Paired-point Registration Research Articles

INTRAOPERATIVE SURGICAL NAVIGATION BASED ON LASER SCANNER FOR IMAGE-GUIDED ORAL AND MAXILLOFACIAL SURGERY

In oral and maxillofacial surgery, computer-assisted navigation technologies have been widely used to achieve intraoperative positioning. The traditional methods mainly rely on the experience of doctors and the difference between the locations of key points in the surgical area and the preoperative planning, which have certain limitations. In this paper, a new intraoperative surgical navigation framework based on mobile laser scanner is proposed, which ensures that surgery is performed accurately according to the preoperative planning. The framework mainly includes two parts. First, the real-time surface reconstruction of the anatomy should be realized during the operation. Second, the acquired image is matched to the planned image in real time. Although the most common method of surface reconstruction is to render the volume directly from raw data or render the surface from the segmented data using computed tomography/magnetic resonance (CT/MR) data, this method is too complicated for performing the real-time operation during surgery. Furthermore, a new surface registration technique is proposed for image-guided oral and maxillofacial surgery based on the point sets. To improve the registration accuracy and robustness, the point sets are modeled by Mixed Student’s t-Distribution model. In the experiments, the point sets of CT data are from 10 patients with craniomaxillofacial diseases and the surface point set is from the LRS. The TRE of 10 data was less than 1[Formula: see text]mm. Compared with the paired-point registration method and Iterative Closest Point algorithm, the results demonstrated better performance of the proposed method, the surgical situation can be displayed in real time during the surgical process, and any differences from the surgical plan can also be reflected.

Stereotactic Navigation-Assisted Laparoscopic Resection of Challenging Low Pelvic Tumors: A Case Series.

(1) Introduction: The laparoscopic approach to low pelvic tumors is challenging and hindered by suboptimal tumor visualization and dissection, with possible oncological failure. Stereotactic navigation provides real-time image guidance that may optimize safety, accuracy, and precision when dissecting challenging low pelvic tumors. (2) Methods: Preoperative CT images were acquired with eight skin-fixed fiducials and loaded into a navigation system. A patient tracker was mounted on the bed side. Patient-to-image paired point registration was performed, and an instrument tracker was mounted on a laparoscopic instrument and calibrated for instrument tracking. Surgical operations were performed with real-time stereotactic navigation assistance. (3) Results: Three patients underwent stereotactic navigation surgery. Fiducial registration errors were good to optimal (±1.9, ±3.4, and ±3.4 mm). Lesions were easily identified and targeted with real-time navigation. Surgeries were uneventful. Histopathology examinations identified one retro-rectal schwannoma, one lateral pelvic recurrence from rectal adenocarcinoma, and one advanced anal canal carcinoma. No navigation-related complications, readmissions, or postoperative mortalities were observed. (4) Conclusions: The application of laparoscopic stereotactic navigation surgery to complex low pelvic tumors is feasible and could impact oncological surgical quality by enabling tumor targeting and ensuring resection margins. Further wider series are needed to confirm stereotactic navigation's impact on challenging low pelvic tumors.

Three-Dimensional Quantitative Assessment of Pedicle Screw Accuracy in Clinical Utilization of a New Robotic System in Spine Surgery: A Multicenter Study.

The objective of this study was to evaluate the accuracy of pedicle screw placement in patients undergoing percutaneous pedicle screw fixation with robotic guidance, using a newly developed 3-dimensional quantitative measurement system. The study also aimed to assess the clinical feasibility of the robotic system in the field of spinal surgery. A total of 113 patients underwent pedicle screw insertion using the CUVIS-spine pedicle screw guide system (CUREXO Inc.). Intraoperative O-arm images were obtained, and screw insertion pathways were planned accordingly. Image registration was performed using paired-point registration and iterative closest point methods. The accuracy of the robotic-guided pedicle screw insertion was assessed using 3-dimensional offset calculation and the Gertzbein-Robbins system (GRS). A total of 448 screws were inserted in the 113 patients. The image registration success rate was 95.16%. The average error of entry offset was 2.86 mm, target offset was 2.48 mm, depth offset was 1.99 mm, and angular offset was 3.07°. According to the GRS grading system, 88.39% of the screws were classified as grade A, 9.60% as grade B, 1.56% as grade C, 0.22% as grade D, and 0.22% as grade E. Clinically acceptable screws (GRS grade A or B) accounted for 97.54% of the total, with no reported neurologic complications. Our study demonstrated that pedicle screw insertion using the novel robot-assisted navigation method is both accurate and safe. Further prospective studies are necessary to explore the potential benefits of this robot-assisted technique in comparison to conventional approaches.

