Quadratic neural networks for CT metal artifact reduction

Fenglei Fan; Hongming Shan; Lars Gjesteby; Ge Wang

doi:10.1117/12.2530363

11 September 2019 Quadratic neural networks for CT metal artifact reduction

Fenglei Fan, Hongming Shan, Lars Gjesteby, Ge Wang

Proceedings Volume 11113, Developments in X-Ray Tomography XII; 111130W (2019) https://doi.org/10.1117/12.2530363
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

Recently, deep learning has become the mainstream method in multiple fields of artificial intelligence / machine learning (AI/ML) applications, including medical imaging. Encouraged by the neural diversity in the human body, our group proposed to replace the inner product in the current artificial neuron with a quadratic operation on inputs (called quadratic neuron) for deep learning. Since the representation capability at the cellular level is enhanced by the quadratic neuron, we are motivated to build network architectures and evaluate the potential of quadratic neurons towards “quadratic deep learning”. Along this direction, our previous theoretical studies have shown advantages of quadratic neurons and quadratic networks in terms of efficiency and representation. In this paper, we prototype a quadratic residual neural network (Q-ResNet) by incorporating quadratic neurons into a convolutional residual structure, and then deploy it for CT metal artifact reduction. Also, we report our experiments on a simulated dataset to show that Q-ResNet performs better than the classic NMAR algorithm.

Conference Presentation

Citation Download Citation

Fenglei Fan, Hongming Shan, Lars Gjesteby, and Ge Wang "Quadratic neural networks for CT metal artifact reduction", Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111130W (11 September 2019); https://doi.org/10.1117/12.2530363

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

Includes PDF, HTML & Video, when available