The distribution of target registration error in rigid-body point-based registration

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.

Spatial Confidence Regions for Quantifying and Visualizing Registration Uncertainty

For image registration to be applicable in a clinical setting, it is important to know the degree of uncertainty in the returned point-correspondences. In this paper, we propose a data-driven method that allows one to visualize and quantify the registration uncertainty through spatially adaptive confidence regions. The method applies to various parametric deformation models and to any choice of the similarity criterion. We adopt the B-spline model and the negative sum of squared differences for concreteness. At the heart of the proposed method is a novel shrinkage-based estimate of the distribution on deformation parameters. We present some empirical evaluations of the method in 2-D using images of the lung and liver, and the method generalizes to 3-D.

An open environment CT-US fusion for tissue segmentation during interventional guidance

Therapeutic ultrasound (US) can be noninvasively focused to activate drugs, ablate tumors and deliver drugs beyond the blood brain barrier. However, well-controlled guidance of US therapy requires fusion with a navigational modality, such as magnetic resonance imaging (MRI) or X-ray computed tomography (CT). Here, we developed and validated tissue characterization using a fusion between US and CT. The performance of the CT/US fusion was quantified by the calibration error, target registration error and fiducial registration error. Met-1 tumors in the fat pads of 12 female FVB mice provided a model of developing breast cancer with which to evaluate CT-based tissue segmentation. Hounsfield units (HU) within the tumor and surrounding fat pad were quantified, validated with histology and segmented for parametric analysis (fat: −300 to 0 HU, protein-rich: 1 to 300 HU, and bone: HU>300). Our open source CT/US fusion system differentiated soft tissue, bone and fat with a spatial accuracy of ∼1 mm. Region of interest (ROI) analysis of the tumor and surrounding fat pad using a 1 mm² ROI resulted in mean HU of 68±44 within the tumor and −97±52 within the fat pad adjacent to the tumor (p<0.005). The tumor area measured by CT and histology was correlated (r² = 0.92), while the area designated as fat decreased with increasing tumor size (r² = 0.51). Analysis of CT and histology images of the tumor and surrounding fat pad revealed an average percentage of fat of 65.3% vs. 75.2%, 36.5% vs. 48.4%, and 31.6% vs. 38.5% for tumors <75 mm³, 75–150 mm³ and >150 mm³, respectively. Further, CT mapped bone-soft tissue interfaces near the acoustic beam during real-time imaging. Combined CT/US is a feasible method for guiding interventions by tracking the acoustic focus within a pre-acquired CT image volume and characterizing tissues proximal to and surrounding the acoustic focus.

Multimodal image registration for preoperative planning and image-guided neurosurgical procedures

Image registration is the process of transforming images acquired at different time points, or with different imaging modalities, into the same coordinate system. It is an essential part of any neurosurgical planning and navigation system because it facilitates combining images with important complementary, structural, and functional information to improve the information based on which a surgeon makes critical decisions. Brigham and Women's Hospital (BWH) has been one of the pioneers in developing intraoperative registration methods for aligning preoperative and intraoperative images of the brain. This article presents an overview of intraoperative registration and highlights some recent developments at BWH.

Properties of the target registration error for surface matching in neuronavigation

OBJECTIVE

Surface matching is a relatively new method of spatial registration in neuronavigation. Compared to the traditional point matching method, surface matching does not use fiducial markers that must be fixed to the surface of the head before image scanning, and therefore does not require an image acquisition specifically dedicated for navigation purposes. However, surface matching is not widely used clinically, mainly because there is still insufficient knowledge about its application accuracy. This study aimed to explore the properties of the Target Registration Error (TRE) of surface matching in neuronavigation.

MATERIALS AND METHODS

The surface matching process was simulated in the image space of a neuronavigation system so that the TRE could be calculated at any point in that space. For each registration, two point clouds were generated to represent the surface extracted from preoperative images (PC(image)) and the surface obtained intraoperatively by laser scanning (PC(laser)). The properties of the TRE were studied by performing multiple registrations with PC(laser) point clouds at different positions and generated by adding different types of error.

