TY - JOUR
T1 - SLIR
T2 - Synthesis, localization, inpainting, and registration for image-guided thermal ablation of liver tumors
AU - Wei, Dongming
AU - Ahmad, Sahar
AU - Huo, Jiayu
AU - Huang, Pu
AU - Yap, Pew Thian
AU - Xue, Zhong
AU - Sun, Jianqi
AU - Li, Wentao
AU - Shen, Dinggang
AU - Wang, Qian
N1 - Funding Information:
This work was partially supported by the National Key Research and Development Program of China (2018YFC0116400) and Science and Technology Commission of Shanghai Municipality ( 19QC1400600 , 17411953300 , 18JC1420305 ).
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - Deep learning
KW - Image registration
KW - Image-guided intervention
KW - Liver tumor thermal ablation
UR - http://www.scopus.com/inward/record.url?scp=85087282409&partnerID=8YFLogxK
U2 - 10.1016/j.media.2020.101763
DO - 10.1016/j.media.2020.101763
M3 - Article
C2 - 32623279
AN - SCOPUS:85087282409
VL - 65
JO - Medical Image Analysis
JF - Medical Image Analysis
SN - 1361-8415
M1 - 101763
ER -