Contour Map Point Distribution and Surgeon Experience Level Affect Accuracy of Surgical Navigation in a Pilot Study.

Reliable use of surgical navigation depends upon the registration process. The gold standard is paired-point registration with bone-anchored fiducials, but contour-map registration is more practical. Surgeons may employ variable contour maps and less experienced team members often perform this critical step. The impact of these practices on target registration error (TRE) is not well-studied. A dry lab set-up consisting of a navigation system (Fusion ENT, Medtronic, Jacksonville, FL) and a sinus phantom with 2 mm radiopaque spheres in the sphenoid and ethmoid regions was developed. A CT (0.625 mm slice thickness) was obtained. Registration was performed with a contour-based protocol. Accuracy was determined using the software's distance measurement tool. Registration was performed with narrow-field (NF; forehead points medial to the mid-pupillary line) and wide field (WF; entire forehead) contour maps. An experienced rhinologist and a resident surgeon performed each registration in triplicate and TRE at the sphenoid and ethmoid markers was measured in triplicate. WF mapping had a lower TRE than NF (1.09 mm [95% Confidence Interval (CI) 0.96-1.22] vs 1.68 mm [95% CI 1.50-1.86]). The experienced surgeon had a lower TRE compared to the resident (1.21 mm [95% CI 1.08-1.34] vs 1.54 mm [95% CI 1.35-1.74]). In this navigation model, wide field mapping offers better accuracy than narrow-field mapping, and an experienced surgeon seemed to achieve better accuracy than a resident surgeon. These observations have potential implications for the use of this technology in the operating room.

ROBUST SURFACE-MATCHING REGISTRATION BASED ON THE STRUCTURE INFORMATION FOR IMAGE-GUIDED NEUROSURGERY SYSTEM

Image-to-patient space registration is to make the accurate alignment between the actual operating space and the image space. Although the image-to-patient space registration using paired-point is used in some image-guided neurosurgery systems, the current paired-point registration method has some drawbacks and usually cannot achieve the best registration result. Therefore, surface-matching registration is proposed to solve this problem. This paper proposes a surface-matching method that accomplishes image-to-patient space registration automatically. We represent the surface point clouds by the Gaussian Mixture Model (GMM), which can smoothly approximate the probability density distribution of an arbitrary point set. We also use mutual information as the similarity measure between the point clouds and take into account the structure information of the points. To analyze the registration error, we introduce a method for the estimation of Target Registration Error (TRE) by generating simulated data. In the experiments, we used the point sets of the cranium surface and the model of the human head determined by a CT and laser scanner. The TRE was less than 2[Formula: see text]mm, and the TRE had better accuracy in the front and the posterior region. Compared to the Iterative Closest Point algorithm, the surface registration based on GMM and the structure information of the points proved superior in registration robustness and accurate implementation of image-to-patient registration.

Evaluating an Image-Guided Operating Room with Cone Beam CT for Skull Base Surgery.

