Leveraging spatial uncertainty for online error compensation in EMT

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.

CycleGAN for interpretable online EMT compensation

PURPOSE

Electromagnetic tracking (EMT) can partially replace X-ray guidance in minimally invasive procedures, reducing radiation in the OR. However, in this hybrid setting, EMT is disturbed by metallic distortion caused by the X-ray device. We plan to make hybrid navigation clinical reality to reduce radiation exposure for patients and surgeons, by compensating EMT error.

METHODS

Our online compensation strategy exploits cycle-consistent generative adversarial neural networks (CycleGAN). Positions are translated from various bedside environments to their bench equivalents, by adjusting their z-component. Domain-translated points are fine-tuned on the x-y plane to reduce error in the bench domain. We evaluate our compensation approach in a phantom experiment.

RESULTS

Since the domain-translation approach maps distorted points to their laboratory equivalents, predictions are consistent among different C-arm environments. Error is successfully reduced in all evaluation environments. Our qualitative phantom experiment demonstrates that our approach generalizes well to an unseen C-arm environment.

CONCLUSION

Adversarial, cycle-consistent training is an explicable, consistent and thus interpretable approach for online error compensation. Qualitative assessment of EMT error compensation gives a glimpse to the potential of our method for rotational error compensation.