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

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

BACKGROUND

Neuronavigation has become an essential system for brain tumor resections. It is sometimes difficult to obtain accurate registration of the neuronavigation with the patient in the prone position. 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 sectioned 3-dimensional (3D) virtual skull in a neuronavigation system and registered with a sectioned 3D skull. In this study, the bony surface-matching with sectioned 3D registration is applied to provide precise registration for brain tumor resection in the prone position.

METHODS

From May 2023 to April 2024, 17 patients who underwent brain tumor resection in the prone position were enrolled. The navigation system StealthStation S8 (Medtronic, Dublin, Ireland) was used. Bony surface-matching registration with a whole 3D skull in a neuronavigation system was performed. Next, a sectioned 3D skull was made according to the surgical location to compare with the whole 3D skull registration. A phantom model was also used to validate the whole and sectioned 3D skull registration.

RESULTS

Whole 3D skull registration was successful for only 2 patients (11.8%). However, sectioned 3D skull registration was successful for 16 patients (94.1%). The examinations with a phantom skull model also showed superiority of sectioned 3D skull registration to whole 3D skull registration.

CONCLUSIONS

Sectioned 3D skull registration was superior to whole 3D skull registration. The sectioned 3D skull method could provide accurate registration with limited exposed bone.

An incremental registration method for endoscopic sinus and skull base surgery navigation: From phantom study to clinical trials

PURPOSE

Surface-based image-to-patient registration in current surgical navigation is mainly achieved by a 3D scanner, which has several limitations in clinical practice such as uncontrollable scanning range, complicated operation, and even high failure rate. An accurate, robust, and easy-to-perform image-to-patient registration method is urgently required.

METHODS

An incremental point cloud registration method was proposed for surface-based image-to-patient registration. The point cloud in image space was extracted from the computed tomography (CT) image, and a template matching method was applied to remove the redundant points. The corresponding point cloud in patient space was incrementally collected by an optically tracked pointer, while the nearest point distance (NPD) constraint was applied to ensure the uniformity of the collected points. A coarse-to-fine registration method under the constraints of coverage ratio (CR) and outliers ratio (OR) was then proposed to obtain the optimal rigid transformation from image to patient space. The proposed method was integrated in the recently developed endoscopic navigation system, and phantom study and clinical trials were conducted to evaluate the performance of the proposed method.

RESULTS

The results of the phantom study revealed that the proposed constraints greatly improved the accuracy and robustness of registration. The comparative experimental results revealed that the proposed registration method significantly outperform the scanner-based method, and achieved comparable accuracy to the fiducial-based method. In the clinical trials, the average registration duration was 1.24 ± 0.43 min, the target registration error (TRE) of 294 marker points (59 patients) was 1.25 ± 0.40 mm, and the lower 97.5% confidence limit of the success rate of positioning marker points exceeds the expected value (97.56% vs. 95.00%), revealed that the accuracy of the proposed method significantly met the clinical requirements (TRE ⩽ 2 mm, p < 0.05).

CONCLUSIONS

The proposed method has both the advantages of high accuracy and convenience, which were absent in the scanner-based method and the fiducial-based method. Our findings will help improve the quality of endoscopic sinus and skull base surgery.

"Image to patient" equal-resolution surface registration supported by a surface scanner: analysis of algorithm efficiency for computer-aided surgery

PURPOSE

The "image to patient" registration procedure is crucial for the accuracy of surgical instrument tracking relative to the medical image while computer-aided surgery. The main aim of this work was to create an equal-resolution surface registration algorithm (ERSR) and analyze its efficiency.

METHODS

The ERSR algorithm provides two datasets with equal, high resolution and approximately corresponding points. The registered sets are obtained by projection of a user-designed rectangle(s)-shaped uniform clouds of points on DICOM and surface scanner datasets. The tests of the algorithm were performed on a phantom with titanium microscrews. We analyzed the influence of DICOM resolution on the effect of the ERSR algorithm and compared the ERSR to standard paired-points landmark transform registration. The methods of analysis were Target Registration Error, distance maps, and their histogram evaluation.

RESULTS

The mean TRE in case of ERSR equaled 0.8 ± 0.3 mm (resolution A), 0.8 ± 0.5 mm (resolution B), and 1.0 ± 0.7 mm (resolution C). The mean values were at least 0.4 mm lower than in the case of landmark transform registration. The distance maps between the model achieved from the scanner and the CT-based model were analyzed by histogram. The frequency of the first bin in a histogram of the distance map for ERSR was about 0.6 for all three resolutions of DICOM dataset and three times higher than in the case of landmark transform registration. The results were statistically analyzed using the Wilcoxon signed-rank test (alpha = 0.05).

