Hierarchical convolutional network for sparse-view X-ray CT reconstruction

Ziling Wu; Ting Yang; Ling Li; Yunhui Zhu

doi:10.1117/12.2521239

13 May 2019 Hierarchical convolutional network for sparse-view X-ray CT reconstruction

Ziling Wu, Ting Yang, Ling Li, Yunhui Zhu

Proceedings Volume 10990, Computational Imaging IV; 109900V (2019) https://doi.org/10.1117/12.2521239
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

We present a hierarchical imaging reconstruction algorithm for a 3D phase tomography based on the densely extracted features on a multi-band pyramid of convolutional network. By implementing a layer-wise hierarchical machine learning network and combine different bands of information for the imaging retrieval, a more efficient and adaptive learning strategy is established to enable an accurate reconstruction with fewer training data and improved accuracy. In addition, the distinction of intensity and spectral bands in the feature training process enables bias correction for reconstruction under varied conditions. In particular, we demonstrate a robust imaging reconstruction for a sparse-view phase tomography application, where spectrally biased phase diffraction and intensity-biased photon noise are both successfully corrected for.

Conference Presentation

Citation Download Citation

Ziling Wu, Ting Yang, Ling Li, and Yunhui Zhu "Hierarchical convolutional network for sparse-view X-ray CT reconstruction", Proc. SPIE 10990, Computational Imaging IV, 109900V (13 May 2019); https://doi.org/10.1117/12.2521239

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