Abstract #1382

Brain Age Gap Prediction and Application to Alzheimer’s Disease Using Synthetic T1 and T2 Maps with Deep Learning

Peiran Xu^1,2, Shihan Qiu², Yifan Gao^2,3, Zengtian Deng^1,2, Sreekanth Madhusoodhanan², Pascal Sati^2,4, Yibin Xie², and Debiao Li^1,2

¹University of California, Los Angeles, Los Angeles, CA, United States, ²Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, United States, ³School of Medicine, Tsinghua University, Beijing, China, ⁴Department of Neurology, Cedars Sinai Medical Center, Los Angeles, CA, United States

Synopsis

Keywords: Diagnosis/Prediction, Aging, Quantitative Imaging, Alzheimer's Disease

Motivation: Brain Age Gap (BAG), which measures the difference between chronological and predicted brain age, is valuable for assessing neurodegenerative disorders such as Alzheimer’s disease (AD).

Goal(s): This study used synthetic T1 and T2 maps to predict BAG and explored its value in detecting AD.

Approach: Synthetic T1 and T2 maps were used as input to deep learning models, combined with bias-correction techniques, to enhance performance in brain age prediction.

Results: Synthetic mappings improved accuracy and generalizability over traditional weighted MR images in predicting brain age. Integrating BAG in image analysis further enhanced disease classification, underscoring its clinical potential.

Impact: The integration of T1 and T2 mapping improves brain age gap prediction and disease classification, offering a robust, accurate tool for early detection and monitoring of neurodegenerative diseases.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.