High-Resolution Laser Surface Scanning for Patient Registration in Cranial Computer-Assisted Surgery

Clinical validation of the virtual splint registration workflow for craniomaxillofacial surgery

Accurate registration is vital to transfer the virtual surgical plan during surgery. This study's goal was to present and clinically validate a virtual splint registration workflow. Ten dentate patients requiring revision surgery were included. Specific inclusion criterion for this study was the presence of at least two osteosynthesis screws on the orbital rim from a previous surgery. Dedicated orthognathic surgery software was used to fuse the maxillary dental scan with the computed tomography and generate a dental splint, which was imported into the navigation software and augmented with fiducial markers. Registration points were indicated virtually and the augmented splint was three-dimensionally printed. Intraoperatively, the splint was fitted on the maxillary dentition and the fiducial markers were used for registration. Accuracy of the registration procedure was quantified by calculating the difference between the landmarks acquired by indicating the pre-existing osteosynthesis material with the navigation pointer and in the virtual planning software. After acquisition of the landmarks, the screws were removed and surgery proceeded according to plan. A median target registration error of 1.53 mm was found. The advantages of the virtual splint registration workflow are that it does not require extensive computer-aided design skills or repeated preoperative imaging, and is non-invasive.

Computed tomography and structured light imaging guided orthopedic navigation puncture system: effective reduction of intraoperative image drift and mismatch

Background

Image-guided surgical navigation systems are widely regarded as the benchmark for computer-assisted surgical robotic platforms, yet a persistent challenge remains in addressing intraoperative image drift and mismatch. It can significantly impact the accuracy and precision of surgical procedures. Therefore, further research and development are necessary to mitigate this issue and enhance the overall performance of these advanced surgical platforms.

Objective

The primary objective is to improve the precision of image guided puncture navigation systems by developing a computed tomography (CT) and structured light imaging (SLI) based navigation system. Furthermore, we also aim to quantifying and visualize intraoperative image drift and mismatch in real time and provide feedback to surgeons, ensuring that surgical procedures are executed with accuracy and reliability.

Methods

A CT-SLI guided orthopedic navigation puncture system was developed. Polymer bandages are employed to pressurize, plasticize, immobilize and toughen the surface of a specimen for surgical operations. Preoperative CT images of the specimen are acquired, a 3D navigation map is reconstructed and a puncture path planned accordingly. During surgery, an SLI module captures and reconstructs the 3D surfaces of both the specimen and a guiding tube for the puncture needle. The SLI reconstructed 3D surface of the specimen is matched to the CT navigation map via two-step point cloud registrations, while the SLI reconstructed 3D surface of the guiding tube is fitted by a cylindrical model, which is in turn aligned with the planned puncture path. The proposed system has been tested and evaluated using 20 formalin-soaked lower limb cadaver specimens preserved at a local hospital.

Results

The proposed method achieved image registration RMS errors of 0.576 ± 0.146 mm and 0.407 ± 0.234 mm between preoperative CT and intraoperative SLI surface models and between preoperative and postoperative CT surface models. In addition, preoperative and postoperative specimen surface and skeletal drifts were 0.033 ± 0.272 mm and 0.235 ± 0.197 mm respectively.

Conclusion

The results indicate that the proposed method is effective in reducing intraoperative image drift and mismatch. The system also visualizes intraoperative image drift and mismatch, and provides real time visual feedback to surgeons.

Regional-surface-based registration for image-guided neurosurgery: effects of scan modes on registration accuracy

PURPOSE

The conventional surface-based method only registers the facial zone with preoperative point cloud, resulting in low accuracy away from the facial area. Acquiring a point cloud of the entire head for registration can improve registration accuracy in all parts of the head. However, it takes a long time to collect a point cloud of the entire head. It may be more practical to selectively scan part of the head to ensure high registration accuracy in the surgical area of interest. In this study, we investigate the effects of different scan regions on registration errors in different target areas when using a surface-based registration method.

METHODS

We first evaluated the correlation between the laser scan resolution and registration accuracy to determine an appropriate scan resolution. Then, with the appropriate resolution, we explored the effects of scan modes on registration error in computer simulation experiments, phantom experiments and two clinical cases. The scan modes were designed based on different combinations of five zones of the head surface, i.e., the sphenoid-frontal zone, parietal zone, left temporal zone, right temporal zone and occipital zone. In the phantom experiment, a handheld scanner was used to acquire a point cloud of the head. A head model containing several tumors was designed, enabling us to calculate the target registration errors deep in the brain to evaluate the effect of regional-surface-based registration.