RESULTS

For each registration, the TRE had a minimal value at a point in the image space, and the iso-valued surface of the TRE was approximately ellipsoid with smaller TRE on the inner surfaces. The position of the point with minimal TRE and the shape of the iso-valued surface were highly random across different registrations, and the surface registration error between the two point clouds was irrelevant to the TRE at a specific point. The overall TRE tended to increase with the increase in errors in PC(laser), and a larger PC(laser) made it less sensitive to these errors. With the introduction of errors in PC(laser), the points with minimal TRE tended to be concentrated in the anterior and inferior part of the head.

CONCLUSION

The results indicate that the alignment between the two surfaces could not provide reliable information about the registration accuracy at an arbitrary target point. However, according to the spatial distribution of the target registration error of a single registration, enough application accuracy could be guaranteed by proper visual verification after registration. In addition, surface matching tends to achieve high accuracy in the inferior and anterior part of the head, and a relatively large scanning area is preferable.

Pulmonary kinematics from image data: a review

The effects of certain lung pathologies include alterations in lung physiology negatively affecting pulmonary compliance. Current approaches to diagnosis and treatment assessment of lung disease commonly rely on pulmonary function testing. Such testing is limited to global measures of lung function, neglecting regional measurements, which are critical for early diagnosis and localization of disease. Increased accessibility to medical image acquisition strategies with high spatiotemporal resolution coupled with the development of sophisticated intensity-based and geometric registration techniques has resulted in the recent exploration of modeling pulmonary motion for calculating local measures of deformation. In this review, the authors provide a broad overview of such research efforts for the estimation of pulmonary deformation. This includes discussion of various techniques, current trends in validation approaches, and the public availability of software and data resources.

General approach to first-order error prediction in rigid point registration

A general approach to the first-order analysis of error in rigid point registration is presented that accommodates fiducial localization error (FLE) that may be inhomogeneous (varying from point to point) and anisotropic (varying with direction) and also accommodates arbitrary weighting that may also be inhomogeneous and anisotropic. Covariances are derived for target registration error (TRE) and for weighted fiducial registration error (FRE) in terms of covariances of FLE, culminating in a simple implementation that encompasses all combinations of weightings and anisotropy. Furthermore, it is shown that for ideal weighting, in which the weighting matrix for each fiducial equals the inverse of the square root of the cross covariance of its two-space FLE, fluctuations of FRE and TRE are mutually independent. These results are validated by comparison with previously published expressions and by simulation. Furthermore, simulations for randomly generated fiducial positions and FLEs are presented that show that correlation is negligible (correlation coefficient < 0.1) in the exact case for both ideal and uniform weighting (i.e., no weighting), the latter of which is employed in commercial surgical guidance systems. From these results we conclude that for these weighting schemes, while valid expressions exist relating the covariance of FRE to the covariance of TRE, there are no measures of the goodness of fit of the fiducials for a given registration that give to first order any information about the fluctuation of TRE from its expected value and none that give useful information in the exact case. Therefore, as estimators of registration accuracy, such measures should be approached with extreme caution both by the purveyors of guidance systems and by the practitioners who use them.

Understanding the effect of bias in fiducial localization error on point-based rigid-body registration

Image registration is a single point of failure in the image-guided computer-assisted surgery. Registration is primarily used to align and fuse the data sets taken from patient's anatomy before and during surgeries. Point-based rigid-body registration is usually performed by identifying corresponding fiducials (either natural landmarks or implanted ones) in the data sets. Since the localization of fiducials is imprecise and is generally perturbed by random noise, the performed registration is imperfect and has some error. Previous work has extensively analyzed the behavior of this error when the fiducial localization error has zero-mean over the entire set of fiducials. However, if noise has a nonzero-mean or a bias, no formulation yet exists to determine the effect of noise on the overall registration accuracy. In this work, we derive novel formulations that relate the bias in the localized fiducials to the accuracy of the performed registration. We analytically and numerically demonstrate that by eliminating the estimated bias from the measured fiducial locations, one can effectively increase the accuracy of the performed registration.

