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Abstract #2188

Automatic Quantitative Analysis of Low-field Infant Brain MR Images

Bo Peng1,2,3, Baohua Hu1,2,3, Mao Sheng4, Yuqi Liu4, Zhongchang Miao5, Zijun Dong6, Jian Bao7, SiSeung Kim7, Bing Keong Li7, and Yakang Dai1,2,3
1Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China, 2Suzhou Key Laboratory of Medical and Health Information Technology, Suzhou, China, 3Jinan Guoke Medical Engineering Technology Development co., Ltd., Jinan, China, 4Department of Radiology, Children’s Hospital of Soochow University, Suzhou, China, 5Department of Radiology, The First People’s Hospital of Lianyungang, Jiangsu Province, China, 6Department of Medical Imaging, Lianyungang Women and Children Hospital and Health Institute, Jiangsu Province, China, 7Jiangsu LiCi Medical Device Co., Ltd., Lianyungang, China

Low-field MRI is foreseeable as a safer system for infants. However, low-field MR images have lower SNR and spatial resolution as compared to high-field images, thus processing of low-field infant brain MR image is challenging. In this study, an automated image processing method that can accurately perform brain extraction, tissue segmentation, and brain labeling on low-field infant brain MR images is developed. It is also capable to automatically construct the inner, middle, and outer surfaces of the cerebral cortex and provides automatic quantitative analysis of selected region of interest, which can be a helpful tool for researchers in neuroimaging studies.

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