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Deep learning-based Slice-to-Volume (2DUS to 3DUS) Registration

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DeepRegS2V

2D US-CT/MRI registration can aid in interpreting anatomical details and identifying tumours, but their clinical application has been hindered by the tradeoff between alignment accuracy and runtime performance, particularly when compensating for liver motion due to patient breathing or movement. Therefore, we proposed a 2D US-CT/MRI registration workflow. In our previous work, we developed the first registration step, “3D US-to-CT/MRI”, to facilitate the procedure. This work focuses on the second registration step, “dynamic 2D-to-3D US”, to demonstrate the clinical effectiveness of mitigating the effect of liver motion, thereby improving tumor visibility during procedures.

We also put our preprint here, just in case you are interested.

Citation

If you want to use our code for your research, please cite our publication.

@article{xing20233d,
  title={3D US-CT/MRI registration for percutaneous focal liver tumor ablations},
  author={Xing, Shuwei and Romero, Joeana Cambranis and Roy, Priyanka and Cool, Derek W and Tessier, David and Chen, Elvis CS and Peters, Terry M and Fenster, Aaron},
  journal={International Journal of Computer Assisted Radiology and Surgery},
  volume={18},
  number={7},
  pages={1159--1166},
  year={2023},
  publisher={Springer}
}

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