RESULT

The optimal scan modes for tumors located in the sphenoid-frontal, parietal and temporal areas are mode 4 (i.e., simultaneously scanning the sphenoid-frontal zone and the temporal zone), mode 4 and mode 6 (i.e., simultaneously scanning the sphenoid-frontal zone, the temporal zone and the parietal zone), respectively. For the tumor located in the occipital area, no modes were able to achieve reliable accuracy.

CONCLUSION

The results show that selecting an appropriate scan resolution and scan mode can achieve reliable accuracy for use in sphenoid-frontal, parietal and temporal area surgeries while effectively reducing the operation time.

A Surface-Based Spatial Registration Method Based on Sense Three-Dimensional Scanner

OBJECTIVE

The purpose of this study was to investigate the feasibility of a surface-based registration method based on a low-cost, hand-held Sense three-dimensional (3D) scanner in image-guided neurosurgery system.

METHODS

The scanner was calibrated prior and fixed on a tripod before registration. During registration, a part of the head surface was scanned at first and the spatial position of the adapter was recorded. Then the scanner was taken off from the tripod and the entire head surface was scanned by moving the scanner around the patient's head. All the scan points were aligned to the recorded spatial position to form a unique point cloud of the head by the automatic mosaic function of the scanner. The coordinates of the scan points were transformed from the device space to the adapter space by a calibration matrix, and then to the patient space. A 2-step patient-to-image registration method was then performed to register the patient space to the image space.

RESULTS

The experimental results showed that the mean target registration error of 15 targets on the surface of the phantom was 1.61±0.09 mm. In a clinical experiment, the mean target registration error of 7 targets on the patient's head surface was 2.50±0.31 mm, which was sufficient to meet clinical requirements.

CONCLUSIONS

It is feasible to use the Sense 3D scanner for patient-to-image registration, and the low-cost Sense 3D scanner can take the place of the current used scanner in the image-guided neurosurgery system.

Brain shift in neuronavigation of brain tumors: A review

PURPOSE

Neuronavigation based on preoperative imaging data is a ubiquitous tool for image guidance in neurosurgery. However, it is rendered unreliable when brain shift invalidates the patient-to-image registration. Many investigators have tried to explain, quantify, and compensate for this phenomenon to allow extended use of neuronavigation systems for the duration of surgery. The purpose of this paper is to present an overview of the work that has been done investigating brain shift.

METHODS

A review of the literature dealing with the explanation, quantification and compensation of brain shift is presented. The review is based on a systematic search using relevant keywords and phrases in PubMed. The review is organized based on a developed taxonomy that classifies brain shift as occurring due to physical, surgical or biological factors.

RESULTS

This paper gives an overview of the work investigating, quantifying, and compensating for brain shift in neuronavigation while describing the successes, setbacks, and additional needs in the field. An analysis of the literature demonstrates a high variability in the methods used to quantify brain shift as well as a wide range in the measured magnitude of the brain shift, depending on the specifics of the intervention. The analysis indicates the need for additional research to be done in quantifying independent effects of brain shift in order for some of the state of the art compensation methods to become useful.

CONCLUSION

This review allows for a thorough understanding of the work investigating brain shift and introduces the needs for future avenues of investigation of the phenomenon.

Surface matching for high-accuracy registration of the lateral skull base

PURPOSE

The accuracy achievable when utilizing image guidance depends to a large extent on the accuracy with which the patient can be registered to preoperative image data. This work proposes a method for the registration of the temporal bone based on surface matching and investigates the achievable accuracy of the technique.

METHODS

Fourteen human temporal bones were utilized for evaluation; incisions were made, fiducial screws were implanted to act as a ground truth, and imaging was performed. The positions of the fiducials and surface of the mastoid were extracted from image data and reference positions defined at the round window and the mastoid surface. The surface of the bone was then digitized using a tracked pointer within the region exposed by the incisions and the physical and image point clouds registered, with the result compared to the fiducial-based registration.

RESULTS

Results of one case were excluded due to a problem with the ground truth registration. In the remaining cases an accuracy of [Formula: see text] and [Formula: see text] mm was observed relative to the ground truth at the surface of the mastoid and round window, respectively.

CONCLUSIONS

A technique for the registration of the temporal bone was proposed, based on surface matching after exposure of the mastoid surface, and evaluated on human temporal bone specimens. The results reveal that high-accuracy patient-to-image registration is possible without the use of fiducial screws.

