Fully connected neural network for virtual monochromatic imaging in spectral computed tomography

Chuqing Feng; Kejun Kang; Yuxiang Xing

doi:10.1117/1.JMI.6.1.011006

22 October 2018 Fully connected neural network for virtual monochromatic imaging in spectral computed tomography

Chuqing Feng, Kejun Kang, Yuxiang Xing

Author Affiliations +

Funded by: National Natural Science Foundation of China (NSFC), National Key Research and Development Program of China

Journal of Medical Imaging, Vol. 6, Issue 1, 011006 (October 2018). https://doi.org/10.1117/1.JMI.6.1.011006

Abstract

Spectral computed tomography (SCT) has advantages in multienergy material decomposition for material discrimination and quantitative image reconstruction. However, due to the nonideal physical effects of photon counting detectors, including charge sharing, pulse pileup and K-escape, it is difficult to obtain precise system models in practical SCT systems. Serious spectral distortion is unavoidable, which introduces error into the decomposition model and affects material decomposition accuracy. Recently, neural networks demonstrated great potential in image segmentation, object detection, natural language processing, etc. By adjusting the interconnection relationship among internal nodes, it provides a way to mine information from data. Considering the difficulty in modeling SCT system spectra and the superiority of data-driven characteristics of neural networks, we proposed a spectral information extraction method for virtual monochromatic attenuation maps using a simple fully connected neural network without knowing spectral information. In our method, virtual monochromatic linear attenuation coefficients can be obtained directly through our neural network, which could contribute to further material recognition. Our method also provides outstanding performance on denoising and artifacts suppression. It can be furnished for SCT systems with different settings of energy bins or thresholds. Various substances available can be used for training. The trained neural network has a good generalization ability according to our results. The testing mean square errors are about 1 × ^{10 − 05} cm^− 2.

Citation Download Citation

Chuqing Feng, Kejun Kang, and Yuxiang Xing "Fully connected neural network for virtual monochromatic imaging in spectral computed tomography," Journal of Medical Imaging 6(1), 011006 (22 October 2018). https://doi.org/10.1117/1.JMI.6.1.011006

Received: 5 June 2018; Accepted: 2 October 2018; Published: 22 October 2018

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE