Abstract #2188

Automatic Quantitative Analysis of Low-field Infant Brain MR Images

Bo Peng^1,2,3, Baohua Hu^1,2,3, Mao Sheng⁴, Yuqi Liu⁴, Zhongchang Miao⁵, Zijun Dong⁶, Jian Bao⁷, SiSeung Kim⁷, Bing Keong Li⁷, and Yakang Dai^1,2,3

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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