A cascade-network framework for integrated registration of liver DCE-MR images

• DOI: 10.1016/j.compmedimag.2021.101887
• Publication Date: 2021-02-24T17:04:49Z
• Authors: Lijun Qian; Qing Zhou; Xiaohuan Cao; Wenjun Shen; Shiteng Suo; Shanshan Ma; Guoxiang Qu; Xuhua Gong; Yunqi Yan; Jianrong Xu; Luan Jiang
• Keywords: Image registration; Dynamic contrast

Registration of hepatic dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) is an important task for evaluation of transarterial chemoembolization (TACE) or radiofrequency ablation by quantifying enhancing viable residue tumor against necrosis. However, intensity changes due to contrast agents combined with spatial deformations render technical challenges for accurate registration of DCE-MRI, and traditional deformable registration methods using mutual information are often computationally intensive in order to tolerate such intensity enhancement and shape deformation variability. To address this problem, we propose a cascade network framework composed of a de-enhancement network (DE-Net) and a registration network (Reg-Net) to first remove contrast enhancement effects and then register the liver images in different phases. In experiments, we used DCE-MRI series of 97 patients from Renji Hospital of Shanghai Jiaotong University and registered the arterial phase and the portal venous phase images onto the pre-contrast phases. The performance of the cascade network framework was compared with that of the traditional registration method SyN in the ANTs toolkit and Reg-Net without DE-Net. The results showed that the proposed method achieved comparable registration performance with SyN but significantly improved the efficiency.