The role of registration in accurate surgical guidance

Registration is presented as the central issue of surgical guidance. The focus is on the accuracy of approaches employed today, all of which use pre-operative images to guide surgery on rigid anatomy. The three most well-established approaches to guidance, namely the stereotactic frame, point fiducials, and surface matching, are examined in detail, together with two new approaches based on microstereotactic frames. It is shown that each method relies on the registration of points in the image to corresponding points in the operating room, and therefore that the error patterns associated with point registration are similar for all of them. Three types of registration error, namely fiducial localization error (FLE), fiducial registration error (FRE), and target registration error (TRE), are highlighted, as well as two additional guidance errors, namely target localization error and total targeting error, the latter of which is the overall error of the guidance system. Statistical relationships between TRE and FLE, between FRE and FLE, and between TRE, TLE, and TTE are given. Finally some myths concerning fiducial registration are highlighted.

Registration between ultrasound and fluoroscopy or CT in prostate brachytherapy

PURPOSE

In prostate brachytherapy, transrectal ultrasound (TRUS) is used to visualize the anatomy, while implanted seeds can be visualized by fluoroscopy. Intraoperative dosimetry optimization is possible using a combination of TRUS and fluoroscopy, but requires localization of the fluoroscopy-derived seed cloud, relative to the anatomy as seen on TRUS. The authors propose to develop a method of registration of TRUS images and the implants reconstructed from fluoroscopy.

METHODS

A phantom was implanted with 48 seeds then imaged with TRUS and CT. Seeds were reconstructed from CT yielding a cloud of seeds. Fiducial-based ground-truth registration was established between the TRUS and CT. TRUS images are filtered, compounded, and registered to the reconstructed implants by using an intensity-based metric. The authors evaluated a volume-to-volume and point-to-volume registration scheme. In total, seven TRUS filtering techniques and three image similarity metrics were analyzed. The method was also tested on human subject data captured from a brachytherapy procedure.

RESULTS

For volume-to-volume registration, noise reduction filter and normalized correlation metrics yielded the best result: An average of 0.54 +/- 0.11 mm seed localization error relative to ground truth. For point-to-volume registration, noise reduction combined with beam profile filter and mean squares metrics yielded the best result: An average of 0.38 +/- 0.19 mm seed localization error relative to the ground truth. In human patient data, C-arm fluoroscopy images showed 81 radioactive seeds implanted inside the prostate. A qualitative analysis showed clinically correct agreement between the seeds visible in TRUS and reconstructed from intraoperative fluoroscopy imaging. The measured registration error compared to the manually selected seed locations by the clinician was 2.86 +/- 1.26 mm.

CONCLUSIONS

Fully automated registration between TRUS and the reconstructed seeds performed well in ground-truth phantom experiments and qualitative observation showed adequate performance on early clinical patient data.

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.

Percutaneous cochlear implant drilling via customized frames: an in vitro study

OBJECTIVE

Percutaneous cochlear implantation (PCI) surgery uses patient-specific customized microstereotactic frames to achieve a single drill-pass from the lateral skull to the cochlea, avoiding vital anatomy. We demonstrate the use of a specific microstereotactic frame, called a "microtable," to perform PCI surgery on cadaveric temporal bone specimens.

STUDY DESIGN

Feasibility study using cadaveric temporal bones.

SUBJECTS AND METHODS

PCI drilling was performed on six cadaveric temporal bone specimens. The main steps involved were 1) placing three bone-implanted markers surrounding the ear, 2) obtaining a CT scan, 3) planning a safe surgical path to the cochlea avoiding vital anatomy, 4) constructing a microstereotactic frame to constrain the drill to the planned path, and 5) affixing the frame to the markers and using it to drill to the cochlea. The specimens were CT scanned after drilling to show the achieved path. Deviation of the drilled path from the desired path was computed, and the closest distance of the mid-axis of the drilled path from critical structures was measured.

RESULTS

In all six specimens, we drilled successfully to the cochlea, preserving the facial nerve and ossicles. In four of six specimens, the chorda tympani was preserved, and in two of six specimens, it was sacrificed. The mean +/- standard deviation error at the target was found to be 0.31 +/- 0.10 mm. The closest distances of the mid-axis of the drilled path to structures were 1.28 +/- 0.17 mm to the facial nerve, 1.31 +/- 0.36 mm to the chorda tympani, and 1.59 +/- 0.43 mm to the ossicles.

