3D US-CT/MRI registration for percutaneous focal liver tumor ablations

Purpose US-guided percutaneous focal liver tumor ablations have been considered promising curative treatment techniques. To address cases with invisible or poorly visible tumors, registration of 3D US with CT or MRI is a critical step. By taking advantage of deep learning techniques to efficiently detect representative features in both modalities, we aim to develop a 3D US-CT/MRI registration approach for liver tumor ablations. Methods Facilitated by our nnUNet-based 3DUSvessel segmentation approach, we propose a coarse-to-fine 3D US-CT/MRI image registration pipeline based on the liver vessel surface and centerlines. Then, phantom, healthy volunteer and patient studies are performed to demonstrate the effectiveness of our proposed registration approach. Results Our nnUNet-based vessel segmentation model achieved a Dice score of 0.69. In healthy volunteer study, 11 out of 12 3D US-MRI image pairs were successfully registered with an overall centerline distance of 4.03 +/- 2.68mm. Two patient cases achieved target registration errors (TRE) of 4.16mm and 5.22mm. Conclusion We proposed a coarse-to-fine 3D US-CT/MRI registration pipeline based on nnUNet vessel segmentationmodels. Experiments based on healthy volunteers and patient trials demonstrated the effectiveness of our registration workflow. Our code and example data are publicly available in this repository.

Automated summary

This study proposes a 3D US-CT/MRI registration pipeline based on nnUNet vessel segmentation models. The effectiveness of the proposed approach is demonstrated through experiments on healthy volunteers and patient trials.