Dr. Chao Huang Profile

Dr. Chao Huang

at Institute of Automation

SPIE Involvement:

Author

Publications (1)

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 | 4 March 2019 Presentation + Paper

Fast and robust reconstruction method for fluorescence molecular tomography based on deep neural network

Chao Huang, Hui Meng, Yuan Gao, Shixin Jiang, Kung Wang, Jie Tian

Proceedings Volume 10881, 108811K (2019) https://doi.org/10.1117/12.2508468

KEYWORDS: Neural networks, Fluorescence tomography, Inverse problems

Read Abstract +

Fluorescence molecular tomography (FMT) is a promising imaging technique in applications of preclinical research. However, the complexity of radiative transfer equation (RTE) and the ill-poseness of the inverse problem limit the effectiveness of FMT reconstruction. In this research, we proposed a novel Deep Convolutional Neural Network (DCNN), Gated Recurrent Unit (GRU) and Multiple Layer Perception (MLP) based method (DGMM) for FMT reconstruction. Instead of establishing the photon transmission models and solving the inverse problem, the proposed method directly fits the nonlinear relationship between fluorescence intensity at the boundary and fluorescent source in biological tissue. For details, DGMM consists of three stages: In the first stage, the measured optical intensity was encoded into a feature vector by transferring the VGG16 model; In the second stage, we fused all encoded feature vectors into one feature vector by using GRU based network; In the last stage, the fused feature vector was used to reconstruct the fluorescent sources by MLP model. To evaluate the performance of our proposed method, a 3D digital mouse was utilized to generate FMT Monte Carlo simulation samples. In quantitative analysis, the results demonstrated that DGMM method has comparable performance with conventional method in tumor position locating. To the best of our knowledge, this is the first study that employed DCNN based methods for FMT reconstruction, which holds a great potential of improving the reconstruction quality of FMT.

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