Leveraging spatial uncertainty for online error compensation in EMT.

PURPOSE: Electromagnetic tracking (EMT) can potentially complement fluoroscopic navigation, reducing radiation exposure in a hybrid setting. Due to the susceptibility to external distortions, systematic error in EMT needs to be compensated algorithmically. Compensation algorithms for EMT in guidewire procedures are only practical in an online setting. METHODS: We collect positional data and train a symmetric artificial neural network (ANN) architecture for compensating navigation error. The results are evaluated in both online and offline scenarios and are compared to polynomial fits. We assess spatial uncertainty of the compensation proposed by the ANN. Simulations based on real data show how this uncertainty measure can be utilized to improve accuracy and limit radiation exposure in hybrid navigation. RESULTS: ANNs compensate unseen distortions by more than 70%, outperforming polynomial regression. Working on known distortions, ANNs outperform polynomials as well. We empirically demonstrate a linear relationship between tracking accuracy and model uncertainty. The effectiveness of hybrid tracking is shown in a simulation experiment. CONCLUSION: ANNs are suitable for EMT error compensation and can generalize across unseen distortions. Model uncertainty needs to be assessed when spatial error compensation algorithms are developed, so that training data collection can be optimized. Finally, we find that error compensation in EMT reduces the need for X-ray images in hybrid navigation.

High-precision evaluation of electromagnetic tracking.

PURPOSE: Navigation in high-precision minimally invasive surgery (HP-MIS) demands high tracking accuracy in the absence of line of sight (LOS). Currently, no tracking technology can satisfy this requirement. Electromagnetic tracking (EMT) is the best tracking paradigm in the absence of LOS despite limited accuracy and robustness. Novel evaluation protocols are needed to ensure high-precision and robust EMT for navigation in HP-MIS. METHODS: We introduce a novel protocol for EMT measurement evaluation featuring a high-accuracy phantom based on LEGO[Formula: see text], which is calibrated by a coordinate measuring machine to ensure accuracy. Our protocol includes relative sequential positions and an uncertainty estimation of positioning. We show effects on distortion compensation using a learned interpolation model. RESULTS: Our high-precision protocol clarifies properties of errors and uncertainties of EMT for high-precision use cases. For EMT errors reaching clinically relevant 0.2 mm, our design is 5-10 times more accurate than previous protocols with 95% confidence margins of 0.02 mm. This high-precision protocol ensures the performance improvement in compensated EMT by 0.05 mm. CONCLUSION: Our protocol improves the reliability of EMT evaluations because of significantly lower protocol-inherent uncertainties. To reduce patient risk in HP-MIS and to evaluate magnetic field distortion compensation, more high-accuracy protocols such as the one proposed here are required.

Influence of patient-specific anatomy on medical computed tomography and risk evaluation of minimally invasive surgery at the otobasis.

PURPOSE: With the increasing use of new minimally invasive approaches in temporal bone surgery, the need arises for evaluation of the risk of injury to sensitive anatomical structures. The factors that influence the measurement uncertainty (variation in representation of position and shape of anatomical structures) of imaging are of relevance. We investigate the effect of patients' anatomy on the measurement uncertainty of medical CT. METHODS: Six formalin-fixed temporal bones were used, fiducial markers were bone-implanted, and 20 CT scans of each temporal bone were generated. Surgically threatened anatomical structures of importance were defined. Manual segmentation was performed to create 3D surface models, and different Gaussian filters were applied. Analysis points were established along the border of the superior semicircular canal to determine the deviation between the 3D images of the labyrinth. The standard uncertainty was calculated, and one-way analysis of variance was performed (significance level = 5%) to evaluate the effect of certain factors (patient, side, Gaussian filter) on the measurement uncertainty. RESULTS: The influence of patient-specific anatomy on the measurement uncertainty of medical CT (p = 0.049) was demonstrated for the first time. The applied Gaussian filter (p = 0.622) and the patient's side (p = 0.341) showed no significant effect. CONCLUSION: The applied method and the results of the statistical analysis suggest that the patient's individual anatomical conditions affect the measurement uncertainty of medical CT. Thus, the patient's anatomy must be considered as an important influencing factor during risk evaluation concerning minimally invasive and image-guided surgery.