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Abstract #1922

Machine-Learning-Based Segmentation of Ischemic Penumbra By Using Diffusion Tensor Metrics in a Rat Model

Cheng-Yu Chen1,2,3,4, Po-Chih Kuo5, Yung-Chieh Chen1, Yu-Chieh Jill Kao2, Ching-Yen Lee6, Hsiao-Wen Chung7, and Duen-Pang Kuo1
1Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan, 2Translational Imaging Research Center, Taipei, Taiwan, 3Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan, 4Radiogenomic Research Center, Taipei Medical University Hospital, Taipei, Taiwan, 5Institute of Statistical Science, Academia Sinica, Taipei, Taiwan, 6TMU Research Center for Artifical Intelligence in Medicine, Taipei, Taiwan, 7Graduate Institute of Biomedical Electrics and Bioinformatics, National Taiwan University, Taipei, Taiwan

In the present study, we developed a 2-level classification model with an overall accuracy of 88.1 ± 6.7% for discriminating the stroke hemisphere into the infarct core (IC), ischemic penumbra (IP), and normal tissue regions on a voxel-wise basis in a permanent left middle cerebral artery occlusion model. According to the analysis results, we suggest that a single diffusion tensor imaging (DTI) sequence combined with machine learning (ML) algorithms can dichotomize ischemic tissue into the IC and IP, which are comparable to the conventional perfusion–diffusion mismatch.

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