CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13152 > Article
Presentation + Paper
17 October 2024 Interior photon-counting CT data denoising via multi-agent reinforcement learning
Md Sayed Tanveer, Christopher Wiedeman, Yongyi Shi, Hengyong Yu, Ge Wang
Author Affiliations +
Proceedings Volume 13152, Developments in X-Ray Tomography XV; 131520C (2024) https://doi.org/10.1117/12.3029551
Event: Optical Engineering + Applications, 2024, San Diego, California, United States
Abstract
Interior photon-counting computed tomography (PCCT) scans are essential for obtaining high-resolution images at minimal radiation dose by focusing only on a region of interest. However, designing a deep learning model for denoising a PCCT interior scan is rather challenging. Recently, several studies explored deep reinforcement learning (RL)-based models with far fewer parameters than those typical for supervised and self-learning models. Such an RL model can be effectively trained on a small dataset, and yet be generalizable and interpretable. In this work, we design an RL model to perform multichannel PCCT scan denoising. Because a reliable reward function is crucial for optimizing the RL model, we focus on designing a small denoising autoencoder-based reward network to learn the latent representation of full-dose simulated PCCT data and use the reconstruction error to quantify the reward. We also use domain-specific batch normalization for unsupervised domain adaptation with a limited amount of multichannel PCCT data. Our results show that the proposed model achieves excellent denoising results, with a significant potential for clinical and preclinical PCCT denoising.
Conference Presentation
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Md Sayed Tanveer, Christopher Wiedeman, Yongyi Shi, Hengyong Yu, and Ge Wang "Interior photon-counting CT data denoising via multi-agent reinforcement learning", Proc. SPIE 13152, Developments in X-Ray Tomography XV, 131520C (17 October 2024); https://doi.org/10.1117/12.3029551
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 11 PAGES + PRESENTATION
DOWNLOAD PAPER SAVE TO MY LIBRARY
WATCH
PRESENTATION
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Denoising
Image restoration
Data modeling
Computed tomography
Reconstruction algorithms
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top