CONCLUSION

In a cadaveric model, PCI drilling is safe and effective.

Target tracking errors for 5D and 6D spatial measurement systems

In recent years, magnetic tracking systems, whose fundamental unit of measurement is a 5D transformation (three translational and two rotational degrees-of-freedom), have become much more popular. Two 5D sensors can be combined to obtain a 6D transformation similar to the ones provided by the point-based registration in optical tracking. However, estimates of the tool tip uncertainty, which we have called the target tracking error (TTE) since no registration is explicitly performed, are not available in the same manner as their optical counterpart. If the systematic bias error can be corrected and estimates of the 5D or 6D fiducial localizer error (FLE) are provided in the form of zero mean normally distributed random variables in [Formula: see text] and [Formula: see text], respectively, then the TTE can be modeled. In this paper, the required expressions that model the TTE as a function of the systematic bias, FLE and target location are derived and then validated using Monte Carlo simulations. We also show that the first order approximation is sufficient beyond the range of errors typically observed during an image-guided surgery (IGS) procedure. Applications of the models are described for a minimally invasive intracardiac surgical guidance system and needle-based therapy systems. Together with the target registration error (TRE) statistical models for point-based registration, the models presented in this article provide the basic framework for estimating the total system measurement uncertainty for an IGS system. Future work includes developing TRE models for commonly used registration methods that do not already have them.

Estimation of optimal fiducial target registration error in the presence of heteroscedastic noise

We study the effect of point dependent (heteroscedastic) and identically distributed anisotropic fiducial localization noise on fiducial target registration error (TRE). We derive an analytic expression, based on the concept of mechanism spatial stiffness, for predicting TRE. The accuracy of the predicted TRE is compared to simulated values where the optimal registration transformation is computed using the heteroscedastic errors in variables algorithm. The predicted values are shown to be contained by the 95% confidence intervals of the root mean square TRE obtained from the simulations.

A process model for direct correlation between computed tomography and histopathology application in lung cancer

RATIONALE AND OBJECTIVES

Multimodal imaging techniques for capturing normal and diseased human anatomy and physiology are being developed to benefit patient clinical care, research, and education. In the past, the incorporation of histopathology into these multimodal datasets has been complicated by the large differences in image quality, content, and spatial association.

MATERIALS AND METHODS

We have developed a novel system, the large-scale image microtome array (LIMA), to bridge the gap between nonstructurally destructive and destructive imaging such that reliable registration between radiological data and histopathology can be achieved. Registration algorithms have been designed to align the multimodal datasets, which include computed tomography, computed micro-tomography, LIMA, and histopathology data to a common coordinate system.

RESULTS

The resulting volumetric dataset provides an abundance of valuable information relating to the tissue sample including density, anatomical structure, color, texture, and cellular information in three dimensions. An image processing pipeline has been established to register all the multimodal data to a common coordinate system.

CONCLUSION

In this study, we have chosen to use human lung cancer nodules as an example; however, the flexibility of the image acquisition and subsequent processing algorithms makes it applicable to any soft organ tissue. A novel process model has been established to generate cross registered multimodal datasets for the investigation of human lung cancer nodule content and associated image-based representation.

Demons deformable registration for CBCT-guided procedures in the head and neck: convergence and accuracy

PURPOSE

The accuracy and convergence behavior of a variant of the Demons deformable registration algorithm were investigated for use in cone-beam CT (CBCT)-guided procedures of the head and neck. Online use of deformable registration for guidance of therapeutic procedures such as image-guided surgery or radiation therapy places trade-offs on accuracy and computational expense. This work describes a convergence criterion for Demons registration developed to balance these demands; the accuracy of a multiscale Demons implementation using this convergence criterion is quantified in CBCT images of the head and neck.

METHODS

Using an open-source "symmetric" Demons registration algorithm, a convergence criterion based on the change in the deformation field between iterations was developed to advance among multiple levels of a multiscale image pyramid in a manner that optimized accuracy and computation time. The convergence criterion was optimized in cadaver studies involving CBCT images acquired using a surgical C-arm prototype modified for 3D intraoperative imaging. CBCT-to-CBCT registration was performed and accuracy was quantified in terms of the normalized cross-correlation (NCC) and target registration error (TRE). The accuracy and robustness of the algorithm were then tested in clinical CBCT images of ten patients undergoing radiation therapy of the head and neck.

