Sensitivity analysis of fiducial placement on transorbital target registration error

Quantitative Augmented Reality-Assisted Free-Hand Orthognathic Surgery Using Electromagnetic Tracking and Skin-Attached Dynamic Reference

The purpose of this study was to develop a quantitative AR-assisted free-hand orthognathic surgery method using electromagnetic (EM) tracking and skin-attached dynamic reference. The authors proposed a novel, simplified, and convenient workflow for augmented reality (AR)-assisted orthognathic surgery based on optical marker-less tracking, a comfortable display, and a non-invasive, skin-attached dynamic reference frame. The 2 registrations between the physical (EM tracking) and CT image spaces and between the physical and AR camera spaces, essential processes in AR-assisted surgery, were pre-operatively performed using the registration body complex and 3D depth camera. The intraoperative model of the maxillary bone segment (MBS) was superimposed on the real patient image with the simulated goal model on a flat-panel display, and the MBS was freely handled for repositioning with respect to the skin-attached dynamic reference tool (SRT) with quantitative visualization of landmarks of interest using only EM tracking. To evaluate the accuracy of AR-assisted Le Fort I surgery, the MBS of the phantom was simulated and repositioned by 6 translational and three rotational movements. The mean absolute deviations (MADs) between the simulation and post-operative positions of MBS landmarks by the SRT were 0.20, 0.34, 0.29, and 0.55 mm in x- (left lateral, right lateral), y- (setback, advance), and z- (impaction, elongation) directions, and RMS, respectively, while those by the BRT were 0.23, 0.37, 0.30, and 0.60 mm. There were no significant differences between the translation and rotation surgeries or among surgeries in the x-, y-, and z-axes for the SRT. The MADs in the x-, y-, and z-axes exhibited no significant differences between the SRT and BRT. The developed method showed high accuracy and reliability in free-hand orthognathic surgery using EM tracking and skin-attached dynamic reference.

Optimization Model for the Distribution of Fiducial Markers in Liver Intervention

The distribution of fiducial markers is one of the main factors affected the accuracy of optical navigation system. However, many studies have been focused on improving the fiducial registration accuracy or the target registration accuracy, but few solutions involve optimization model for the distribution of fiducial markers. In this paper, we propose an optimization model for the distribution of fiducial markers to improve the optical navigation accuracy. The strategy of optimization model is reducing the distribution from three dimensional to two dimensional to obtain the 2D optimal distribution by using optimization algorithm in terms of the marker number and the expectation equation of target registration error (TRE), and then extend the 2D optimal distribution in two dimensional to three dimensional to calculate the optimal distribution according to the distance parameter and the expectation equation of TRE. The results of the experiments show that the averaged TRE for the human phantom is approximately 1.00 mm by applying the proposed optimization model, and the averaged TRE for the abdominal phantom is 0.59 mm. The experimental results of liver simulator model and ex-vivo porcine liver model show that the proposed optimization model can be effectively applied in liver intervention.

Selection of Fiducial Locations and Performance Metrics for Point-Based Rigid-Body Registration

A method is described to select the location and number of fiducials used in point-based, rigid-body registration of two coordinate frames. Two indices are introduced which are used to search for the optimum configuration of fiducials. They can be used to quickly evaluate a large number of configurations because no actual registration is involved in their calculation. Furthermore, configurations yielding small values of the indices correlate well with configurations which result in optimum registrations. Three registration performance metrics are discussed, and it is shown that optimization of different metrics leads to different selection of fiducial configurations. If an optimized configuration is selected as a starting configuration of N fiducials, the addition of extra fiducials does not significantly improve the registration in most cases. This work is based on 3D data acquired with three different instruments, each having different noise and bias characteristics.

A region-based anatomical landmark configuration for sinus surgery using image guided navigation system: a phantom-study

PURPOSE

To evaluate the current beliefs about the ways to reduce target registration error (TRE) values in image guided Sinus surgery by rearranging the fiducial configuration, and investigating the best configurations for various surgical fields in a phantom study.

METHODS

A new CT-compatible skull phantom consisting of implanted targets was designed to enable direct measurement of TRE in four fields of sinus surgery, Frontal, Ethmoid, Sphenoid and Maxillary. The effects of different landmark configurations on TRE values, measured by the Parsiss-IV navigation system were investigated to find the best landmark arrangement for each region, and compared to the TRE prediction formula to assess the clinically accepted landmark selection approaches based on this formula.