Importance Skull base surgery requires precise preoperative assessment and intraoperative management of the patient. Surgical navigation is routinely used for complex skull base cases; however, the image guidance is commonly based on preoperative scans alone. Objective The primary objective of this study was to assess the image quality of intraoperative cone-beam computed tomography (CBCT) within anatomical landmarks used in sinus and skull base surgery. The secondary objective was to assess the registration error of a surgical navigation system based on intraoperative CBCT. Design Present study is a retrospective case series of image quality after intraoperative cone beam CT. Setting The study was conducted at Toronto General Hospital and Princess Margaret Cancer Centre, University Health Network, Toronto. Participants A total of 46 intraoperative scans (34 patients, 21 skull base, 13 head and neck) were studied. Main Outcome and Measures Thirty anatomical landmarks (vascular, soft tissue, and bony) within the sinuses and anterior skull base were evaluated for general image quality characteristics: (1) bony detail visualization; (2) soft-tissue visualization; (3) vascular visualization; and (4) freedom from artifacts (e.g., metal). Levels of intravenous (IV) contrast enhancement were quantified in Hounsfield's units (HU). Standard paired-point registration between imaging and tracker coordinates was performed using 6 to 8 skin fiducial markers and the corresponding fiducial registration error (FRE) was measured. Results Median score for bony detail on CBCT was 5, remaining at 5 after administration of IV contrast. Median soft-tissue score was 2 for both pre- and postcontrast. Median vascular score was 1 precontrast and 3 postcontrast. Median score for artifacts on CBCT were 2 for both pre-and postcontrast, and metal objects were noted to be the most significant source of artifact. Intraoperative CBCT allowed preresection images and immediate postresection images to be available to the skull base surgeon. There was a significant improvement in mean (standard deviation [SD]) CT intensity in the left carotid artery postcontrast 334 HU (67 HU) ( p < 10 -10 ). The mean FRE was 1.8 mm (0.45 mm). Conclusion Intraoperative CBCT in complex skull base procedures provides high-resolution bony detail allowing immediate assessment of complex resections. The use of IV contrast with CBCT improves the visualization of vasculature. Image-guidance based on CBCT yields registration errors consistent with standard techniques.

Validation of the registration accuracy of navigation-assisted arthroscopic débridement for elbow osteoarthritis

The identification and precise removal of bony impingement lesions during arthroscopic débridement arthroplasty for elbow osteoarthritis is technically difficult. Surgical navigation systems, combined with preoperative 3-dimensional (3D) assessment of bony impingements, can provide real-time tracking of the surgical instruments and impingement lesions. This study aims to determine the registration accuracy of the navigation system for the humerus and ulna during elbow arthroscopy. We tested the registration procedure using resin bone models of 3 actual patients with elbow osteoarthritis. We digitized bone surface points using navigation pointers under arthroscopy. We initially performed paired-point registration, digitizing 6 preset anatomical landmarks, and then refined the initial alignment with surface matching registration, digitizing 30 points. The registration accuracy for each trial was evaluated as the mean target registration error in each reference marker. Three observers repeated the registration procedure 5 times each with the 3 specimens (total, 45 trials). The median of the registration accuracy was evaluated in total (45 trials) as the accuracy of the registration procedure. The differences in the registration accuracy among the 3 observers (median of 15 trials) were also examined. The total registration accuracies were 0.96 mm for the humerus and 0.85 mm for the ulna. No significant differences were found in the registration accuracy for the humerus and ulna among the 3 observers. This arthroscopic-assisted registration procedure is sufficiently feasible and accurate for application of the navigation system to arthroscopic débridement arthroplasty in clinical settings.

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

BackgroundManual paired‐point registration for navigated ENT‐surgery is prone to human errors; automatic surface registration is often caught in local minima.MethodsAnatomical 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.ResultsSuccessful 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.ConclusionThe satisfactory behavior of this approach potentially suggests a wider clinical integration.

Probe versus microscope: a comparison of different methods for image-to-patient registration

PurposeComputer-aided navigation is widely used in ENT surgery. The position of a surgical instrument is shown in the CT/MR images of the patient and can thus be a good support for the surgeon. The accuracy is highly dependent on the registration done prior to surgery. A microscope and a probe can both be used for registration and navigation, depending on the surgical intervention. A navigation system typically only reports the fiducial registration error after paired-point registration. However, the target registration error (TRE)—a measurement for the accuracy in the surgical area—is much more relevant. The aim of this work was to compare the performance of a microscope relative to a conventional probe-based approach with different registration methods.MethodsIn this study, optical tracking was used to register a plastic skull to its preoperative CT images with paired-point registration. Anatomical landmarks and skin-affixed markers were used as fiducials and targets. With both microscope and probe, four different registration methods were evaluated based on their TREs at 10 targets. For half of the experiments, a surface registration and/or external fiducials were used additionally to paired-point registration to study their influence to accuracy.ResultsOverall, probe registration leads to a smaller TRE (1.69 pm 0.74,hbox {mm}) than registration with a microscope (2.19 pm 0.94,hbox {mm}). Additional surface registration does not result in better accuracy of navigation for microscope and probe. The lowest mean TRE for both pointers can be achieved with paired-point registration only and radiolucent markers.ConclusionOur experiments showed that a probe used for registration and navigation achieves lower TREs compared using a microscope. Neither additional surface registration nor additional fiducials on an external reference element are necessary for improved accuracy of navigated ENT surgery on a plastic skull.

