AirNet-SNL: End-to-End Training of Iterative Reconstruction and Deep Neural Network Regularization for Sparse-Data XPCI CT

Dennis J. Lee; John Mulcahy-Stanislawczyk; Edward Jimenez; Derek West; Ryan Goodner; Collin Epstein

doi:10.1364/DH.2021.DF4F.2

OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2021),
paper DF4F.2
•https://doi.org/10.1364/DH.2021.DF4F.2

AirNet-SNL: End-to-End Training of Iterative Reconstruction and Deep Neural Network Regularization for Sparse-Data XPCI CT

Dennis J. Lee, John Mulcahy-Stanislawczyk, Edward Jimenez, Derek West, Ryan Goodner, and Collin Epstein

Digital Holography and Three-Dimensional Imaging 2021

Washington, DC United States
19–23 July 2021
ISBN: 978-1-943580-89-7

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Novel Diagnostic and Data Analysis (DF4F)

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D. J. Lee, J. Mulcahy-Stanislawczyk, E. Jimenez, D. West, R. Goodner, and C. Epstein, "AirNet-SNL: End-to-End Training of Iterative Reconstruction and Deep Neural Network Regularization for Sparse-Data XPCI CT," in OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP), H. Hua, B. Javidi, M. Martinez-Corral, O. Matoba, A. Stern, S. Thibault, T. Alieva, J. Ke, F. Willomitzer, F. Okten, P. Silveira, P. Banerjee, E. Stoykova, D. Chu, J. Park, F. Imai, C. Joo, M. Digman, D. Gardner, S. Gladysz, D. LeMaster, S. Basu, and O. Korotkova, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper DF4F.2.

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Abstract

We present a deep learning image reconstruction method called AirNet-SNL for sparse view computed tomography. It combines iterative reconstruction and convolutional neural networks with end-to-end training. Our model reduces streak artifacts from filtered back-projection with limited data, and it trains on randomly generated shapes. This work shows promise to generalize learning image reconstruction.

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