Meeting Banner
Abstract #4208

Deep Learning Method Based Automatic Subtraction of Liver DCE-MR Images

Zhijun Geng1,2, Botong Wu3,4, Tiantian Liu3, Niange Yu3, Dandan Zheng3, Hua Guo4, and Chuanmiao Xie1,2
1The department of medical imaging, Sun Yat-sen University,Cancer Center, Guangzhou, China, 2States key laboratory in South China, Guangzhou, China, 3Shukun (Beijing) Technology Co., Ltd, Beijing, China, 4Center for Biomedical Imaging Research, Department of Biomedical Engineering,School of Medicine, Tsinghua University, Beijing, China


Subtraction images are an important part of routine multi-phase contrast-enhanced MRI for characterizing enhancement of lesions which are intrinsically hyperintense on T1-weighted imaging. Successful subtraction MRI is dependent on precise 3D co-registration of the pre- and contrast-enhanced source data. However, there still lack a robust, convenient, time efficient and labor free method for automatically image subtraction. This study developed a deep learning based nonrigid registration algorithm, measure the improvement in displacement after registration using anatomic landmarks and automatically generate the subtraction liver images.

This abstract and the presentation materials are available to members only; a login is required.

Join Here