Can Augmented Reality Be Helpful in Pelvic Bone Cancer Surgery? An In Vitro Study.

Application of surgical navigation for pelvic bone cancer surgery may prove useful, but in addition to the fact that research supporting its adoption remains relatively preliminary, the actual navigation devices are physically large, occupying considerable space in already crowded operating rooms. To address this issue, we developed and tested a navigation system for pelvic bone cancer surgery assimilating augmented reality (AR) technology to simplify the system by embedding the navigation software into a tablet personal computer (PC). Using simulated tumors and resections in a pig pelvic model, we asked: Can AR-assisted resection reduce errors in terms of planned bone cuts and improve ability to achieve the planned margin around a tumor in pelvic bone cancer surgery? We developed an AR-based navigation system for pelvic bone tumor surgery, which could be operated on a tablet PC. We created 36 bone tumor models for simulation of tumor resection in pig pelves and assigned 18 each to the AR-assisted resection group and conventional resection group. To simulate a bone tumor, bone cement was inserted into the acetabular dome of the pig pelvis. Tumor resection was simulated in two scenarios. The first was AR-assisted resection by an orthopaedic resident and the second was resection using conventional methods by an orthopaedic oncologist. For both groups, resection was planned with a 1-cm safety margin around the bone cement. Resection margins were evaluated by an independent orthopaedic surgeon who was blinded as to the type of resection. All specimens were sectioned twice: first through a plane parallel to the medial wall of the acetabulum and second through a plane perpendicular to the first. The distance from the resection margin to the bone cement was measured at four different locations for each plane. The largest of the four errors on a plane was adopted for evaluation. Therefore, each specimen had two values of error, which were collected from two perpendicular planes. The resection errors were classified into four grades: ≤ 3 mm; 3 to 6 mm; 6 to 9 mm; and > 9 mm or any tumor violation. Student's t-test was used for statistical comparison of the mean resection errors of the two groups. The mean of 36 resection errors of 18 pelves in the AR-assisted resection group was 1.59 mm (SD, 4.13 mm; 95% confidence interval [CI], 0.24-2.94 mm) and the mean error of the conventional resection group was 4.55 mm (SD, 9.7 mm; 95% CI, 1.38-7.72 mm; p < 0.001). All specimens in the AR-assisted resection group had errors < 6 mm, whereas 78% (28 of 36) of errors in the conventional group were < 6 mm. In this in vitro simulated tumor model, we demonstrated that AR assistance could help to achieve the planned margin. Our model was designed as a proof of concept; although our findings do not justify a clinical trial in humans, they do support continued investigation of this system in a live animal model, which will be our next experiment. The AR-based navigation system provides additional information of the tumor extent and may help surgeons during pelvic bone cancer surgery without the need for more complex and cumbersome conventional navigation systems.

Experimental validation of predicted application accuracies for computer-assisted (CAS) intraoperative navigation with paired-point registration

PurposeThe target registration error (TRE) is a crucial parameter to estimate the potential usefulness of computer-assisted navigation intraoperatively. Both image-to-patient registration on base of rigid-body registration and TRE prediction methods are available for spatially isotropic and anisotropic data. This study presents a thorough validation of data obtained in an experimental operating room setting with CT images.MethodsOptical tracking was used to register a plastic skull, an anatomic specimen, and a volunteer to their respective CT images. Plastic skull and anatomic specimen had implanted bone fiducials for registration; the volunteer was registered with anatomic landmarks. Fiducial localization error, fiducial registration error, and total target error (TTE) were measured; the TTE was compared to isotropic and anisotropic error prediction models. Numerical simulations of the experiment were done additionally.ResultsThe user localization error and the TTE were measured and calculated using predictions, both leading to results as expected for anatomic landmarks and screws used as fiducials. TRE/TTE is submillimetric for the plastic skull and the anatomic specimen. In the experimental data a medium correlation was found between TRE and target localization error (TLE). Most of the predictions of the application accuracy (TRE) fall in the 68% confidence interval of the measured TTE. For the numerically simulated data, a prediction of TTE was not possible; TRE and TTE show a negligible correlation.ConclusionExperimental application accuracy of computer-assisted navigation could be predicted satisfactorily with adequate models in an experimental setup with paired-point registration of CT images to a patient. The experimental findings suggest that it is possible to run navigation and prediction of navigation application accuracy basically defined by the spatial resolution/precision of the 3D tracker used.

