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BCD-Net for Low-Dose CT Reconstruction: Acceleration, Convergence, and Generalization

  • Il Yong Chun
  • Xuehang Zheng
  • Yong LongEmail author
  • Jeffrey A. Fessler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

Obtaining accurate and reliable images from low-dose computed tomography (CT) is challenging. Regression convolutional neural network (CNN) models that are learned from training data are increasingly gaining attention in low-dose CT reconstruction. This paper modifies the architecture of an iterative regression CNN, BCD-Net, for fast, stable, and accurate low-dose CT reconstruction, and presents the convergence property of the modified BCD-Net. Numerical results with phantom data show that applying faster numerical solvers to model-based image reconstruction (MBIR) modules of BCD-Net leads to faster and more accurate BCD-Net; BCD-Net significantly improves the reconstruction accuracy, compared to the state-of-the-art MBIR method using learned transforms; BCD-Net achieves better image quality, compared to a state-of-the-art iterative NN architecture, ADMM-Net. Numerical results with clinical data show that BCD-Net generalizes significantly better than a state-of-the-art deep (non-iterative) regression NN, FBPConvNet, that lacks MBIR modules.

Notes

Acknowledgments

This work is supported in part by NSFC 61501292 and NIH U01 EB018753. The authors thank GE Healthcare for supplying the clinical data. The authors thank Zhipeng Li for his help with debugging the codes.

Supplementary material

490281_1_En_4_MOESM1_ESM.pdf (2.4 mb)
Supplementary material 1 (pdf 2449 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Il Yong Chun
    • 1
  • Xuehang Zheng
    • 2
  • Yong Long
    • 2
    Email author
  • Jeffrey A. Fessler
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of MichiganAnn ArborUSA
  2. 2.University of Michigan - Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong UniversityShanghaiChina

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