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Synthesis and Inpainting-Based MR-CT Registration for Image-Guided Thermal Ablation of Liver Tumors

  • Dongming Wei
  • Sahar Ahmad
  • Jiayu Huo
  • Wen Peng
  • Yunhao Ge
  • Zhong Xue
  • Pew-Thian Yap
  • Wentao Li
  • Dinggang ShenEmail author
  • Qian WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11768)

Abstract

Thermal ablation is a minimally invasive procedure for treating small or unresectable tumors. Although CT is widely used for guiding ablation procedures, the contrast of tumors against surrounding normal tissues in CT images is often poor, aggravating the difficulty in accurate thermal ablation. In this paper, we propose a fast MR-CT image registration method to overlay a pre-procedural MR (pMR) image onto an intra-procedural CT (iCT) image for guiding the thermal ablation of liver tumors. By first using a Cycle-GAN model with mutual information constraint to generate synthesized CT (sCT) image from the corresponding pMR, pre-procedural MR-CT image registration is carried out through traditional mono-modality CT-CT image registration. At the intra-procedural stage, a partial-convolution-based network is first used to inpaint the probe and its artifacts in the iCT image. Then, an unsupervised registration network is used to efficiently align the pre-procedural CT (pCT) with the inpainted iCT (inpCT) image. The final transformation from pMR to iCT is obtained by combining the two estimated transformations, i.e., (1) from the pMR image space to the pCT image space (through sCT) and (2) from the pCT image space to the iCT image space (through inpCT). Experimental results confirm that the proposed method achieves high registration accuracy with a very fast computational speed.

Keywords

Thermal ablation Liver tumor Image registration Neural network 

Notes

Acknowledgement

This work was partially supported by the National Key Research and Development Program of China (2018YFC0116400) and STCSM (19QC1400600).

Supplementary material

490279_1_En_57_MOESM1_ESM.pdf (106 kb)
Supplementary material 1 (pdf 105 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Dongming Wei
    • 1
    • 2
    • 4
  • Sahar Ahmad
    • 2
  • Jiayu Huo
    • 1
  • Wen Peng
    • 3
  • Yunhao Ge
    • 4
  • Zhong Xue
    • 4
  • Pew-Thian Yap
    • 2
  • Wentao Li
    • 5
  • Dinggang Shen
    • 2
    Email author
  • Qian Wang
    • 1
    Email author
  1. 1.Institute for Medical Imaging Technology, School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Department of Radiology and Biomedical Research Imaging Center (BRIC)University of North Carolina at Chapel HillChapel HillUSA
  3. 3.North China Electric Power UniversityBeijingChina
  4. 4.Shanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
  5. 5.Shanghai Cancer CenterFudan UniversityShanghaiChina

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