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Estimating CT from MR Abdominal Images Using Novel Generative Adversarial Networks

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Abstract

Computed tomography (CT) plays key roles in radiotherapy treatment planning and PET attenuation correction (AC). Magnetic resonance (MR) imaging has better soft tissue contrast than CT and has no ionizing radiation but cannot directly provide information about photon interactions with tissue that is needed for radiation treatment planning and AC. Therefore, estimating synthetic CT (sCT) images from corresponding MR images and obviating CT scanning is of great interest, but can be particularly challenging in the abdomen owing to a range of tissue types and physiologic motion. For this purpose, inspired by deep learning, we design a novel generative adversarial network (GAN) model that organically combines ResNet, U-net, and auxiliary classifier-augmented GAN (RU-ACGAN for short). The significance of our effort is three-fold: 1) The combination of ResNet and U-net, instead of only the U-net which was commonly used in existing conditional GAN, is enlisted to constitute the generative network in RU-ACGAN. This has the potential to generate more accurate CT than existing methods. 2) Adding the classifier to the discriminant network makes the training process of the proposed model more stable, and thereby benefits the robustness of sCT estimation. 3) Owing to the delicate architecture, RU-ACGAN is capable of estimating superior sCT using only a limited quantity of training data. The experimental studies on ten subjects’ MR-CT pair images indicate that the proposed RU-ACGAN model can capture the potential, non-linear matching between the MR and CT images, and thus achieves the better performance for sCT estimation for the abdomen than many other existing methods.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61772241 and 61702225, by the Natural Science Foundation of Jiangsu Province under Grant BK20160187, by the Fundamental Research Funds for the Central Universities under Grant JUSRP51614A, by 2016 Qinglan Project of Jiangsu Province, by 2016 Six Talent Peaks Project of Jiangsu Province, and by the Science and Technology Demonstration Project of Social Development of Wuxi under Grant WX18IVJN002. Research in this publication was also supported by National Cancer Institute of the National Institutes of Health, USA, under award number R01CA196687 (The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, USA).

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Authors and Affiliations

  1. School of Artificial Intelligence and Computer Science and Jiangsu Key Lab of Media Design and Software Technology, Jiangnan University, Wuxi, 214122, China

    Pengjiang Qian, Ke Xu, Qiankun Zheng & Huan Yang

  2. Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA

    Tingyu Wang

  3. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA

    Atallah Baydoun

  4. Department of Internal Medicine, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, 44106, USA

    Atallah Baydoun

  5. Department of Radiology, Case Western Reserve University, Cleveland, OH, 44106, USA

    Junqing Zhu

  6. Department of Radiation Oncology and Case Center for Imaging Research, Case Western Reserve University, Cleveland, OH, 44106, USA

    Bryan Traughber

  7. Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Cleveland, OH, 44106, USA

    Bryan Traughber

  8. Department of Radiation Oncology, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, 44106, USA

    Bryan Traughber

  9. Department of Radiology and Case Center for Imaging Research, Case Western Reserve University, Cleveland, OH, 44106, USA

    Raymond F. Muzic Jr.

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  1. Pengjiang Qian
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Qian, P., Xu, K., Wang, T. et al. Estimating CT from MR Abdominal Images Using Novel Generative Adversarial Networks. J Grid Computing 18, 211–226 (2020). https://doi.org/10.1007/s10723-020-09513-3

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