Mr. Abhinaav Ramesh Profile

Abhinaav Ramesh

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 6 March 2023 Paper

Predicting individual brain MRIs at any age using style encoding generative adversarial networks

Shruti Gadewar, Abhinaav Ramesh, Mengting Liu, Iyad Ba Gari, Talia Nir, Paul Thompson, Neda Jahanshad

Proceedings Volume 12567, 125671H (2023) https://doi.org/10.1117/12.2669741

KEYWORDS: Brain, Magnetic resonance imaging, 3D modeling, Neuroimaging, Data modeling, Brain diseases, Anatomy, 3D image processing, Deep learning

Read Abstract +

Brain structural changes in older adults over time can help identify and predict which individuals are at risk for neurodegenerative disorders and dementias. These trajectories are traditionally calculated by assessing localized rates of brain tissue atrophy from a longitudinal series of brain MRIs as compared to group averages. However, these methods do not preserve individual differences in brain structure, which may provide added information regarding risk. A map of how an individual’s brain may look at a given age - in the case of a normal, healthy, aging trajectory - may help to identify deviations and abnormalities when presented with a true scan at that age. Here, we consider estimating the age-related brain changes as a domain transfer problem. We develop a fully unsupervised generative adversarial network (GAN) with cycle consistency reconstruction losses, trained on cross-sectional brain MRI data from participants of the UK Biobank aged 45 to 81. We show that brain MRIs for males and females at a given age can be predicted by converting the content information encoded in a T1-weighted MRI (i.e., the individual’s identifying anatomical features), accompanied by adding the style (age/sex) information from a reference group. Results on the PREVENT-AD cohort demonstrated that our style-encoding domain transfer model can predict follow-up brain MRIs, successfully, without relying on longitudinal data from the subjects. We show how deviations from the predicted images are indicative of factors related to neurodegenerative disease risk.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years