Surface Matching Registration Research Articles

Bony Surface-Matching Registration of Neuronavigation with Sectioned 3-Dimensional Skull in Prone Position

BackgroundNeuronavigation has become an essential system for brain tumor resections. It is sometimes difficult to obtain accurate registration of neuronavigation in prone position. The bony surface-matching registration should be more precise than skin surface-matching registration, however it is difficult to establish bony registration with limited exposed bone. We created a new bony surface-matching method to a section 3-dimensional (D) virtual skull in a neuronavigation system and register with a sectioned 3-D skull. In this study, the bony surface-matching with sectioned 3-D registration is applied to provide precise registration for brain tumor resections in prone position. MethodsFrom May 2023 to April 2024, 17 patients who underwent brain tumor resection in prone position were enrolled. The navigation system StealthStation S8 (Medtronic) was used. Bony surface-matching registration with a whole 3-D skull in a neuronavigation system was performed. Then, a sectioned 3-D skull was made according to surgical location to compare with whole 3-D skull registration. A phantom model was also used to validate between whole and sectioned 3-D skull registration. ResultsWhole 3-D skull registration was successful in only 2 (11.8%) patients, on the other hand, sectioned 3-D skull registration was successful in 16 (94.1%) patients. The examinations with a phantom skull model also showed superiority of sectioned 3-D skull registration to whole 3-D skull registration. ConclusionSectioned 3-D skull registration was superior to whole 3-D skull registration. Sectioned 3-D skull method could provide accurate registration with limited exposed bone.

Automatic image registration on intraoperative CBCT compared to Surface Matching registration on preoperative CT for spinal navigation: accuracy and workflow

IntroductionSpinal navigation solutions have been slower to develop compared to cranial ones. To facilitate greater adoption and use of spinal navigation, the relatively cumbersome registration processes need to be improved upon. This study aims to validate a new solution for automatic image registration and compare it to a traditional Surface Matching method.MethodAdult patients undergoing spinal surgery requiring navigation were enrolled after providing consent. A registration matrix—Universal AIR (= Automatic Image Registration)—was placed in the surgical field and used for automatic registration based on intraoperative 3D imaging. A standard Surface Matching method was used for comparison. Accuracy measurements were obtained by comparing planned and acquired coordinates on the vertebrae.ResultsThirty-nine patients with 42 datasets were included. The mean accuracy of Universal AIR registration was 1.20 ± 0.42 mm, while the mean accuracy of Surface Matching registration was 1.94 ± 0.64 mm. Universal AIR registration was non-inferior to Surface Matching registration. Post hoc analysis showed a significantly greater accuracy for Universal AIR registration. In Surface Matching, but not automatic registration, user-related errors such as incorrect identification of the vertebral level were seen.ConclusionAutomatic image registration for spinal navigation using Universal AIR and intraoperative 3D imaging provided improved accuracy compared to Surface Matching registration. In addition, it minimizes user errors and offers a standardized workflow, making it a reliable registration method for navigated spinal procedures.

Open-source navigation system for tracking dissociated parts with multi-registration.

During reconstructive surgery, knee and hip replacements, and orthognathic surgery, small misalignments in the pose of prosthesis and bones can lead to severe complications. Hence, the translational and angular accuracies are critical. However, traditional image-based surgical navigation lacks orientation data between structures, and imageless systems are unsuitable for cases of deformed anatomy. We introduce an open-source navigation system using a multiple registration approach that can track instruments, implants, and bones to precisely guide the surgeon in emulating a preoperative plan. We derived the analytical error of our method and designed a set of phantom experiments to measure its precision and accuracy. Additionally, we trained two classification models to predict the system reliability from fiducial points and surface matching registration data. Finally, to demonstrate the procedure feasibility, we conducted a complete workflow for a real clinical case of a patient with fibrous dysplasia and anatomical misalignment of the right femur using plastic bones. The system is able to track the dissociated fragments of the clinical case and average alignment errors in the anatomical phantoms of [Formula: see text] mm and [Formula: see text]. While the fiducial-points registration showed satisfactory results given enough points and covered volume, we acknowledge that the surface refinement step is mandatory when attempting surface matching registrations. We believe that our device could bring significant advantages for the personalized treatment of complex surgical cases and that its multi-registration attribute is convenient for intraoperative registration loosening cases.

Fibular registration using surface matching in navigation-guided osteotomies: a proof of concept study on 3D-printed models.

