Unpaired whole-body MR to CT synthesis with correlation coefficient constrained adversarial learning

Yunhao Ge; Zhong Xue; Tuoyu Cao; Shu Liao

doi:10.1117/12.2512479

15 March 2019 Unpaired whole-body MR to CT synthesis with correlation coefficient constrained adversarial learning

Yunhao Ge, Zhong Xue, Tuoyu Cao, Shu Liao

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 1094905 (2019) https://doi.org/10.1117/12.2512479
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

MR to CT image synthesis plays an important role in medical image analysis, and its applications included, but not limited to PET-MR attenuation correction and MR only radiation therapy planning. Recently, deep learning-based image synthesis techniques have achieved much success. However, most of the current methods require large scales of paired data from two different modalities, which greatly limits their usage as in some situation paired data is infeasible to obtain. Some efforts have been proposed to relax this constraint such as cycle-consistent adversarial networks (Cycle-GAN). However, the cycle consistency loss is an indirect structural similarity constraint of input and synthesized images, and it can lead to inferior synthesized results. To overcome this challenge, a novel correlation coefficient loss is proposed to directly enforce the structural similarity between MR and synthesized CT image, which can not only improve the representation capability of the network but also guarantee the structure consistency between MR and synthesized CT images. In addition, to overcome the problem of big variance in whole-body mapping, we use the multi-view adversarial learning scheme to combine the complementary information along different directions to provide more robust synthesized results. Experimental results demonstrate that our method can achieve better MR to CT synthesis results both qualitatively and quantitatively with unpaired MR and CT images compared with state-of-the-art methods.

Conference Presentation

Citation Download Citation

Yunhao Ge, Zhong Xue, Tuoyu Cao, and Shu Liao "Unpaired whole-body MR to CT synthesis with correlation coefficient constrained adversarial learning", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094905 (15 March 2019); https://doi.org/10.1117/12.2512479

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