Multimodal navigated skull base tumor resection using image-based vascular and cranial nerve segmentation: A prospective pilot study

Background:

Skull base tumors frequently encase or invade adjacent normal neurovascular structures. For this reason, optimal tumor resection with incomplete knowledge of patient anatomy remains a challenge.

Methods:

To determine the accuracy and utility of image-based preoperative segmentation in skull base tumor resections, we performed a prospective study. Ten patients with skull base tumors underwent preoperative 3T magnetic resonance imaging, which included thin section three-dimensional (3D) space T2, 3D time of flight, and magnetization-prepared rapid acquisition gradient echo sequences. Imaging sequences were loaded in the neuronavigation system for segmentation and preoperative planning. Five different neurovascular landmarks were identified in each case and measured for accuracy using the neuronavigation system. Each segmented neurovascular element was validated by manual placement of the navigation probe, and errors of localization were measured.

Results:

Strong correspondence between image-based segmentation and microscopic view was found at the surface of the tumor and tumor-normal brain interfaces in all cases. The accuracy of the measurements was 0.45 ± 0.21 mm (mean ± standard deviation). This information reassured the surgeon and prevented vascular injury intraoperatively. Preoperative segmentation of the related cranial nerves was possible in 80% of cases and helped the surgeon localize involved cranial nerves in all cases.

Conclusion:

Image-based preoperative vascular and neural element segmentation with 3D reconstruction is highly informative preoperatively and could increase the vigilance of neurosurgeons for preventing neurovascular injury during skull base surgeries. Additionally, the accuracy found in this study is superior to previously reported measurements. This novel preliminary study is encouraging for future validation with larger numbers of patients.

A new markerless patient-to-image registration method using a portable 3D scanner

PURPOSE

Patient-to-image registration is critical to providing surgeons with reliable guidance information in the application of image-guided neurosurgery systems. The conventional point-matching registration method, which is based on skin markers, requires expensive and time-consuming logistic support. Surface-matching registration with facial surface scans is an alternative method, but the registration accuracy is unstable and the error in the more posterior parts of the head is usually large because the scan range is limited. This study proposes a new surface-matching method using a portable 3D scanner to acquire a point cloud of the entire head to perform the patient-to-image registration.

METHODS

A new method for transforming the scan points from the device space into the patient space without calibration and tracking was developed. Five positioning targets were attached on a reference star, and their coordinates in the patient space were measured prior. During registration, the authors moved the scanner around the head to scan its entire surface as well as the positioning targets, and the scanner generated a unique point cloud in the device space. The coordinates of the positioning targets in the device space were automatically detected by the scanner, and a spatial transformation from the device space to the patient space could be calculated by registering them to their coordinates in the patient space that had been measured prior. A three-step registration algorithm was then used to register the patient space to the image space. The authors evaluated their method on a rigid head phantom and an elastic head phantom to verify its practicality and to calculate the target registration error (TRE) in different regions of the head phantoms. The authors also conducted an experiment with a real patient's data to test the feasibility of their method in the clinical environment.

RESULTS

In the phantom experiments, the mean fiducial registration error between the device space and the patient space, the mean surface registration error, and the mean TRE of 15 targets on the surface of each phantom were 0.34 ± 0.01 mm and 0.33 ± 0.02 mm, 1.17 ± 0.02 mm and 1.34 ± 0.10 mm, and 1.06 ± 0.11 mm and 1.48 ± 0.21 mm, respectively. When grouping the targets according to their positions on the head, high accuracy was achieved in all parts of the head, and the TREs were similar across different regions. The authors compared their method with the current surface registration methods that use only a part of the facial surface on the elastic phantom, and the mean TRE of 15 targets was 1.48 ± 0.21 mm and 1.98 ± 0.53 mm, respectively. In a clinical experiment, the mean TRE of seven targets on the patient's head surface was 1.92 ± 0.18 mm, which was sufficient to meet clinical requirements.

CONCLUSIONS

The proposed surface-matching registration method provides sufficient registration accuracy even in the posterior area of the head. The 3D point cloud of the entire head, including the facial surface and the back of the head, can be easily acquired using a portable 3D scanner. The scanner does not need to be calibrated prior or tracked by the optical tracking system during scanning.

The silent loss of neuronavigation accuracy: a systematic retrospective analysis of factors influencing the mismatch of frameless stereotactic systems in cranial neurosurgery

BACKGROUND

Neuronavigation has become an intrinsic part of preoperative surgical planning and surgical procedures. However, many surgeons have the impression that accuracy decreases during surgery.