Alarming system in real time and high quality display using volumerendering for otologic surgical navigation: a preclinical study

Objective:The aim of this study is to design and develop a novel navigation framework that alarms the surgeon with sound during drilling and dynamically renders the high quality medical image data. Method:The accuracy of the proposed system were measured with a skull model using paired point registration and the target registration error (TRE) was computed. We segmented the accurate structure of target and compute the minimum distance between the drill tip and the target. Once the drill tip approached the target structures, the system would alarm the surgeon. We proposed a novelty method that used a mask to render the medical image data in real time. Result:The maximum distance error of 90 target points was 1.016 mm, the minimum was 0.427 mm, and the average distance error was (0.74±0.07) mm. The design and development of the alarming system and dynamically rendering the medical image data of navigation system was accomplished. Conclusion:The results show that the accuracy of the navigation system can meet the clinical needs. It also demonstrates the feasibility of the alarm system and dynamic display system and its application prospects.

Robotically assisted ureteroscopy for kidney exploration.

Ureteroscopy is a minimally invasive procedure for diagnosis and treatment of a wide range of urinary tract pathologies. It is most commonly performed in the diagnostic work-up of hematuria and the diagnosis and treatment of upper urinary tract malignancies and calculi. Ergonomic and visualization challenges as well as radiation exposure are limitations to conventional ureteroscopy. For example, for diagnostic tumor inspection, the urologist has to maneuver the ureteroscope through each of the 6 to 12 calyces in the kidney under fluoroscopy to ensure complete surveillance. Therefore, we have been developing a robotic system to "power drive" a flexible fiber-optic ureteroscope with 3D tip tracking and pre-operative image overlay. Our goal is to provide the urologist precise control of the ureteroscope tip with less radiation exposure. Our prototype system allows control of the three degrees of freedom of the ureteroscope via brushless motors and a joystick interface. The robot provides a steady platform for controlling the ureteroscope. Furthermore, the robot design facilitates a quick "snap-in" of the ureteroscope, thus allowing the ureteroscope to be mounted midway through the procedure. We have completed the mechanical system and the controlling software and begun evaluation using a kidney phantom. We put MRI-compatible fiducials on the phantom and obtained MR images. We registered these images with the robot using an electromagnetic tracking system and paired-point registration. The system is described and initial evaluation results are given in this paper.

Fiducial-based registration with a touchable region model.

Image-guided surgery requires registration between an image coordinate system and an intraoperative coordinate system that is typically referenced to a tracking device. In fiducial-based registration methods, this is achieved by localizing points (fiducials) in each coordinate system. Often, both localizations are performed manually, first by picking a fiducial point in the image and then by using a hand-held tracked pointer to physically touch the corresponding fiducial on the patient. These manual procedures introduce localization error that is user-dependent and can significantly decrease registration accuracy. Thus, there is a need for a registration method that is tolerant of imprecise fiducial localization in the preoperative and intraoperative phases. We propose the iterative closest touchable point (ICTP) registration framework, which uses model-based localization and a touchable region model. This method consists of three stages: (1) fiducial marker localization in image space, using a fiducial marker model, (2) initial registration with paired-point registration, and (3) fine registration based on the iterative closest point method. We perform phantom experiments with a fiducial marker design that is commonly used in neurosurgery. The results demonstrate that ICTP can provide accuracy improvements compared to the standard paired-point registration method that is widely used for surgical navigation and surgical robot systems, especially in cases where the surgeon introduces large localization errors. The results demonstrate that the proposed method can reduce the effect of the surgeon's localization performance on the accuracy of registration, thereby producing more consistent and less user-dependent registration outcomes.

Assessment of registration accuracy during computer-aided oncologic limb-salvage surgery.

