X-ray image decomposition for improved magnetic navigation
Published in IJCARS, 2023
Recommended citation: Xia, W., Xing, S., Jarayathne, U., Pardasani, U., Peters, TM., and Chen ECS.(2023). "X-rayimagedecompositionforimprovedmagneticnavigation"; in International Journal of Computer Assisted Radiology and Surgery, 18(7) pp. 1225--1233 https://doi.org/10.1007/s11548-023-02958-3
Purpose: Existing field generators (FGs) for magnetic tracking causes evere 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-rayimages 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 agenerative adversarial network. Experiments on both synthetic and real phantom data demonstrated the efficacy of our method.