Study on spectral CT material decomposition via deep learning

被引：0
|
作者：
Wu, Xiaochuan [1 ]
He, Peng [1 ]
Long, Zourong [1 ]
Li, Pengcheng [1 ]
Wei, Biao [1 ]
Feng, Peng [1 ]
机构：
[1] Chongqing Univ, Key Lab Optoelect Technol & Syst, Educ Minist China, 174 Shazheng St, Chongqing 400044, Peoples R China
来源：
15TH INTERNATIONAL MEETING ON FULLY THREE-DIMENSIONAL IMAGE RECONSTRUCTION IN RADIOLOGY AND NUCLEAR MEDICINE | 2019年 / 11072卷
基金：
国家重点研发计划; 中国国家自然科学基金;
关键词：
spectral CT; photon-counting detector; material decomposition; deep learning;
D O I：
10.1117/12.2533019
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
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.
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页数：4
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