Computer-aided surgery is used in musculoskeletal tumor procedures to improve the surgeon's orientation to local anatomy during tumor resection. For the navigation system to function correctly, preoperative imaging (e.g., CT, MR) must be registered to the patient in the operating room. The goals of this study were (1) to directly quantify registration accuracy in computer-aided tumor surgery and (2) to validate the "system reported error" (SRE) of the navigation system. Registration accuracy was evaluated in eight bone sarcoma cases by determining the location of the anatomical paired-points used for registration following surface matching. Coordinates of specific intraoperative post-registration points were compared with the corresponding coordinates in preoperative CT scans to determine the measurement error (ME). The mean difference between post-registration points and planned registration points was 12.21±6.52 mm significantly higher than the mean SRE (0.68 ± 0.15 mm; p = 0.002; 95 % CI 6.11-16.96 mm). The SRE poorly correlated with the calculated ME (R(2) = 0.040). Anatomical paired-point registration with surface matching results in a substantial shift in the post-registration coordinates of the same paired-points used for registration, and this shift is not represented by the SRE. The SRE of a surgical navigation system was poorly correlated with direct measurements obtained in musculoskeletal tumor surgery. Improvement in registration accuracy is needed to better navigate tumor boundaries and ensure clear margins while maximally preserving the unaffected tissues and reducing operative morbidity.

Direct application of MR images to computer-assisted bone tumor surgery

BackgroundWe describe a method for the direct application of MR images to navigation-assisted bone tumor surgery as an alternative to CT-MRI fusion. MethodSix patients with an orthopedic malignancy were employed for this method during navigation-assisted tumor resection. Tumor types included osteosarcoma (4), high-grade chondrosarcoma (1), and adamantinoma (1). Mean patient age was 25.3 years (range 18–52 years). Mean duration of follow-up was 25.8 months (range 18–32 months). Resorbable pin placement and rapid 3-dimensional spoiled gradient echo sequences made the direct application of MR images to computer-assisted bone tumor surgery without CT-MR image fusion possible. A paired-point registration technique was employed for patient-image registration in all patients. ResultsIt took 20 min on average to set up the navigation (range 15–25 min). The mean registration error was 0.98 mm (range 0.4–1.7 mm). On histologic examination, distances from tumors to resection margins were in accord with preoperative plans. No patient had a local recurrence or distant metastasis at the last follow-up. ConclusionDirect patient-to-MRI registration is a very useful method for bone tumor surgery, permitting the application of MR images to intraoperative visualization without any additional costs or exposure of the patient to radiation from the preoperative CT scan.

A comparison of registration techniques for computer- and image-assisted elbow surgery

Purpose: Accurate determination of the flexion-extension axis of the elbow is critical to the successful placement of elbow arthroplasties, articulated external fixators and ligament reconstructions. We expect axis alignment using computer-assisted techniques to improve the outcome of these procedures. For image-based procedures, registration (i.e. the transformation needed to align two sets of points) during surgery is critical for accurate alignment. A surface-based registration technique, employing a hand-held laser scanner, was evaluated against a stand-alone paired-point registration method to determine whether it led to improved alignment of the elbow’s flexion-extension axis. Methods: Twelve cadaveric distal-humeri were selected for registration. To perform paired-point (TP-PP) registration, key anatomical landmarks (capitellum, trochlear sulcus and distal humeral shaft) were digitized using a tracked-probe (TP) and an electromagnetic tracking device (Flock of Birds, Ascension Tech). Using the geometric centers of these landmarks, TP-PP registration to CT data was performed. Surface registration was achieved using the iterative closest point (ICP) least-squares algorithm and the results were evaluated for two devices; registration employing the tracked-probe (TP-ICP) and registration employing a hand-held laser scanner, HHS-ICP (FastSCAN, Polhemus). For surface registration, to be consistent with the amount of the joint exposed during a typical surgical procedure, only the articular surface was used for alignment. Results: Registration error (Figure 1) was lowest for the HHS-ICP method with a mean of 0.8±0.3-mm (maximum error, 1.4-mm) in translation, compared with a mean error of 1.5±0.5-mm (maximum error, 2.4-mm) for the TP-ICP method and 1.9±1.0-mm (maximum error, 4.4-mm) for the TP-PP method (p Conclusions: Overall, the reliability of surface-based registration combined with the implementation of the hand-held laser scanner demonstrated greater registration accuracy. A reliable surface-based registration technique may lead to a more accurate determination of the elbow’s flexion-extension axis during surgical procedures, leading to improved joint motion and implant longevity. The implications of these results can also be extended to other joints that employ comparable computer-assisted surgical techniques.

