Correspondence to Yang Chen or Wanyin Shi.

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The authors do not have any conflict of interest.

Ethical statements

All procedures performed in studies involving human participants were in accordance with the Ethical Standards of the Medical University of Warsaw and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Gao, Y., Song, Y., Yin, X. et al. Deep learning-based digital subtraction angiography image generation. Int J CARS 14, 1775–1784 (2019). https://doi.org/10.1007/s11548-019-02040-x

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  • DOI: https://doi.org/10.1007/s11548-019-02040-x

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