Abstract #1922

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

Cheng-Yu Chen^1,2,3,4, 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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