OBJECTIVE

To quantify the decrease of neuronavigation accuracy and identify possible origins, we performed a retrospective quality-control study.

METHODS

Between April and July 2011, a neuronavigation system was used in conjunction with a specially prepared head holder in 55 consecutive patients. Two different neuronavigation systems were investigated separately. Coregistration was performed with laser-surface matching, paired-point matching using skin fiducials, anatomic landmarks, or bone screws. The initial target registration error (TRE1) was measured using the nasion as the anatomic landmark. Then, after draping and during surgery, the accuracy was checked at predefined procedural landmark steps (Mayfield measurement point and bone measurement point), and deviations were recorded.

RESULTS

After initial coregistration, the mean (SD) TRE1 was 2.9 (3.3) mm. The TRE1 was significantly dependent on patient positioning, lesion localization, type of neuroimaging, and coregistration method. The following procedures decreased neuronavigation accuracy: attachment of surgical drapes (DTRE2 = 2.7 [1.7] mm), skin retractor attachment (DTRE3 = 1.2 [1.0] mm), craniotomy (DTRE3 = 1.0 [1.4] mm), and Halo ring installation (DTRE3 = 0.5 [0.5] mm). Surgery duration was a significant factor also; the overall DTRE was 1.3 [1.5] mm after 30 minutes and increased to 4.4 [1.8] mm after 5.5 hours of surgery.

CONCLUSION

After registration, there is an ongoing loss of neuronavigation accuracy. The major factors were draping, attachment of skin retractors, and duration of surgery. Surgeons should be aware of this silent loss of accuracy when using neuronavigation.

Facial plastic surgery area acquisition method based on point cloud mathematical model solution

It is one of the hot research problems nowadays to find a quick and accurate method of acquiring the facial plastic surgery area to provide sufficient but irredundant autologous or in vitro skin source for covering extensive wound, trauma, and burnt area. At present, the acquisition of facial plastic surgery area mainly includes model laser scanning, point cloud data acquisition, pretreatment of point cloud data, three-dimensional model reconstruction, and computation of area. By using this method, the area can be computed accurately, but it is hard to control the random error, and it requires a comparatively longer computation period. In this article, a facial plastic surgery area acquisition method based on point cloud mathematical model solution is proposed. This method applies symmetric treatment to the point cloud based on the pretreatment of point cloud data, through which the comparison diagram color difference map of point cloud error before and after symmetry is obtained. The slicing mathematical model of facial plastic area is got through color difference map diagram. By solving the point cloud data in this area directly, the facial plastic area is acquired. The point cloud data are directly operated in this method, which can accurately and efficiently complete the surgery area computation. The result of the comparative analysis shows the method is effective in facial plastic surgery area.

Intraoperative patient registration using volumetric true 3D ultrasound without fiducials

PURPOSE

Accurate patient registration is crucial for effective image-guidance in open cranial surgery. Typically, it is accomplished by matching skin-affixed fiducials manually identified in the operating room (OR) with their counterparts in the preoperative images, which not only consumes OR time and personnel resources but also relies on the presence (and subsequent fixation) of the fiducials during the preoperative scans (until the procedure begins). In this study, the authors present a completely automatic, volumetric image-based patient registration technique that does not rely on fiducials by registering tracked (true) 3D ultrasound (3DUS) directly with preoperative magnetic resonance (MR) images.

METHODS

Multistart registrations between binary 3DUS and MR volumes were first executed to generate an initial starting point without incorporating prior information on the US transducer contact point location or orientation for subsequent registration between grayscale 3DUS and MR via maximization of either mutual information (MI) or correlation ratio (CR). Patient registration was then computed through concatenation of spatial transformations.

RESULTS

In ten (N = 10) patient cases, an average fiducial (marker) distance error (FDE) of 5.0 mm and 4.3 mm was achieved using MI or CR registration (FDE was smaller with CR vs MI in eight of ten cases), which are comparable to values reported for typical fiducial- or surface-based patient registrations. The translational and rotational capture ranges were found to be 24.0 mm and 27.0° for binary registrations (up to 32.8 mm and 36.4°), 12.2 mm and 25.6° for MI registrations (up to 18.3 mm and 34.4°), and 22.6 mm and 40.8° for CR registrations (up to 48.5 mm and 65.6°), respectively. The execution time to complete a patient registration was 12-15 min with parallel processing, which can be significantly reduced by confining the 3DUS transducer location to the center of craniotomy in MR before registration (an execution time of 5 min is achievable).

