Integration of model-based weighting into an ICP variant to account for measurement errors in intra-operative A-Mode ultrasound-based registration

Vector field analysis for surface registration in computer-assisted ENT surgery

BACKGROUND

Manual paired-point registration for navigated ENT-surgery is prone to human errors; automatic surface registration is often caught in local minima.

METHODS

Anatomical features of the human occiput are integrated into an algorithm for surface registration. A vector force field is defined between the patient and operating room datasets; registration is facilitated through gradient-based vector field analysis optimization of an energy function. The method is validated exemplarily on patient surface data provided by a mechanically positioned A-mode ultrasound sensor.

RESULTS

Successful registrations were achieved within the entire parameter space, as well as from positions of local minima that were found by the Gaussian fields algorithm for surface registration. Sub-millimetric registration error was measured in clinically relevant anatomical areas on the anterior skull and within the generally accepted margin of 1.5 mm for the entire head.

CONCLUSION

The satisfactory behavior of this approach potentially suggests a wider clinical integration.

Bayesian characterization of uncertainty in intra-subject non-rigid registration

In settings where high-level inferences are made based on registered image data, the registration uncertainty can contain important information. In this article, we propose a Bayesian non-rigid registration framework where conventional dissimilarity and regularization energies can be included in the likelihood and the prior distribution on deformations respectively through the use of Boltzmann's distribution. The posterior distribution is characterized using Markov Chain Monte Carlo (MCMC) methods with the effect of the Boltzmann temperature hyper-parameters marginalized under broad uninformative hyper-prior distributions. The MCMC chain permits estimation of the most likely deformation as well as the associated uncertainty. On synthetic examples, we demonstrate the ability of the method to identify the maximum a posteriori estimate and the associated posterior uncertainty, and demonstrate that the posterior distribution can be non-Gaussian. Additionally, results from registering clinical data acquired during neurosurgery for resection of brain tumor are provided; we compare the method to single transformation results from a deterministic optimizer and introduce methods that summarize the high-dimensional uncertainty. At the site of resection, the registration uncertainty increases and the marginal distribution on deformations is shown to be multi-modal.