Electromagnetic tracking for abdominal interventions in computer aided surgery: Zhang H, Banovac F, Lin R, Glossop N, Wood BJ, Lindisch D, Levy E, Cleary K, Department of Radiology, Imaging Science and Information Systems (ISIS) Center, Georgetown University, and Georgetown University Hospital, Washington, DC

Electromagnetic tracking has great potential for assisting physicians in precision placement of instruments during minimally invasive interventions in the abdomen, since electromagnetic tracking is not limited by the line-of-sight restrictions of optical tracking. A new generation of electromagnetic tracking has recently become available, with sensors small enough to be included in the tips of instruments. To fully exploit the potential of this technology, our research group has been developing a computer aided, image-guided system that uses electromagnetic tracking for visualization of the internal anatomy during abdominal interventions. As registration is a critical component in developing an accurate image-guided system, we present three registration techniques: 1) enhanced paired-point registration (time-stamp match registration and dynamic registration); 2) orientation-based registration; and 3) needle shape-based registration. Respiration compensation is another important issue, particularly in the abdomen, where respiratory motion can make precise targeting difficult. To address this problem, we propose reference tracking and affine transformation methods. Finally, we present our prototype navigation system, which integrates the registration, segmentation, path-planning and navigation functions to provide real-time image guidance in the clinical environment. The methods presented here have been tested with a respiratory phantom specially designed by our group and in swine animal studies under approved protocols. Based on these tests, we conclude that our system can provide quick and accurate localization of tracked instruments in abdominal interventions, and that it offers a user-friendly display for the physician.

A comparison of registration techniques for computer- and image-assisted elbow surgery

Optimal function following elbow replacement surgery is dependent on the accurate replication of the elbow's flexion-extension axis. Currently, position and orientation of the axis are estimated from visual landmarks. In order to develop computer-assisted techniques to more accurately define this axis, a surface-based registration technique employing a hand-held laser scanner was evaluated against a conventional paired-point registration method to determine whether it produced improved alignment of the flexion-extension axis of the elbow. Registration error was 0.8 ± 0.3 mm for surface-based registration, compared with 1.9 ± 1.0 mm for the conventional registration method. These results suggest that the implementation of a surface-based registration technique may lead to a more accurate axis determination and improved clinical outcomes following elbow replacement surgery.

Contour and Paired‐Point Registration in a Model for Image‐Guided Surgery

This study assesses target registration error (TRE) of contour-based registration (CBR) and paired-point registration (PPR) for endoscopic sinus surgery. The experimental registration model consisted of a replica of a human head (Sawbones #1345-27; Pacific Research Laboratories, Vashon, WA). Twelve surface fiducial markers were affixed to the simulated skin on the model, and titanium screws were placed in the regions of the anterior ethmoid (AE) and sphenoid face (SF). An axial computed tomography scan (1-mm slice thickness) was then obtained. Registration was then performed on the InstaTrak 3500 Plus (GE Surgical Navigation & Visualization, Lawrence, MA) with the standard PPR protocol with 12 points and CBR protocol with 500, 250, 125, 50, and 4 points. TRE was then calculated at the AE and SF targets. Target registration error was significantly lower for paired-point registration compared with contour-based registration (AE, 0.5 mm vs. 1.5 mm, P < .0001; SF, 0.8 mm vs. 1.5 mm, P < .0001). Among contour-based registration protocols, target registration error at the sphenoid face was lowest with 50 points (1.5 mm, P < .02). At the anterior ethmoid, contour-based registration with 50 points produced a lower target registration error than contour-based registration with 125 points (1.5 vs. 1.8 mm, P < .01). Other target registration error values for CBR were similar at both regions. The target registration error interquartile range was lowest with CBR-125 at both regions. Paired-point registration provided significantly lower target registration error. Contour-based registration with 125 points (and possibly as few as 50 points) produces clinically acceptable target registration error. This registration model elucidates important concepts about registration for surgical navigation for sinus surgery.