CONCLUSIONS

Because common features deep in the brain and throughout the surgical volume of interest are used, intraoperative fiducial-less patient registration is possible on-demand, which is attractive in cases where preoperative patient registration is compromised (e.g., from loss∕movement of skin-affixed fiducials) or not possible (e.g., in cases of emergency when external fiducials were not placed in time). CR registration was more robust than MI (capture range about twice as big) and appears to be more accurate, although both methods are comparable to or better than fiducial-based registration in the patient cases evaluated. The results presented here suggest that 3DUS image-based patient registration holds promise for clinical application in the future.

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.

Neuro-navigation: An Adjunct in Craniofacial Surgeries: Our Experience

PURPOSE

Due to the destruction of osseous landmarks of the skull base or paranasal sinuses, the anatomical orientation during surgery of frontobasal or clival tumors with (para) nasal extension is often challenging. In this relation, Neuro-navigation guidance might be a useful tool. Here, we explored the use of Neuro-navigation in an interdisciplinary setting.

METHODS AND MATERIALS

The surgical series consists of 3 patients who underwent Lefort-I access osteotomy and surgical decompression of the tumor. The procedures were planned and assisted by neuro-navigation techniques with image fusion of CT and MRI. Two of the patients were diagnosed to have clival chordoma and one had extensive JNA.

RESULTS

The application of Neuro-navigation in the combined approaches was both safe and reliable for delineation of tumors and identification of vital structures hidden or encased by the tumors. There was no perioperative mortality. Tumors were either removed completely, or subtotal resection was achieved.

CONCLUSION

Craniofacial approaches with intra-operative neuro-navigational guidance in a multidisciplinary setting allow safe resection of large tumors of the upper clivus and the paranasal sinuses involving the anterior skull base. Complex skull base surgery with the involvement of bony structures appears to be an ideal field for advanced navigation techniques given the lack of intraoperative shift of relevant structures.

Classification and Analysis of the Errors in Neuronavigation

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

Image-guided surgical planning using anatomical landmarks in the retrosigmoid approach

OBJECTIVE

The suboccipital lateral or retrosigmoid approach is the main neurosurgical approach to the cerebellopontine angle (CPA). It is mainly used in the treatment of CPA tumors and vascular decompression of cranial nerves. A prospective study using navigation registered with anatomical landmarks in order to identify the transverse and sigmoid sinuses junction (TSSJ) was carried out in a series of 30 retrosigmoid craniotomies. The goal of this study was to determine the accuracy of this navigation technique and to establish the relationship between the location of the asterion and the TSSJ.

METHODS

From March through November 2008, 30 patients underwent a retrosigmoid craniotomy for removal of CPA tumors or for surgical treatment of neurovascular syndromes. Magnetic resonance imaging (MRI) T1 sequences with gadolinium (FSPGR with FatSst, 1.5 T GE Signa) and frameless navigation (Vector vision, Brainlab) were used for surgical planning. Registration was performed using six anatomical landmarks. The position of the TSSJ indicated by navigation was the landmark to guide the craniotomy. The location of the asterion was compared with the position of the TSSJ. After craniotomy, the real TSSJ position was compared with the virtual position, as demonstrated by navigation.

RESULTS

There were 19 cases of vestibular schwannomas, 5 petroclival meningiomas, 3 trigeminal neuralgias, 1 angioblastoma, 1 epidermoid cyst and 1 hemifacial spasm. In all cases, navigation enabled the location of the TSSJ and the emissary vein, with an accuracy flaw below 2 mm. The asterion was located directly over the TSSJ in only seven cases. One patient had a laceration of the sigmoid sinus during the craniotomy.

CONCLUSIONS

Navigation using anatomical landmarks for registration is a reliable method in the localization of the TSSJ for retrosigmoid craniotomies and thereby avoiding unnecessary sinus exposure. In addition, the method proved to be fast and accurate. The asterion was found to be a less accurate landmark for the localization of the TSSJ using navigation.

Surface-based facial scan registration in neuronavigation procedures: a clinical study

OBJECT

Surface-based registration (SBR) with facial surface scans has been proposed as an alternative for the commonly used fiducial-based registration in image-guided neurosurgery. Recent studies comparing the accuracy of SBR and fiducial-based registration have been based on a few targets located on the head surface rather than inside the brain and have yielded contradictory conclusions. Moreover, no visual feedback is provided with either method to inform the surgeon about the estimated target registration error (TRE) at various target locations. The goals in the present study were: 1) to quantify the SBR error in a clinical setup, 2) to estimate the targeting error for many target locations inside the brain, and 3) to create a map of the estimated TRE values superimposed on a patient's head image.

