Study on spectral CT material decomposition via deep learning

Xiaochuan Wu; Peng He; Zourong Long; Pengcheng Li; Biao Wei; Peng Feng

doi:10.1117/12.2533019

28 May 2019 Study on spectral CT material decomposition via deep learning

Xiaochuan Wu, Peng He, Zourong Long, Pengcheng Li, Biao Wei, Peng Feng

Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 110723C (2019) https://doi.org/10.1117/12.2533019
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States

Abstract

Spectral computed tomography (CT) has the capability to resolve the energy levels of incident photons, which is able to distinguish different material compositions. Nowadays, deep learning has generated widespread attention in CT imaging applications. In this paper, a method of material decomposition for spectral CT based on improved Fully Convolutional DenseNets (FC-DenseNets) was proposed. Spectral data were acquired by a photon-counting detector and reconstructed spectral CT images were used to construct a training dataset. Experimental results showed that the proposed method could effectively identify bone and different tissues in high noise levels. This work could establish guidelines for multi-material decomposition approaches with spectral CT.

Citation Download Citation

Xiaochuan Wu, Peng He, Zourong Long, Pengcheng Li, Biao Wei, and Peng Feng "Study on spectral CT material decomposition via deep learning", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110723C (28 May 2019); https://doi.org/10.1117/12.2533019

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