Quantifying the quality of GAN-synthesized images: a study on synthesizing post-contrast sequences from pre-contrast sequences in breast DCE-MRI

Zhengbo Zhou; Dooman Arefan; Margarita L. Zuley; Jules H. Sumkin; Shandong Wu

doi:10.1117/12.3008803

3 April 2024 Quantifying the quality of GAN-synthesized images: a study on synthesizing post-contrast sequences from pre-contrast sequences in breast DCE-MRI

Zhengbo Zhou, Dooman Arefan, Margarita L. Zuley, Jules H. Sumkin, Shandong Wu

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129272A (2024) https://doi.org/10.1117/12.3008803
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Recent research has shown that Generative Adversarial Networks (GANs) can generate highly realistic breast images through synthesis. Nevertheless, most of these studies assessed image quality solely through visual appraisal or reader studies, lacking quantitative analysis for specific clinical applications. This study aimed to quantitatively assess the quality of GAN-generated breast MRI images in predicting breast cancer recurrence risk. To achieve this, we developed a GAN model to synthesize the first post-contrast sequences from precontrast MRI sequences, utilizing an in-house dataset comprising 200 patients with confirmed breast cancer and available breast Dynamic Contrast-Enhanced MRI (DCE-MRI) staging data. In our study, we conducted a statistical analysis of radiomic features, revealing that among the 98 features assessed, 83 showed no significant differences (with p-values greater than 0.05) when comparing synthesized images with real images. Additionally, we employed a Lasso-Regression model to predict the Oncotype DX recurrence risk score. This analysis indicated that the predictive results for recurrence risk, derived from both real and synthesized images, did not exhibit significant differences, underscoring the comparability of synthesized images in this context.

Citation Download Citation

Zhengbo Zhou, Dooman Arefan, Margarita L. Zuley, Jules H. Sumkin, and Shandong Wu "Quantifying the quality of GAN-synthesized images: a study on synthesizing post-contrast sequences from pre-contrast sequences in breast DCE-MRI", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129272A (3 April 2024); https://doi.org/10.1117/12.3008803

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