RESULTS

The cadaver model allowed optimization of the convergence factor and initial measurements of registration accuracy: Demons registration exhibited TRE=(0.8+/-0.3) mm and NCC =0.99 in the cadaveric head compared to TRE=(2.6+/-1.0) mm and NCC=0.93 with rigid registration. Similarly for the patient data, Demons registration gave mean TRE=(1.6+/-0.9) mm compared to rigid registration TRE=(3.6+/-1.9) mm, suggesting registration accuracy at or near the voxel size of the patient images (1 x 1 x 2 mm3). The multiscale implementation based on optimal convergence criteria completed registration in 52 s for the cadaveric head and in an average time of 270 s for the larger FOV patient images.

CONCLUSIONS

Appropriate selection of convergence and multiscale parameters in Demons registration was shown to reduce computational expense without sacrificing registration performance. For intraoperative CBCT imaging with deformable registration, the ability to perform accurate registration within the stringent time requirements of the operating environment could offer a useful clinical tool allowing integration of preoperative information while accurately reflecting changes in the patient anatomy. Similarly for CBCT-guided radiation therapy, fast accurate deformable registration could further augment high-precision treatment strategies.

Distribution of fiducial registration error in rigid-body point-based registration

Rigid-body point-based registration is frequently used in computer assisted surgery to align corresponding points, or fiducials, in preoperative and intraoperative data. This alignment is mostly achieved by assuming the same homogeneous error distribution for all the points; however, due to the properties of the medical instruments used in measuring the point coordinates, the error distribution might be inhomogeneous and different for each point. In this paper, in an effort to understand the effect of error distribution in the localized points on the performed registration, we derive a closed-form solution relating the error distribution of each point with the performed registration accuracy. The solution uses maximum likelihood estimation to calculate the probability density function of registration error at each fiducial point. Extensive numerical simulations are performed to validated the proposed solution.

Intra- and inter-subject variability of high field fMRI digit maps in somatosensory area 3b of new world monkeys

This study evaluates the intra- and inter-subject variability of digit maps in area 3b of anesthetized squirrel monkeys. Maps were collected using high field blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI). BOLD responses to individual digit stimulations were mapped and their response properties (location, area of activation, % signal change, time to peak response) were compared within and across imaging sessions separated by up to 20 months. During single digit stimulation using a block design, the spatiotemporal response of the BOLD signal for individual runs within and across sessions and animals was well conserved, with a time to peak BOLD response of 20+/-4 s. The variability in the center of BOLD activation in area 3b was 0.41+/-0.24 mm (mean+/-SD) across individual 5-7 min runs within a scanning session and 0.55+/-0.15 mm across sessions. The average signal change across all animals, runs and sessions was 0.62+/-0.38%, and varied 32% within and 40% across sessions. In a comparison of the stability and reproducibility of the area of single digit activation obtained using three approaches, use of a fixed statistical threshold (P<10(-5)) yielded an average area of 4.8+/-3.5 mm(2) (mean+/-SD), adaptive statistical thresholding 1.32+/-1.259 mm(2) (mean+/-SD), and combined fixed statistical and adaptive BOLD signal amplitude 4.4+/-2.5 mm(2) (mean+/-SD) across image runs and sessions. The somatotopic organization was stable within animals across sessions, while across animals, there was some variation in overall activation pattern and inter-digit distances. These results confirm that BOLD activation maps of single digits in area 3b as characterized by activation center, signal amplitudes, and temporal profile are very stable. The activation sizes determined by various criteria are the most variable measure in this preparation, but adaptive statistical thresholding appears to yield the most stable and reproducible maps. This study serves as a baseline assessment of the limits imposed on the detection of plastic changes by experimental variations of the digit BOLD fMRI activation maps in normal animals, and as an indicator of the likely performance limits in human studies.