METHODS

The authors randomly selected 12 patients (8 supine and 4 in a lateral position) who underwent neurosurgery with a commercial navigation system. Intraoperatively, scans of the patients' faces were acquired using a fast 3D surface scanner and aligned with their preoperative MR or CT head image. In the laboratory, the SBR accuracy was measured on the facial zone and estimated at various intracranial target locations. Contours related to different TREs were superimposed on the patient's head image and informed the surgeon about the expected anisotropic error distribution.

RESULTS

The mean surface registration error in the face zone was 0.9 +/- 0.35 mm. The mean estimated TREs for targets located 60, 105, and 150 mm from the facial surface were 2.0, 3.2, and 4.5 mm, respectively. There was no difference in the estimated TRE between the lateral and supine positions. The entire registration procedure, including positioning of the scanner, surface data acquisition, and the registration computation usually required < 5 minutes.

CONCLUSIONS

Surface-based registration accuracy is better in the face and frontal zones, and error increases as the target location lies further from the face. Visualization of the anisotropic TRE distribution may help the surgeon to make clinical decisions. The observed and estimated accuracies and the intraoperative registration time show that SBR using the fast surface scanner is practical and feasible in a clinical setup.

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.

Image overlay navigation by markerless surface registration in gastrointestinal, hepatobiliary and pancreatic surgery

BACKGROUND

We applied a new concept of "image overlay surgery" consisting of the integration of virtual reality (VR) and augmented reality (AR) technology, in which dynamic 3D images were superimposed on the patient's actual body surface and evaluated as a reference for surgical navigation in gastrointestinal, hepatobiliary and pancreatic surgery.

METHODS

We carried out seven surgeries, including three cholecystectomies, two gastrectomies and two colectomies. A Macintosh and a DICOM workstation OsiriX were used in the operating room for image analysis. Raw data of the preoperative patient information obtained via MDCT were reconstructed to volume rendering and projected onto the patient's body surface during the surgeries. For accurate registration, OsiriX was first set to reproduce the patient body surface, and the positional coordinates of the umbilicus, left and right nipples, and the inguinal region were fixed as physiological markers on the body surface to reduce the positional error.

RESULTS

The registration process was non-invasive and markerlesss, and was completed within 5 min. Image overlay navigation was helpful for 3D anatomical understanding of the surgical target in the gastrointestinal, hepatobiliary and pancreatic anatomies. The surgeon was able to minimize movement of the gaze and could utilize the image assistance without interfering with the forceps operation, reducing the gap from the VR. Unexpected organ injury could be avoided in all procedures. In biliary surgery, the projected virtual cholangiogram on the abdominal wall could advance safely with identification of the bile duct. For early gastric and colorectal cancer, the small tumors and blood vessels, which usually could not be found on the gastric serosa by laparoscopic view, were simultaneously detected on the body surface by carbon dioxide-enhanced MDCT. This provided accurate reconstructions of the tumor and involved lymph node, directly linked with optimization of the surgical procedures.

CONCLUSIONS

Our non-invasive markerless registration using physiological markers on the body surface reduced logistical efforts. The image overlay technique is a useful tool when highlighting hidden structures, giving more information.

Comparison of different registration methods for surgical navigation in cranio-maxillofacial surgery

BACKGROUND

Surgical navigation requires registration of the pre-operative image dataset with the patient in the operation theatre. Various marker and marker-free registration techniques are available, each bearing an individual level of precision and clinical practicability. In this study the precision of four different registration methods in a maxillofacial surgical setting is analyzed.

MATERIALS AND METHODS

A synthetic full size human skull model was registered with its computer tomography-dataset using (a) a dentally mounted occlusal splint, (b) the laser surface scanning, (c) five facial bone implants and (d) a combination of dental splint and two orbital bone implants. The target registration error was computed for 170 landmarks spread over the entire viscero- and neurocranium in 10 repeats using the VectorVision2 (BrainLAB AG, Heimstetten, Germany) navigation system. Statistical and graphical analyses were performed by anatomical region.

RESULTS

An average precision of 1mm was found for the periorbital region irrespective of registration method (range 0.6-1.1mm). Beyond the mid-face, precision linearly decreases with the distance from the reference markers. The combination of splint with two orbital bone markers significantly improved precision from 1.3 to 0.8mm (p<0.001) on the viscerocranium and 2.3-1.2mm (p<0.001) on the neurocranium.