RESULTS

It was shown that smaller values of TRE could be attained by arranging the center of the fiducials to be more focused on the surgery target. The addition of more fiducials and keeping non-linear arrangement of landmark would not necessarily decrease the TRE value.

CONCLUSION

Optimizing the landmark configuration is important for increasing the localization accuracy in image guided sinus surgery. The common beliefs accepted in the clinical community about the ways to reduce the TRE are very general and should be adapted to specific field of image guided surgery.

Fiducial optimization for minimal target registration error in image-guided neurosurgery

This paper presents new methods for the optimal selection of anatomical landmarks and optimal placement of fiducial markers in image-guided neurosurgery. These methods allow the surgeon to optimally plan fiducial marker locations on routine diagnostic images before preoperative imaging and to intraoperatively select the set of fiducial markers and anatomical landmarks that minimize the expected target registration error (TRE). The optimization relies on a novel empirical simulation-based TRE estimation method built on actual fiducial localization error (FLE) data. Our methods take the guesswork out of the registration process and can reduce localization error without additional imaging and hardware. Our clinical experiments on five patients who underwent brain surgery with a navigation system show that optimizing one marker location and the anatomical landmarks configuration reduced the TRE. The average TRE values using the usual fiducials setup and using the suggested method were 4.7 mm and 3.2 mm, respectively. We observed a maximum improvement of 4 mm. Reducing the target registration error has the potential to support safer and more accurate minimally invasive neurosurgical procedures.

Classification and Analysis of the Errors in Neuronavigation

There are many different types of errors in neuronavigation, and the reasons and results of these errors are complex. For a neurosurgeon using the neuronavigation system, it is important to have a clear understanding of when an error may occur, what the magnitude of it is, and how to avoid it or reduce its influence on the final application accuracy. In this article, we classify all the errors into 2 groups according to the working principle of neuronavigation systems. The first group contains the errors caused by the differences between the anatomic structures in the images and that of the real patient, and the second group contains the errors occurring in transforming the position of surgical tools from the patient space to the image space. Each group is further divided into 2 subgroups. We discuss 16 types of errors and classify each of them into one of the subgroups. The classification and analysis of these errors should help neurosurgeons understand the power and limits of neuronavigation systems and use them more properly.

Optimal Fiducial Configuration in Image-Guided Neurosurgery Using a Genetic Algorithm

Image-guided neurosurgery allows surgeons to navigate and localize lesion through the patient’s cranium with a 3D image guidance. The model of the head is reconstructed using preoperative computed tomography or magnetic resonance images and real and virtual spaces are aligned by means of fiducial markers placed on the patient. In this paper, a new method for the optimal placement of the fiducial markers in order to reduce misalignment is presented. Using routine diagnostic images, a customized 3D model of the patient’s cranium is reconstructed. A genetic algorithm calculates optimal positions of the marker in order to minimize the target registration error. The fiducial set is shown to the surgeons on the 3D model to help him/her in placement of them.

Distribution templates of the fiducial points in image-guided neurosurgery

BACKGROUND

Point-pair registration is widely used in an image-guided neurosurgery system. Poor distribution of the fiducial points leads to an increase in the target registration error (TRE).

OBJECTIVE

This study aimed to provide templates consisting of optimized positioning of the fiducial points to reduce the TRE in image-guided neurosurgery.

METHODS

We divided the head into 6 regions and provided distribution templates of the fiducial points for each of them. A variable termed TREM(r) was used to express the approximate expected square of the TRE at the target point with a specified distribution of fiducial points. We randomly selected 85 patients from 5 hospitals who underwent image-guided neurosurgery and compared the TREM(r) of the real fiducial points with that of the templates.

RESULTS

We grouped the patients by hospitals and regions. The mean TREM(r)s of the templates were much smaller than those of the real fiducial points. In each group, the range of the TREM(r) values of the templates was much smaller than that of the real fiducial points.

CONCLUSION

This study provides an easy method to implement a good distribution of the fiducial points to help reduce TRE in image-guided neurosurgery. The templates are simple and exact and can be easily integrated into current workflow.