Four-dimensional targeting error analysis in image-guided radiotherapy

Image-guided therapy (IGT) involves acquisition and processing of biomedical images to actively guide medical interventions. The proliferation of IGT technologies has been particularly significant in image-guided radiotherapy (IGRT), as a way to increase the tumor targeting accuracy. When IGRT is applied to moving tumors, image guidance becomes challenging, as motion leads to increased uncertainty. Different strategies may be applied to mitigate the effects of motion: each technique is related to a different technological effort and complexity in treatment planning and delivery. The objective comparison of different motion mitigation strategies can be achieved by quantifying the residual uncertainties in tumor targeting, to be detected by means of IGRT technologies. Such quantification requires an extension of targeting error theory to a 4D space, where the 3D tumor trajectory as a function of time measured (4D Targeting Error, 4DTE). Accurate 4DTE analysis can be represented by a motion probability density function, describing the statistical fluctuations of tumor trajectory. We illustrate the application of 4DTE analysis through examples, including weekly variations in tumor trajectory as detected by 4DCT, respiratory gating via external surrogates and real-time tumor tracking.

Real-time estimation of FLE statistics for 3-D tracking with point-based registration

Target registration error (TRE) has become a widely accepted error metric in point-based registration since the error metric was introduced in the 1990s. It is particularly prominent in image-guided surgery (IGS) applications where point-based registration is used in both image registration and optical tracking. In point-based registration, the TRE is a function of the fiducial marker geometry, location of the target and the fiducial localizer error (FLE). While the first two items are easily obtained, the FLE is usually estimated using an a priori technique and applied without any knowledge of real-time information. However, if the FLE can be estimated in real-time, particularly as it pertains to optical tracking, then the TRE can be estimated more robustly. In this paper, a method is presented where the FLE statistics are estimated from the latest measurement of the fiducial registration error (FRE) statistics. The solution is obtained by solving a linear system of equations of the form Ax=b for each marker at each time frame where x are the six independent FLE covariance parameters and b are the six independent estimated FRE covariance parameters. The A matrix is only a function of the tool geometry and hence the inverse of the matrix can be computed a priori and used at each instant in which the FLE estimation is required, hence minimizing the level of computation at each frame. When using a good estimate of the FRE statistics, Monte Carlo simulations demonstrate that the root mean square of the FLE can be computed within a range of 70-90 microm. Robust estimation of the TRE for an optically tracked tool, using a good estimate of the FLE, will provide two enhancements in IGS. First, better patient to image registration will be obtained by using the TRE of the optical tool as a weighting factor of point-based registration used to map the patient to image space. Second, the directionality of the TRE can be relayed back to the surgeon giving the surgeon the option of changing their strategy in order to improve the overall system accuracy and, in turn, the quality of procedure.

Automatic image-to-world registration based on x-ray projections in cone-beam CT-guided interventions

Intraoperative imaging offers a means to account for morphological changes occurring during the procedure and resolve geometric uncertainties via integration with a surgical navigation system. Such integration requires registration of the image and world reference frames, conventionally a time consuming, error-prone manual process. This work presents a method of automatic image-to-world registration of intraoperative cone-beam computed tomography (CBCT) and an optical tracking system. Multimodality (MM) markers consisting of an infrared (IR) reflective sphere with a 2 mm tungsten sphere (BB) placed precisely at the center were designed to permit automatic detection in both the image and tracking (world) reference frames. Image localization is performed by intensity thresholding and pattern matching directly in 2D projections acquired in each CBCT scan, with 3D image coordinates computed using backprojection and accounting for C-arm geometric calibration. The IR tracking system localized MM markers in the world reference frame, and the image-to-world registration was computed by rigid point matching of image and tracker point sets. The accuracy and reproducibility of the automatic registration technique were compared to conventional (manual) registration using a variety of marker configurations suitable to neurosurgery (markers fixed to cranium) and head and neck surgery (markers suspended on a subcranial frame). The automatic technique exhibited subvoxel marker localization accuracy (< 0.8 mm) for all marker configurations. The fiducial registration error of the automatic technique was (0.35 +/-0.01) mm, compared to (0.64 +/- 0.07 mm) for the manual technique, indicating improved accuracy and reproducibility. The target registration error (TRE) averaged over all configurations was 1.14 mm for the automatic technique, compared to 1.29 mm for the manual in accuracy, although the difference was not statistically significant (p = 0.3). A statistically significant improvement in precision was observed-specifically, the standard deviation in TRE was 0.2 mm for the automatic technique versus 0.34 mm for the manual technique (p = 0.001). The projection-based automatic registration technique demonstrates accuracy and reproducibility equivalent or superior to the conventional manual technique for both neurosurgical and head and neck marker configurations. Use of this method with C-arm CBCT eliminates the burden of manual registration on surgical workflow by providing automatic registration of surgical tracking in 3D images within approximately 20 s of acquisition, with registration automatically updated with each CBCT scan. The automatic registration method is undergoing integration in ongoing clinical trials of intraoperative CBCT-guided head and neck surgery.