CONCLUSIONS

An occlusal splint alone yields poor precision for navigation beyond the mid-face. The precision can be increased by combining an occlusal splint with just two bone implants inserted percutaneously on the lateral orbital rim of each side.

Three-dimensional and thermal surface imaging produces reliable measures of joint shape and temperature: a potential tool for quantifying arthritis

Introduction

The assessment of joints with active arthritis is a core component of widely used outcome measures. However, substantial variability exists within and across examiners in assessment of these active joint counts. Swelling and temperature changes, two qualities estimated during active joint counts, are amenable to quantification using noncontact digital imaging technologies. We sought to explore the ability of three dimensional (3D) and thermal imaging to reliably measure joint shape and temperature.

Methods

A Minolta 910 Vivid non-contact 3D laser scanner and a Meditherm med2000 Pro Infrared camera were used to create digital representations of wrist and metacarpalphalangeal (MCP) joints. Specialized software generated 3 quantitative measures for each joint region: 1) Volume; 2) Surface Distribution Index (SDI), a marker of joint shape representing the standard deviation of vertical distances from points on the skin surface to a fixed reference plane; 3) Heat Distribution Index (HDI), representing the standard error of temperatures. Seven wrists and 6 MCP regions from 5 subjects with arthritis were used to develop and validate 3D image acquisition and processing techniques. HDI values from 18 wrist and 9 MCP regions were obtained from 17 patients with active arthritis and compared to data from 10 wrist and MCP regions from 5 controls. Standard deviation (SD), coefficient of variation (CV), and intraclass correlation coefficients (ICC) were calculated for each quantitative measure to establish their reliability. CVs for volume and SDI were <1.3% and ICCs were greater than 0.99.

Results

Thermal measures were less reliable than 3D measures. However, significant differences were observed between control and arthritis HDI values. Two case studies of arthritic joints demonstrated quantifiable changes in swelling and temperature corresponding with changes in symptoms and physical exam findings.

Conclusion

3D and thermal imaging provide reliable measures of joint volume, shape, and thermal patterns. Further refinement may lead to the use of these technologies to improve the assessment of disease activity in arthritis.

Evaluation of bone surface registration applying a micro-needle array

AIM

In this study we present and evaluated a new registration technology for the jaw-bone surface. It is based on a micromechatronic device for the generation of a "mechanical image" of the bone surface by means of an array of micro-needles that are penetrating the soft tissue until they touch the surface of the bone. This "mechanical impression image" is aligned with the CT data set.

MATERIAL AND METHODS

Based on laboratory measurements on 10 specially prepared jawbone models we evaluate the accuracy of this new registration method.

RESULTS

Our measurements of the 10 specimens revealed a maximum overall location error of 0.97 mm (range: 0.35-0.97 mm).

CONCLUSIONS

From the technical point of view the presented registration technology has the potential to improve the performance (i.e. accuracy and avoidance of errors) of the registration process for bony structures in selected applications of image-guided surgery.

Comparison of 4 registration strategies for computer-aided maxillofacial surgery

PURPOSE

Surgeons have recently started to use computer-aided surgery (CAS) to assist with maxillofacial reconstructive surgery. This study evaluates four different CAS registration strategies in the maxillofacial skeleton.

MATERIALS AND METHODS

Fifteen fiducial markers were placed on each of four cadaveric heads. Four registration protocols were used: 1) group 1-invasive markers, 2) group 2-skin surface, 3) group 3-bony landmark, 4) group 4-intraoral splint. Two observers registered each head twice with each of the four protocols and measured the target registration error (TRE). The process was repeated on two different navigation systems for confirmation.

RESULTS

The mean TRE values were: invasive, 1.13 +/- 0.05 mm (P < 0.05); skin, 2.03 +/- 0.07 mm (P < 0.05); bone, 3.17 +/- 0.10 mm (P < 0.05); and splint, 3.79 +/- 0.13 mm (P < 0.05). The TRE values were consistent across CAS systems.

CONCLUSION

Of the techniques tested for CAS registration, invasive fiducial markers are the most accurate. Skin surface landmarks, bony landmarks, and an intraoral splint are incrementally less accurate.