Fibula free flap is currently used in mandibular reconstruction. The main difficulties involved in this surgery concern mandible shaping and therefore, osteotomy positioning on the fibula. The use of navigation could help in osteotomy positioning, but accurate registration is required. We assess a surface-matching method for fibula registration that relies on an iterative closest point (ICP) algorithm. Since the fibula shape is landmark free, a robust registration initialization approach is used to avoid non-optimal local minimums in the ICP. Bone surface-matching registration was evaluated on a 3D printed fibula and compared to its virtual reference model. The registration initialization relied on 3 initialization points placed on the surgically exposed area, geometrically remote from the fibular distal extremity. The bone surface was digitized, and the obtained point clouds were registered to the virtual reference model. The position of 3 assessment points engraved on the 3D printed fibula was then compared to that of the equivalent points on the virtual model. The registration procedure was performed 24 times by an expert surgeon. Seventy-two target registration errors (TRE) were computed, corresponding to the distance between the paired assessment points. Most TRE (86.1%) were less than 1mm, with a maximum of 1.552mm. The overall mean value was 0.759 ± 0.302mm. This study illustrates a surface-matching approach for fibula registration, with an initialization method based on points remote from the fibula distal extremity. This registration technique gave promising results and should be considered as a valid registration method for straight bones like the fibula. These findings indicate that navigation can be used for fibula flap shaping for mandibular reconstruction, with a noninvasive and accurate registration method.

Image Guidance in Spinal Surgery: A Critical Appraisal and Future Directions.

Image-guided spinal surgery (IGSS) underwent rapid development over the past decades. The goal of IGSS is to increase patient safety and improve workflow. We present an overview of the history of IGSS, illustrate its current state, and highlight future developments. Currently, IGSS requires an image set, a tracking system, and a calibration method. Two-dimensional images have many disadvantages as a source for navigation. Currently, the most common navigation technique is three-dimensional (3D) navigation based on cross-sectional imaging techniques such as cone-beam computed tomography (CT) or fan-beam CT. Electromagnetic tracking uses an electromagnetic field to localize instruments. Optical tracking using infrared cameras has currently become one of the most common tracking methods in IGSS. The three most common techniques currently used are the point-matching registration technique, the surface-matching registration technique, and the automated registration technique. Augmented reality (AR) describes a computer-generated image that can be superimposed onto the real-world environment. Marking pathologies and anatomical landmarks are a few examples of many possible future applications. Additionally, AR offers a wide range of possibilities in surgical training. The latest development in IGSS is robotic-assisted surgery (RAS). The presently available data on RAS are very encouraging, but further improvements of these procedures is expected. IGSS significantly evolved since its inception and is becoming a routinely used technology. In the future, IGSS will combine the advantages of "active/freehand 3D navigation" with AR and RAS and will one day find its way into all aspects of spinal surgery, not only in instrumented procedures.

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.

A robust automated surface-matching registration method for neuronavigation.

The surface-matching registration method in the current neuronavigation completes the coarse registration mainly by manually selecting anatomical landmarks, which increases the registration time, makes the automatic registration impossible and sometimes results in mismatch. It may be more practical to use a fast, accurate, and automatic spatial registration method for the patient-to-image registration. A coarse-to-fine spatial registration method to automatically register the patient space to the image space without placing any markers on the head of the patient was proposed. Three-dimensional (3D) keypoints were extracted by 3D Harris corner detector from the point clouds in the patient and image spaces, and used as input to the 4-points congruent sets (4PCS) algorithm which automatically registered the keypoints in the patient space with the keypoints in the image space without any assumptions about initial alignment. Coarsely aligned point clouds in the patient and image space were then fine-registered with a variant of the iterative closest point (ICP) algorithm. Two experiments were designed based on one phantom and five patients to validate the efficiency and effectiveness of the proposed method. Keypoints were extracted within 7.0s with a minimum threshold 0.001. In the phantom experiment, the mean target registration error (TRE) of 15 targets on the surface of the elastic phantom in the five experiments was 1.17±0.04mm, and the average registration time was 17.4s. In the clinical experiments, the mean TRE of the targets on the first, second, third, fourth, and fifth patient's head surface were 1.70±0.32mm, 1.83±0.38mm, 1.64±0.3mm, 1.67±0.35mm, and 1.72±0.31mm, respectively, and the average registration time was 21.4s. Compared with the method only based on the 4PCS and ICP algorithm and the current clinical method, the proposed method has obvious speed advantage while ensuring the registration accuracy. The proposed method greatly improves the registration speed while guaranteeing the equivalent or higher registration accuracy, and avoids a tedious manual process for the coarse registration.

