Thermal ablation is a minimally invasive procedure for treating small or unresectable tumors. Although CT is widely used for guiding ablation procedures, yet the contrast of tumors against normal soft tissues is often poor in CT scans, aggravating the accurate thermal ablation. In this paper, we propose a fast MR-CT image registration method to overlay pre-procedural MR (pMR) and pre-procedural CT (pCT) images onto an intra-procedural CT (iCT) image to guide the thermal ablation of liver tumors. At the pre-procedural stage, the Cycle-GAN model with mutual information constraint is employed to generate the synthesized CT (sCT) image from the input pMR. Then, pMR-pCT image registration is carried out via traditional mono-modal sCT-pCT image registration. At the intra-procedural stage, the region of the probe and its artifacts are automatically localized and inpainted in the iCT image. Then, an unsupervised registration network (UR-Net) is used to efficiently align the pCT with the inpainted iCT (inpCT) image. The final transform from pMR to iCT is obtained by concatenating the two estimated transforms, i.e., (i) from pMR image space to pCT image space (via sCT) and (ii) from pCT image space to iCT image space (via inpCT). The proposed method has been evaluated over a real clinical dataset and compared with state-of-the-art methods. Experimental results confirm that the proposed method achieves high registration accuracy with fast computation speed.
Bibliographical notePublisher Copyright:
© 2020 Elsevier B.V.
- Deep learning
- Image registration
- Image-guided intervention
- Liver tumor thermal ablation
ASJC Scopus subject areas
- Radiological and Ultrasound Technology
- Radiology Nuclear Medicine and imaging
- Computer Vision and Pattern Recognition
- Health Informatics
- Computer Graphics and Computer-Aided Design