Markov random field texture generation with an internalized database using a conditional encoder-decoder structure

Yongfeng Gao; Ti Bai; Siming Lu; Shaojie Chang; Hao Zhang; Mahsa Hoshmand-Kochi; Zhengrong Liang

doi:10.1117/12.2613033

4 April 2022 Markov random field texture generation with an internalized database using a conditional encoder-decoder structure

Yongfeng Gao, Ti Bai, Siming Lu, Shaojie Chang, Hao Zhang, Mahsa Hoshmand-Kochi, Zhengrong Liang

Author Affiliations +

Proceedings Volume 12031, Medical Imaging 2022: Physics of Medical Imaging; 120311G (2022) https://doi.org/10.1117/12.2613033
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

The tissue specific MRF type texture prior (MRFt) proposed in our previous work has been demonstrated to be advantageous in various clinical tasks. However, this MRFt model requires a previous full-dose CT (FdCT) scan of the same patient to extract the texture information for LdCT reconstructions. This requirement may not be met in practice. To alleviate this limitation, we propose to build a MRFt generator by internalizing a database with paired FdCT and LdCT scans using a (conditional) encoder-decoder structure model. We denote this method as the MRFtG-ConED. This generation model depends only on physiological features thus is robust for ultra-low dose CT scans (i.e., dosage < 10mAs). When the dosage is not extremely low (i.e., dosage > 10mAs), some texture information from LdCT images reconstructed by filtered back projection (FBP) can be also used to provide extra information.

Conference Presentation

Citation Download Citation

Yongfeng Gao, Ti Bai, Siming Lu, Shaojie Chang, Hao Zhang, Mahsa Hoshmand-Kochi, and Zhengrong Liang "Markov random field texture generation with an internalized database using a conditional encoder-decoder structure", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 120311G (4 April 2022); https://doi.org/10.1117/12.2613033

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