Asymmetrical surface scanning registration for image-guided otologic surgery: A phantom study

ObjectiveTo develop a registration procedure to achieve a higher degree of registration accuracy in image-guided otological surgery, paying particular attention to the registration centroid. MethodsA head phantom was used to measure the target registration error (TRE) at measurement points at various depth from the surface of the head. The surface-matching registration was performed using a commercially available surgical navigation system. We registered the phantom using only one ear of either side (right 100% - left 0%, or right 0% - left 100%) or using both ears with variable ratios (right 75% - left 25%, right 50% - left 50%, or right 25% - left 75%). ResultsThe overall TRE was the smallest when registration was performed equally on both sides. However, the TRE at 20–50 mm from the surface was the smallest when the fiducial points for the registration were collected asymmetrically at a ratio of 75:25 and weighed heavier on the operating side, and this difference was statistically significant. ConclusionThe accuracy of image-guided surgery can be improved by carefully planning the registration procedure without changing the procedure itself. Accurate image-guided surgery at the middle and inner ear was achieved using 75% of the point cloud for the operating side and 25% of that for the opposite side for the registration.

Image-Guided Biopsy of Intracranial Lesions with a Small Robotic Device (iSYS1): A Prospective, Exploratory Pilot Study.

Robotic technologies have been used in the neurosurgical operating rooms for the last 30 yr. They have been adopted for several stereotactic applications and, particularly, image-guided biopsy of intracranial lesions which are not amenable for open surgical resection. To assess feasibility, safety, accuracy, and diagnostic yield of robot-assisted frameless stereotactic brain biopsy with a recently introduced miniaturized device (iSYS1; Interventional Systems Medizintechnik GmbH, Kitzbühel, Austria), fixed to the Mayfield headholder by a jointed arm. Clinical and surgical data of all patients undergoing frameless stereotactic biopsies using the iSYS1 robotized system from October 2016 to December 2017 have been prospectively collected and analyzed. Facial surface registration has been adopted for optical neuronavigation. Thirty-nine patients were included in the study. Neither mortality nor morbidity related to the surgical procedure performed with the robot was recorded. Diagnostic tissue samples were obtained in 38 out of 39 procedures (diagnostic yield per procedure was 97.4%). All patients received a definitive histological diagnosis. Mean target error was 1.06 mm (median 1 mm, range 0.1-4 mm). The frameless robotic iSYS1-assisted biopsy technique was determined to be feasible, safe, and accurate procedure; moreover, the diagnostic yield was high. The surface matching registration method with computed tomography as the reference image set did not negatively affect the accuracy of the procedure.

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.

An Analysis of the Characteristics and Improved Use of Newly Developed CT-based Navigation System in Total Hip Arthroplasty.

We developed a surface matching-type computed tomography (CT)-based navigation system for total hip arthroplasty (the N-navi; TEIJIN NAKASHIMA MEDICAL, Okayama, Japan). In the registration step, surface matching was performed with digitizing points on the pelvic bone surface after coarse paired matching. In the present study, we made model bones from the CT data of patients whose acetabular shapes had various deformities. We measured the distances and angles after surface matching from the fiducial points and evaluated the ability to correct surface-matching registration on each pelvic form, using several areas and numbers of points. When the surface-matching points were taken on the superior area of the acetabulum, the correction was easy for the external direction, but it was difficult to correct for the anterior and proximal directions. The correction was difficult for external and proximal directions on the posterior area. Each area of surface-matching points has particular directions that are easily corrected and other directions that are difficult to correct. The shape of the pelvis also affected the correction ability. Our present findings suggest that checking the position after coarse paired matching and choosing the surface-matching area and points that are optimal to correct will improve the accuracy of total hip arthroplasty and reduce surgical times.

