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Abstract #1226

Artificial Intelligence to Eliminate the Need for Gadolinium-Based Contrast Agents in Brain Perfusion Weighted Imaging

Anbo Cao1, Yiren Zhang2, Yan Kang3, and Jia Guo2
1Shenzhen University, Shenzhen, China, 2Columbia University, New York, NY, United States, 3Shenzhen Technology University, Shenzhen, China

Synopsis

Keywords: Stroke, Machine Learning/Artificial Intelligence

Motivation: Acute ischemic stroke (AIS) requires rapid intervention, but current methods for quantifying cerebral hemodynamic parameters rely on contrast agents, which are time-consuming and carry patient risks. Non-invasive alternatives are needed.

Goal(s): This study aims to use AI to quantify CBV, CBF, and MTT from non-contrast-enhanced imaging, providing a safer and faster alternative to contrast-based methods.

Approach: Two AI models (one-stage and two-stage) were trained on multimodal imaging from 120 subjects and evaluated through multiple metrics.

Results: The two-stage model outperformed the one-stage model, demonstrating higher consistency in parameter quantification for both whole-brain and lesion regions.

Impact: AI-generated hemodynamic parameters from non-contrast imaging offer significant clinical benefits, including reduced costs, faster acquisition, and improved patient safety. This approach could streamline stroke diagnosis and improve healthcare accessibility.

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