Thermal ablation is a minimally invasive procedure for treat-ing 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 cor-responding 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.

本文提出了一种快速的 MR-CT 图像配准方法，通过 Cycle-GAN 生成的合成 CT 图像将术前 MR 图像叠加到术中 CT 图像上，然后使用基于部分卷积的网络对 iCT 图像进行修复和无监督配准网络对 pMR 和 inpCT 图像进行匹配。实验结果表明，该方法具有高配准精度和非常快的计算速度。