Development of a surgical navigation system based on 3D Slicer for intraoperative implant placement surgery

Implant placement has been widely used in various kinds of surgery. However, accurate intraoperative drilling performance is essential to avoid injury to adjacent structures. Although some commercially-available surgical navigation systems have been approved for clinical applications, these systems are expensive and the source code is not available to researchers. 3D Slicer is a free, open source software platform for the research community of computer-aided surgery. In this study, a loadable module based on Slicer has been developed and validated to support surgical navigation. This research module allows reliable calibration of the surgical drill, point-based registration and surface matching registration, so that the position and orientation of the surgical drill can be tracked and displayed on the computer screen in real time, aiming at reducing risks. In accuracy verification experiments, the mean target registration error (TRE) for point-based and surface-based registration were 0.31±0.06mm and 1.01±0.06mm respectively, which should meet clinical requirements. Both phantom and cadaver experiments demonstrated the feasibility of our surgical navigation software module.

Prospects and limitations of different registration modalities in electromagnetic ENT navigation.

The present study examined electromagnetic tracking technology for ENT navigation. Five different registration modalities were compared and navigation accuracy was assessed. Four skull models were individually fabricated with a three-dimensional printer, based on patients' computer tomography datasets. Individual silicone masks were fitted for skin and soft tissue simulation. Five registration modalities were examined: (1) invasive marker, (2) automatic, (3) surface matching (AccuMatch), (4) anatomic landmarks, and (5) oral splint registration. Overall navigation accuracy and accuracy on selected anatomic locations were assessed by targeting 26 titanium screws previously placed over the skull. Overall navigation accuracy differed significantly between all registration modalities. The target registration error was 0.94±0.06mm (quadratic mean±standard deviation) for the invasive marker registration, 1.41±0.04mm for the automatic registration, 1.59±0.14mm for the surface matching registration, and 5.15±0.66mm (four landmarks) and 4.37±0.73mm (five landmarks) for the anatomic landmark registration. Oral splint registration proved itself to be inapplicable to this navigation system. Invasive marker registration was superior on most selected anatomic locations. However, on the ethmoid and sphenoid sinus the automatic registration process revealed significantly lower target registration error values. Only automatic and surface registration met the accuracy requirements for noninvasive registration. Particularly, the automatic image-to-world registration reaches target registration error values on the anterior skull base which are comparable with the gold standard of invasive screw registration.

Automated Global Optimization Surface-Matching Registration Method for Image-to-Patient Spatial Registration in an Image-Guided Neurosurgery System

Automated Global Optimization Surface-Matching Registration Method for Image-to-Patient Spatial Registration in an Image-Guided Neurosurgery System

A phantom evaluation of Sentinel™, a commercial laser/camera surface imaging system for patient setup verification in radiotherapy

The aim of this study is to investigate the performance of Sentinel(™), a patient setup verification device based on a laser/camera system, when used on rigid-body phantoms. The Sentinel system consists of a scanner unit, containing the laser and the camera, and surface-matching registration software. For the registration procedure, both a computed tomography (CT) and a Sentinel image can be used as a reference. Tests were performed on phantoms using an Elekta Synergy(®) beam modulator Linac equipped with a cone beam CT (CBCT), a HexaPOD RT couch top, and an iViewGT portal imaging system. Experiments conducted in this study tested reproducibility of the Sentinel surface acquisition and of set-up procedure, accuracy in quantifying known phantom mispositioning, and compared Sentinel, CBCT, and portal imaging system performance. Reproducibility of surface acquisition and setup procedure was better than 0.5 mm and 0.5° and 1 mm and 0.4°, respectively. The system accuracy was better than 1 mm and 1° when a Sentinel image was used as reference. A global worsening of Sentinel performance was observed using as reference an external surface extracted from CT study. This effect is probably due to small differences in considered surfaces, caused by different imaging modalities. The results obtained by testing the system on rigid phantoms were comparable to those obtained using CBCT and better than those obtained with conventional portal imaging systems. The Sentinel setup verification device is a reproducible and consistent system able to detect misalignments with accuracy better than 1 mm and 1°. When tested on rigid body phantoms, Sentinel and CBCT performed similarly. When compared to portal imaging, both Sentinel and CBCT were more accurate.

