Ms. Chuqing Feng Profile

Chuqing Feng

PhD candidate at Tsinghua Univ

SPIE Involvement:

Author

Publications (3)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 28 May 2019 Paper

Synthesize monochromatic images in spectral CT by dual-domain deep learning

Chuqing Feng, Zhiqiang Chen, Kejun Kang, Yuxiang Xing

Proceedings Volume 11072, 1107229 (2019) https://doi.org/10.1117/12.2534921

KEYWORDS: Neural networks, X-ray computed tomography, Calibration, Computed tomography, Photon counting, CT reconstruction

Read Abstract +

Spectral computed tomography (CT) with photon counting detectors (PCDs) can collect photons by setting different energy bins. It is well acknowledged that PCD-based spectral CT has great potential for lowering radiation dose and improve material discrimination. One critical processing in spectral CT is energy spectrum modelling or spectral information decomposition. In this work, we proposed a dual-domain deep learning (DDDL) method to calibrate a spectral CT system by a neural network. Without explicit energy spectrum and detector response model, we train a neural network to implicitly define the non-linear relationship in spectral CT. Virtual monochromatic attenuation maps are synthesized directly from polychromatic projections. Simulation and real experimental results verified the feasibilities and accuracies of the proposed method.

SPIE Journal Paper | 22 October 2018

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

Chuqing Feng, Kejun Kang, Yuxiang Xing

JMI, Vol. 6, Issue 01, 011006, (October 2018) https://doi.org/10.1117/12.10.1117/1.JMI.6.1.011006

KEYWORDS: Neural networks, Signal attenuation, Sensors, Sodium, Systems modeling, Spectral computed tomography, CT reconstruction, Computed tomography, Neurons, Calibration

Read 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.

Proceedings Article | 9 March 2018 Paper

A multi-energy material decomposition method for spectral CT using neural network

Chuqing Feng, Kejun Kang, Yuxiang Xing

Proceedings Volume 10573, 105734J (2018) https://doi.org/10.1117/12.2294611

KEYWORDS: Neural networks, CT reconstruction, Image processing, Image denoising

Read Abstract +

Spectral Computed Tomography (CT) has an advantage of providing energy spectrum information, which is valued on multi-energy material decomposition for material discrimination and accurate image reconstruction. However, due to the non-ideal physical effects of photon counting detectors (PCDs), such as charge sharing, pulse pileup and K-escape, serious spectral distortion is unavoidable in practical systems. The degraded spectrum will induce error into the decomposition model and affect the accuracy of material decomposition. Recently, artificial neural network has demonstrated great potential in the tasks of image segmentation, object detection, natural language processing, and etc. By adjusting the interconnection relationship among a large number of internal nodes, a neural network provides us a way to mine information from huge data depending on the complexity of the network system. Considering the difficulty of modeling the spectral CT system spectrum including the response function of a PCD and the superiority of data-driven characteristics of a neural network, we proposed a novel multi-energy material decomposition method using a neural network without the knowledge of spectral information. On one hand, specific linear attenuation coefficients can be obtained directly through our method. It would help further material recognition and spectral CT reconstruction. On the other hand, the network outputs show outstanding performance on image denoising and artifacts suppression. Our method can fit for different selections of training materials and different settings of imaging systems such as different number of energy bins and energy bin thresholds. According to our test results, the trained neural network has a good generalization ability.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years