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Abstract #0676

Automatic Detection of Cerebral Microbleeds using Susceptibility Weighted Imaging and Deep Learning

Saifeng Liu1, David Utriainen1,2, Chao Chai3, Yongsheng Chen1,4, Lin Wang1, and E. Mark Haacke1,4

1the MRI Institute for Biomedical Research, Bingham Farms, MI, United States, 2Magnetic Resonance Innovations, Bingham Farms, MI, United States, 3Department of Radiology, Tianjin First Central Hospital, Tianjin, China, 4Department of Radiology, Wayne State University, Detroit, MI, United States

Detecting cerebral microbleeds can be time-consuming and prone to errors. Susceptibility weighted imaging (SWI) offers exquisite sensitivity to blood products. Furthermore, the use of SWI phase data makes it possible to differentiate diamagnetic calcifications from paramagnetic microbleeds. In this paper, we present a machine learning model based on residual neural networks, using SWI magnitude and phase data. The model was tested on 41 cases and compared with human raters with different levels of experience. A sensitivity of 93%, a positive predictive value of 80%, and 1.5 false positives per subject were achieved, outperforming both human raters and previously reported methods.

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