Image-guided, stereotactic perforator flap surgery: a prospective comparison of current techniques and review of the literature

Image-guided stereotaxy is a recent advancement in imaging technology, allowing computer guidance to aid surgical planning and accuracy. Despite the use of multiple techniques for patient registration in several surgical specialities, only fiducial marker registration has been described for use in soft tissue reconstructive surgery. The current study comprises an evaluation of the current techniques available for this purpose. A cohort of nine consecutive patients planned for elective free flaps were recruited, with the first five patients (four for the abdominal wall and one anterolateral thigh donor site) undergoing fiducial marker registration with a variable number of fiducial markers in order to determine the optimal number of fiducial markers to be used. Four subsequent patients undergoing perforator flap surgery underwent registration using three available registration modalities: fiducial marker registration, surface matching pointer/landmark and surface matching laser registration. For the abdominal wall, registration was not able to be achieved with five fiducial markers, and was successfully achieved in all cases with either six or seven fiducial markers. For the anterolateral thigh, registration was achieved with either nine or ten markers. The four patients who also underwent surface-landmark registration and 'Z-touch' laser surface matching registration all failed the registration process. Stereotactic navigation is a useful adjunct to the preoperative imaging of perforator flaps. Fiducial marker registration was able to be achieved in all cases, can be successfully achieved with a low and predictable number of fiducial markers, is highly accurate, and was the only reliable registration process in our experience.

Timing of Paired Points and Surface Matching Registration in Three-Dimensional (3D) Image-guided Spinal Surgery

Image-guidance can increase the safety and accuracy of spinal instrumentation placement. However, many spine surgeons are reluctant to incorporate spinal image-guidance into their surgical practice due to the perception that it is time-consuming and tedious, especially the task of vertebral registration. The authors evaluated the time required for paired points and surface matching registration when using the BrainLAB (BrainLAB, Westchester, IL) image-guided spine application for spinal surgery cases. The time required to register vertebral segments using paired points and surface matching techniques was assessed in 13 consecutive patients undergoing spinal fusions by the senior author. Overall, 23 vertebral segments were registered spanning from T1 to S1. Note was made of the vertebral segments that required reregistration due to poor accuracy. The average time required to register a single vertebral segment using the paired points and surface matching technique was 117 seconds (1 min 57 s). Average accuracy obtained was 0.9 mm. Inaccurate registration occurred in 3/23 (13%) of the segments requiring a second attempt at registration. In 3/23 (13%) of segments, adequate navigation accuracy was maintained on an adjacent vertebral segment thereby allowing for instrumentation to be placed in that adjacent segment without having to register that segment. Though associated with a learning curve, image-guidance can be used effectively and efficiently in spinal surgery. Average time required for registration of a vertebral segment using the BrainLAB spine application in this study was less than 2 minutes. The average accuracy obtained was 0.9 mm.

Accuracy evaluation of image registration and segmentation tools used in conformal treatment planning of prostate cancer

Segmentation and registration tools are commonly used in radiotherapy for target and at risk organs localisation. In this work the performances of three different segmentation tools and of a surface matching registration technique, used on computed tomography (CT) and magnetic resonance (MR) images for the treatment planning of conformal prostate carcinoma, are studied. The accuracy of the segmentation and registration tools was evaluated by phantom experiment and on patient data, respectively. A preliminary estimate of MR image distortion was also performed.

Correlation of SPECT and PET cardiac images by a surface matching registration technique

Complementary information provided by Single Photon and Positron Emission Tomography (SPECT and PET) in nuclear cardiology allows a better comprehension of the physiopathology of the heart. In this work a surface matching registration technique is evaluated in the spatial correlation of SPECT and PET cardiac images. The method is based on matching correspondent anatomical surfaces extracted from transmission (TR) SPECT and PET studies, usually performed for attenuation correction. The accuracy of the technique was evaluated by phantom experiments and on patient data ( 201Tl SPECT and 13NH 3 PET perfusion studies). An application of the method is presented for the correlation of SPECT 201Tl perfusion and PET 18FDG metabolic studies in the evaluation of myocardial viability.

Application of a surface matching image registration technique to the correlation of cardiac studies in positron emission tomography (PET) by transmission images

The aim of this work is to assess the accuracy of a surface matching registration (SMR) technique for the correlation of cardiac studies in positron emission tomography (PET). Registration parameters were estimated by matching corresponding body surfaces, extracted from transmission studies, aligned to the PET emission images to be correlated. The accuracy of the SMR technique in this specific application was assessed by computer simulations, phantom experiments and on clinical PET data. Registration accuracy was evaluated in relation to the body surfaces (external, internal and the combination of the two) used by the SMR method. Better results were found when matching shaped and irregular surfaces such as internal lung contours. The robustness of the method was verified for different counting statistics recorded in transmission images. A clinical validation of the SMR method was performed on fluorine-18-deoxyglucose PET cardiac studies.