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Abstract #2380

Improving Automatic Cerebral Microbleed Detection Using Algorithmic Methods in Multi-Echo STAGE Data

Miller Fawaz1, Sara Gharabaghi1, Mojtaba Jokar1, Ying Wang1,2, Chao Chai3, and E. Mark Haacke1,2
1Magnetic Resonance Innovations, Inc., Bingham Farms, MI, United States, 2Wayne State University, Detroit, MI, United States, 3Tianjin First Central Hospital, Tianjin, China

Automatic cerebral microbleed detection is attainable with our two step model for many disease states. We attributed previously shown lower performance in STAGE data to veins and edges, including some in the basal ganglia. We improved our existing pipeline for this detection by adding a false positive correction step to our pipeline using previously tested and new data. The results were improved overall, including on previously tested STAGE data, new STAGE data and our previously tested single echo data (multiple diseases). This makes our pipeline a viable and versatile real time automatic microbleed detection procedure.

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