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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