Distribution of target registration error for anisotropic and inhomogeneous fiducial localization error

In point-based rigid-body registration, target registration error (TRE) is an important measure of the accuracy of the performed registration. The registration's accuracy depends on the fiducial localization error (FLE) which, in turn, is due to the measurement errors in the points (fiducials) used to perform the registration. FLE may have different characteristics and distributions at each point of the registering data sets, and along each orthogonal axis. Previously, the distribution of TRE was estimated based on the assumption that FLE has an independent, identical, and isotropic or anisotropic distribution for each point in the registering data sets. In this article, we present a general solution based on the Maximum Likelihood (ML) algorithm that estimates the distribution of TRE for the cases where FLE has an independent, identical or inhomogeneous, isotropic or anisotropic, distribution at each point in the registering data sets, and when an algorithm is available that is capable of calculating the optimum registration to first order. Mathematically, we show that the proposed algorithm simplifies to the one proposed by Fitzpatrick and West when FLE has an independent, identical, and isotropic distribution in the registering data sets. Furthermore, we use numerical simulations to show that the proposed algorithm accurately estimates the distribution of TRE when FLE has an independent, inhomogeneous, and anisotropic distribution in the registering data sets.

Towards image guided robotic surgery: multi-arm tracking through hybrid localization

OBJECTIVE

Use of the robotic assisted surgery has been increasing in recent years, due both the continuous increase in the number of applications and the clinical benefits that surgical robots can provide. Currently robotic assisted surgery relies on endoscopic video for navigation, providing only surface visualization, thus limiting subsurface vision. To be able to visualize and identify subsurface information, techniques in image-guidance can be used. As part of designing an image guidance system, all arms of the robot need to be co-localized in a common coordinate system.

METHODS

In order to track multiple arms in a common coordinate space, intrinsic and extrinsic tracking methods can be used. First, the intrinsic tracking of the daVinci, specifically of the setup joints is analyzed. Because of the inadequacy of the setup joints for co-localization a hybrid tracking method is designed and implemented to mitigate the inaccuracy of the setup joints. Different both optical and magnetic tracking methods are examined for setup joint localization.

RESULTS

The hybrid localization method improved the localization accuracy of the setup joints. The inter-arm accuracy in hybrid localization was improved to 3.02 mm. This inter-arm error value was shown to be further reduced when the arms are co-registered, thus reducing common error.

Accuracy of image-guided surgical systems at the lateral skull base as clinically assessed using bone-anchored hearing aid posts as surgical targets

OBJECTIVE

Image-guided surgical (IGS) technology has been clinically available for more than a decade. To date, no acceptable standard exists for reporting the accuracy of IGS systems, especially for lateral skull base applications. We present a validation method that uses the post from bone-anchored hearing aid (BAHA) patients as a target. We then compare the accuracy of 2 IGS systems-one using laser skin-surface scanning (LSSS) and another using a noninvasive fiducial frame (FF) attached to patient via dental bite-block (DBB) for registration.

STUDY DESIGN

Prospective.

SETTING

Tertiary referral center.

PATIENTS

Six BAHA patients who had adequate dentition for creation of a DBB.

INTERVENTION(S)

For each patient, a dental impression was obtained, and a customized DBB was made to hold an FF. Temporal bone computed tomographic (CT) scans were obtained with the patient wearing the DBB, FF, and a customized marker on the BAHA post. In a mock operating room, CT scans were registered to operative anatomy using 2 methods: 1) LSSS and 2) FF.

