Mr. Damond M. Li Profile

Damond M. Li

at California Polytechnic State Univ San Luis Obispo

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 | 9 May 2024 Presentation + Paper

Comparison between a novel compressed sensing-based neural network and traditional neural network approaches for electrical impedance tomography reconstruction

Damond Li, Marco Araiza, Long Wang

Proceedings Volume 12951, 129511I (2024) https://doi.org/10.1117/12.3010509

KEYWORDS: Neural networks, Reconstruction algorithms, Electrical conductivity, Deep learning, Tomography, Inverse problems, Data acquisition, Compressed sensing, Machine learning

Read Abstract +

Electrical Impedance Tomography (EIT) is a non-destructive and non-radioactive imaging technique used to detect anomalies in a material of interest. Applications of EIT range from medical imaging and early tumor detection to identifying structural damage. Within the past decade, deep learning (DL)-based EIT reconstruction has been an emerging field of study as it shows promise in addressing many of the challenges associated with the non-linear, ill-conditioned nature of EIT inverse problems. The DL-based approach allows for the conductivity of materials to be reconstructed directly through Neural Networks (NNs) as opposed to iteratively with conventional inverse reconstruction algorithms. So far, the reported DL-based NNs for EIT have mostly been trained by minimizing the Mean Squared Error (MSE) between the predicted and “true” outputs (i.e., conductivity distributions). The performance of these current NNs heavily relies on both the quality and quantity of training data. The NNs trained with simulated data may perform poorly with experimental data. On the other hand, generating sufficient experimental data NN training can be extremely expensive and time-consuming, if feasible at all. To advance the DL-based reconstruction for EIT, this study develops a novel NN architecture, trained with a custom loss function, that serves as a surrogate model for the compressed sensing-based EIT reconstruction algorithm. In other words, the NN is trained to mimic a compressed sensing algorithm that performs the EIT conductivity reconstruction. This approach enables the NN to accurately capture the electrical properties and characteristics of the sensing domain when trained with limited data of varying quality. The performance of the proposed NN was compared to other DL models trained with the traditional MSE loss function by evaluating their reconstruction resolution, accuracy, and other training metrics.

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