Abstract
Purpose
Existing field generators (FGs) for magnetic tracking cause severe image artifacts in X-ray images. While FG with radio-lucent components significantly reduces these imaging artifacts, traces of coils and electronic components may still be visible to trained professionals. In the context of X-ray-guided interventions using magnetic tracking, we introduce a learning-based approach to further reduce traces of field-generator components from X-ray images to improve visualization and image guidance.
Methods
An adversarial decomposition network was trained to separate the residual FG components (including fiducial points introduced for pose estimation), from the X-ray images. The main novelty of our approach lies in the proposed data synthesis method, which combines existing 2D patient chest X-ray and FG X-ray images to generate 20,000 synthetic images, along with ground truth (images without the FG) to effectively train the network.
Results
For 30 real images of a torso phantom, our enhanced X-ray image after image decomposition obtained an average local PSNR of 35.04 and local SSIM of 0.97, whereas the unenhanced X-ray images averaged a local PSNR of 31.16 and local SSIM of 0.96.
Conclusion
In this study, we proposed an X-ray image decomposition method to enhance X-ray image for magnetic navigation by removing FG-induced artifacts, using a generative adversarial network. Experiments on both synthetic and real phantom data demonstrated the efficacy of our method.
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Acknowledgements
We acknowledge the generous hardware support provided by NVIDIA Inc.
Funding
This work was supported by INOVAIT (2021-1102 Western University).
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Uditha Jarayathne and Utsav Pardasani are employed by Northern Digital Inc. Wenyao Xia, SHuwei Xing, Elvis Chen, and Terry Peters declare that they have no conflict of interest.
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Xia, W., Xing, S., Jarayathne, U. et al. X-ray image decomposition for improved magnetic navigation. Int J CARS 18, 1225–1233 (2023). https://doi.org/10.1007/s11548-023-02958-3
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DOI: https://doi.org/10.1007/s11548-023-02958-3