MAIN OUTCOME MEASURE(S)

For each patient and each system, the position of the BAHA marker in the CT scan and in the mock operating room (using optical measurement technology) was compared, and the distances between them are reported to demonstrate accuracy.

RESULTS

Accuracy (mean +/- standard deviation) was 1.54 +/- 0.63 mm for DBB registration and 3.21 +/- 1.02 mm for LSSS registration.

CONCLUSION

An IGS system using FF registration is more accurate than one using LSSS (p = 0.03, 2-sided Student's t test). BAHA patients provide a unique patient population upon which IGS systems may be validated.

On combining internal and external fiducials for liver motion compensation

This paper presents an in-vivo accuracy study on combining skin markers (external fiducials) and fiducial needles (internal fiducials) for motion compensation during liver interventions. We compared the target registration error (TRE) for different numbers of skin markers n(s) and fiducial needles n(f), as well as for different transformation types, in two swine using the tip of an additional tracked needle as the target. During continuous breathing, n(f) had the greatest effect on the accuracy, yielding mean root mean square (RMS) errors of 4.8 +/- 1.1 mm (n(f) = 0), 2.0 +/- 0.9 mm (n(f) = 1) and 1.7 +/- 0.8 mm (n(f) = 2) when averaged over multiple tool arrangements (n = 18, 36, 18) with n(s) = 4. These values correspond to error reductions of 11%, 64% and 70%, respectively, compared to the case when no motion compensation is performed, i.e., when the target position is assumed to be constant. At expiration, the mean RMS error ranged from 1.1 mm (n(f) = 0) to 0.8 mm (n(f) = 2), which is of the order of magnitude of the target displacement. Our study further indicates that the fiducial registration error (FRE) of a rigid transformation reflecting tissue motion generally correlates strongly with the TRE. Our findings could be used in practice to (1) decide on a suitable combination of fiducials for a given intervention, considering the trade-off between high accuracy and low invasiveness, and (2) provide an intra-interventional measure of confidence for the accuracy of the system based on the FRE.

Localization and registration accuracy in image guided neurosurgery: a clinical study

PURPOSE

To measure and compare the clinical localization and registration errors in image-guided neurosurgery, with the purpose of revising current assumptions.

MATERIALS AND METHODS

Twelve patients who underwent brain surgeries with a navigation system were randomly selected. A neurosurgeon localized and correlated the landmarks on preoperative MRI images and on the intraoperative physical anatomy with a tracked pointer. In the laboratory, we generated 612 scenarios in which one landmark pair was defined as the target and the remaining ones were used to compute the registration transformation. Four errors were measured: (1) fiducial localization error (FLE); (2) target registration error (TRE); (3) fiducial registration error (FRE); (4) Fitzpatrick's target registration error estimation (F-TRE). We compared the different errors and computed their correlation.

RESULTS

The image and physical FLE ranges were 0.5-2.0 and 1.6-3.0 mm, respectively. The measured TRE, FRE and F-TRE were 4.1 +/- 1.6, 3.9 +/- 1.2, and 3.7 +/- 2.2 mm, respectively. Low correlations of 0.19 and 0.37 were observed between the FRE and TRE and between the F-TRE and the TRE, respectively. The differences of the FRE and F-TRE from the TRE were 1.3 +/- 1.0 mm (max = 5.5 mm) and 1.3 +/- 1.2 mm (max = 7.3 mm), respectively.

CONCLUSION

Contrary to common belief, the FLE presents significant variations. Moreover, both the FRE and the F-TRE are poor indicators of the TRE in image-to-patient registration.

Ingress of blood-borne macrophages across the blood-brain barrier in murine HIV-1 encephalitis

Blood-borne macrophage ingress into brain in HIV-1 associated neurocognitive disorders governs the tempo of disease. We used superparamagnetic iron-oxide particles loaded into murine bone marrow-derived macrophages (BMM) injected intravenously into HIV-1 encephalitis mice to quantitatively assess BMM entry into diseased brain regions. Magnetic resonance imaging tests were validated by histological coregistration and enhanced image processing. The demonstration of robust BMM migration into areas of focal encephalitis provide 'proof of concept' for the use of MRI to monitor macrophage ingress into brain.