Evaluation of optimization methods for intensity-based 2D-3D registration in x-ray guided interventions

\u3cp\u3eThe advantage of 2D-3D image registration methods versus direct image-to-patient registration, is that these methods generally do not require user interaction (such as manual annotations), additional machinery or additional acquisition of 3D data. A variety of intensity-based similarity measures has been proposed and evaluated for different applications. These studies showed that the registration accuracy and capture range are influenced by the choice of similarity measure. However, the influence of the optimization method on intensity-based 2D-3D image registration has not been investigated. We have compared the registration performance of seven optimization methods in combination with three similarity measures: gradient difference, gradient correlation, and pattern intensity. Optimization methods included in this study were: regular step gradient descent, Nelder-Mead, Powell-Brent, Quasi-Newton, nonlinear conjugate gradient, simultaneous perturbation stochastic approximation, and evolution strategy. Registration experiments were performed on multiple patient data sets that were obtained during cerebral interventions. Various component combinations were evaluated on registration accuracy, capture range, and registration time. The results showed that for the same similarity measure, different registration accuracies and capture ranges were obtained when different optimization methods were used. For gradient difference, largest capture ranges were obtained with Powell-Brent and simultaneous perturbation stochastic approximation. Gradient correlation and pattern intensity had the largest capture ranges in combination with Powell-Brent, Nelder-Mead, nonlinear conjugate gradient, and Quasi-Newton. Average registration time, expressed in the number of DRRs required for convergence, was the lowest for Powell-Brent. Based on these results, we conclude that Powell-Brent is a reliable optimization method for intensity-based 2D-3D registration of x-ray images to CBCT, regardless of the similarity measure used.\u3c/p\u3

A finite element method to predict adverse events in intracranial stenting using microstents: in vitro verification and patient specific case study

Clinical studies have demonstrated the efficacy of stent supported coiling for intra-cranial aneurysm treatment. Despite encouraging outcomes, some matters are yet to be addressed. In particular closed stent designs are influenced by the delivery technique and may suffer from under-expansion, with the typical effect of "hugging" the inner curvature of the vessel which seems related to adverse events. In this study we propose a novel finite element (FE) environment to study potential failure able to reproduce the microcatheter "pull-back" delivery technique. We first verified our procedure with published in vitro data and then replicated the intervention on one patient treated with a 4.5 x 22 mm Enterprise microstent (Codman Neurovascular; Raynham MA, USA). Results showed good agreement with the in vitro test, catching both size and location of the malapposed area. A simulation of a 28 mm stent in the same geometry highlighted the impact of the delivery technique, which leads to larger area of malapposition. The patient specific simulation matched the global stent configuration and zones prone to malapposition shown on the clinical images with difference in tortuosity between actual and virtual treatment around 2.3%. We conclude that the presented FE strategy provides an accurate description of the stent mechanics and, after further in vivo validation and optimization, will be a tool to aid clinicians to anticipate the acute procedural outcome avoiding poor initial results