Keywords: AI Diffusion Models, Blood vessels, non contrast, peripheral artery disease
Motivation: Traditional FBI is effective for evaluating peripheral artery disease but is time-intensive, requiring image subtraction for clarity between arteries and veins.
Goal(s): Develop a deep learning (DL) method to depict only arteries in non-subtraction MRA (nsMRA) images for faster, clearer diagnostics.
Approach: A U-Net-based DL model trained on standard FBI images removes veins from nsMRA images, creating artery-only depictions, and fusion images retain the original appearance while highlighting arteries.
Results: nsMRA saved ~64% scan time. DL processing removed veins effectively, enhancing readability for inexperienced readers and enabling simultaneous artery-vein evaluation through fusion images.
Impact: Our deep learning model effectively depicted arteries in non-subtraction MRA images, with a potential for enhancing vascular disease assessment and reducing confusion for untrained readers by clearly distinguishing arteries from veins. Further testing is needed for clinical generalizability.
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