Navigated control in functional endoscopic sinus surgery

This study designs and evaluates a mechatronic system to assist ENT surgery, taking as an example a navigation controlled shaver as used in paranasal sinus surgery. The on/off status of the shaver is regulated automatically, depending on the current position of the shaver tip. The working space for the navigation controlled shaver is planned preoperatively as a three-dimensional model and is based on the individual patient's CT data. Within this area the shaver reacts to signals from the surgeon. If the tip of the shaver moves outside the predefined working space, the shaver's automatic drive control is interrupted by an electrical pulse. The planning software was evaluated using CT data sets from 32 patients. The accuracy of the registration was analysed on an anatomical model with the aid of 451 measurements on titanium screws attached endonasally, whilst the implementation of the working space was evaluated on 5 technical models. The average time taken for segmenting the working space was found to be 4.23 minutes. The average accuracy of the shaver registration was 1.08 mm. The selected cavity was to be resected without any restrictions. The preoperatively determined working space was implemented with a mean deviation of 3.1 mm over all levels. The study proves the feasibility of a mechatronic assistance system taking as an example the navigation controlled shaver used in paranasal sinus surgery. In contrast to isolated CAS solutions, this conceptual approach provides for the redundancy of the surgeon and eases their cognitive burden. We can foresee numerous applications in ENT surgery of the future following the principle presented here, in the control systems of power tools such as cutters, high frequency scalpels and lasers.

Comparison of laser surface scanning and fiducial marker–based registration in frameless stereotaxy

✓The authors compared the accuracy of laser surface scanning patient registration using the commercially available Fazer (Medtronic, Inc.) with the conventional registration procedure based on fiducial markers (FMs) in computer-assisted surgery.

Four anatomical head specimens were prepared with 10 titanium microscrews placed at defined locations and scanned with a 16-slice spiral computed tomography unit. To compare the two registration methods, each method was applied five times for each cadaveric specimen; thus data were obtained from 40 registrations. Five microscrews (selected following a randomization protocol) were used for each FM-based registration; the other five FMs were selected for coordinate measurements by touching with a point measurement stylus. Coordinates of these points were also measured manually on the screen of the navigation computer. Coordinates were measured in the same manner after laser surface registration.

The root mean square error as calculated by the navigation system ranged from 1.3 to 3.2 mm (mean 1.8 mm) with the Fazer and from 0.3 to 1.8 mm (mean 1.0 mm) with FM-based registration. The overall mean deviations (the arithmetic mean of the mean deviations of measurements on the four specimens) were 3.0 mm (standard deviation [SD] range 1.4–2.6 mm) with the Fazer and 1.4 mm (SD range 0.4–0.9 mm) with the FMs. The Fazer registration scans 300 surface points. Statistical tests showed the difference in the accuracy of these methods to be highly significant.

In accordance with the findings of other groups, the authors concluded that the inclusion of a larger number of registration points might improve the accuracy of Fazer registration.

Image-guided surgery and medical robotics in the cranial area

Surgery in the cranial area includes complex anatomic situations with high-risk structures and high demands for functional and aesthetic results. Conventional surgery requires that the surgeon transfers complex anatomic and surgical planning information, using spatial sense and experience. The surgical procedure depends entirely on the manual skills of the operator. The development of image-guided surgery provides new revolutionary opportunities by integrating presurgical 3D imaging and intraoperative manipulation. Augmented reality, mechatronic surgical tools, and medical robotics may continue to progress in surgical instrumentation, and ultimately, surgical care. The aim of this article is to review and discuss state-of-the-art surgical navigation and medical robotics, image-to-patient registration, aspects of accuracy, and clinical applications for surgery in the cranial area.

Real-time 3D-surface-guided head refixation useful for fractionated stereotactic radiotherapy

Accurate and precise head refixation in fractionated stereotactic radiotherapy has been achieved through alignment of real-time 3D-surface images with a reference surface image. The reference surface image is either a 3D optical surface image taken at simulation with the desired treatment position, or a CT/MRI-surface rendering in the treatment plan with corrections for patient motion during CT/MRI scans and partial volume effects. The real-time 3D surface images are rapidly captured by using a 3D video camera mounted on the ceiling of the treatment vault. Any facial expression such as mouth opening that affects surface shape and location can be avoided using a new facial monitoring technique. The image artifacts on the real-time surface can generally be removed by setting a threshold of jumps at the neighboring points while preserving detailed features of the surface of interest. Such a real-time surface image, registered in the treatment machine coordinate system, provides a reliable representation of the patient head position during the treatment. A fast automatic alignment between the real-time surface and the reference surface using a modified iterative-closest-point method leads to an efficient and robust surface-guided target refixation. Experimental and clinical results demonstrate the excellent efficacy of <2 min set-up time, the desired accuracy and precision of <1 mm in isocenter shifts, and <1 degree in rotation.