A Radiolucent Electromagnetic Tracking System for Use with Intraoperative X-ray Imaging

X-ray image decomposition for improved magnetic navigation

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.

HEX: a safe research framework for hybrid EMT X-ray navigation

PURPOSE

Navigating with continuous X-ray provides visual guidance, but exposes both surgeon and patient to ionizing radiation, which is associated with serious health risks. Interleaving fluoro snapshots with electromagnetic tracking (EMT) potentially minimizes radiation.

METHODS

We propose hybrid EMT + X-ray (HEX), a research framework for navigation with an emphasis on safe experimentation. HEX is based on several hardware and software components that are orchestrated to allow for safe and efficient data acquisition.

RESULTS

In our study, hybrid navigation reduces radiation by [Formula: see text] with cubic, and by [Formula: see text] with linear error compensation while achieving submillimeter accuracy. Training points for compensation can be reduced by half while keeping a similar accuracy-radiation trade-off.

CONCLUSION

The HEX framework allows to safely and efficiently evaluate the hybrid navigation approach in simulated procedures. Complementing intraoperative X-ray with EMT significantly reduces radiation in the OR, increasing the safety of patients and surgeons.

Current trends in three-dimensional visualization and real-time navigation as well as robot-assisted technologies in hepatobiliary surgery

With the continuous development of digital medicine, minimally invasive precision and safety have become the primary development trends in hepatobiliary surgery. Due to the specificity and complexity of hepatobiliary surgery, traditional preoperative imaging techniques such as computed tomography and magnetic resonance imaging cannot meet the need for identification of fine anatomical regions. Imaging-based three-dimensional (3D) reconstruction, virtual simulation of surgery and 3D printing optimize the surgical plan through preoperative assessment, improving the controllability and safety of intraoperative operations, and in difficult-to-reach areas of the posterior and superior liver, assistive robots reproduce the surgeon's natural movements with stable cameras, reducing natural vibrations. Electromagnetic navigation in abdominal surgery solves the problem of conventional surgery still relying on direct visual observation or preoperative image assessment. We summarize and compare these recent trends in digital medical solutions for the future development and refinement of digital medicine in hepatobiliary surgery.