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Abstract #4287

Deep learning of MRI contrast enhancement for mapping cerebral blood volume from single-modal non-contrast scan with Mamba3D-CNN hybrid model

Yiren Zhang1, Aaron Cao2, Vishwanatha Rao1, and Jia Guo3
1Department of Biomedical Engineering, Columbia University, New York, NY, United States, 2University of California, Santa Barbara,, Santa Barbara, CA, United States, 3Department of Psychiatry, Columbia University, New York, NY, United States

Synopsis

Keywords: Analysis/Processing, Brain, Deep Learning, MRI, Gadolinium, CBV, 3D Mamba

Motivation: MRI contrast agents, such as Gadolinium, are essential for high-resolution mapping of brain metabolism but pose health risks due to their invasive intravenous administration.

Goal(s): To address these risks, we introduce a novel deep learning approach utilizing a 3D patch-based Mamba model to replace traditional contrast agents and enhance imaging quality.

Approach: This study is the first to apply a 3D patch-based Mamba model specifically for this purpose.

Results: Our model surpasses previous methods in estimating cerebral blood volume with sub-millimeter resolution, validated on MRI scans of aging and Alzheimer’s patients, and is clinically applicable using a single T1-weighted pre-contrast scan.

Impact: By accurately estimating cerebral blood volume, this approach eliminates risks associated with gadolinium administration, such as long-term tissue retention. This advancement enables functional imaging for researchers and clinicians, providing a safe and cost-effective alternative for studying and diagnosing neurodegenerative diseases.

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Keywords