CONCLUSION

The tests proved a statistically significant higher efficiency of equal resolution surface registration related to the landmark transform algorithm. It was proven that the lower resolution of the CT DICOM dataset did not degrade the efficiency of the ERSR algorithm. We observed a significantly lower response to decreased resolution than in the case of paired-points landmark transform registration.

Automated Machine Learning (AutoML)-based Surface Registration Methodology for Image-guided Surgical Navigation System

BACKGROUND

While the surface registration technique has the advantage of being relatively safe and the operation time is short, it generally has the disadvantage of low accuracy.

PURPOSE

This research proposes automated machine learning (AutoML)-based surface registration to improve the accuracy of image-guided surgical navigation systems.

METHODS

The state-of-the-art surface registration concept is that first, using a neural network model, a new point-cloud that matches the facial information acquired by a passive probe of an optical tracking system (OTS) is extracted from the facial information obtained by computerized tomography (CT). Target registration error (TRE) representing the accuracy of surface registration is then calculated by applying the iterative closest point (ICP) algorithm to the newly extracted point-cloud and OTS information. In this process, the hyperparameters used in the neural network model and ICP algorithm are automatically optimized using Bayesian Optimization with Expected Improvement to yield improved registration accuracy.

RESULTS

Using the proposed surface registration methodology, the average TRE for the targets located in the sinus space and nasal cavity of the soft phantoms is (0.939 ± 0.375) mm, which shows 57.8 % improvement compared to the average TRE of (2.227 ± 0.193) mm calculated by the conventional surface registration method (p < 0.01). The performance of the proposed methodology is evaluated, and the average TREs computed by the proposed methodology and the conventional method are (0.767 ± 0.132) mm and (2.615 ± 0.378) mm, respectively. Additionally, for one healthy adult, the clinical applicability of the AutoML-based surface registration is also presented.

CONCLUSION

Our findings showed that the registration accuracy could be improved while maintaining the advantages of the surface registration technique. This article is protected by copyright. All rights reserved.

Calibrating 3D Scanner in the Coordinate System of Optical Tracker for Image-To-Patient Registration

Three-dimensional scanners have been widely applied in image-guided surgery (IGS) given its potential to solve the image-to-patient registration problem. How to perform a reliable calibration between a 3D scanner and an external tracker is especially important for these applications. This study proposes a novel method for calibrating the extrinsic parameters of a 3D scanner in the coordinate system of an optical tracker. We bound an optical marker to a 3D scanner and designed a specified 3D benchmark for calibration. We then proposed a two-step calibration method based on the pointset registration technique and nonlinear optimization algorithm to obtain the extrinsic matrix of the 3D scanner. We applied repeat scan registration error (RSRE) as the cost function in the optimization process. Subsequently, we evaluated the performance of the proposed method on a recaptured verification dataset through RSRE and Chamfer distance (CD). In comparison with the calibration method based on 2D checkerboard, the proposed method achieved a lower RSRE (1.73 mm vs. 2.10, 1.94, and 1.83 mm) and CD (2.83 mm vs. 3.98, 3.46, and 3.17 mm). We also constructed a surgical navigation system to further explore the application of the tracked 3D scanner in image-to-patient registration. We conducted a phantom study to verify the accuracy of the proposed method and analyze the relationship between the calibration accuracy and the target registration error (TRE). The proposed scanner-based image-to-patient registration method was also compared with the fiducial-based method, and TRE and operation time (OT) were used to evaluate the registration results. The proposed registration method achieved an improved registration efficiency (50.72 ± 6.04 vs. 212.97 ± 15.91 s in the head phantom study). Although the TRE of the proposed registration method met the clinical requirements, its accuracy was lower than that of the fiducial-based registration method (1.79 ± 0.17 mm vs. 0.92 ± 0.16 mm in the head phantom study). We summarized and analyzed the limitations of the scanner-based image-to-patient registration method and discussed its possible development.

A robust automated surface-matching registration method for neuronavigation

PURPOSE

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.

METHODS

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.

RESULTS

Keypoints were extracted within 7.0 s 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 ± 0.04 mm, and the average registration time was 17.4 s. 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 ± 0.32 mm, 1.83 ± 0.38 mm, 1.64 ± 0.3 mm, 1.67 ± 0.35 mm, and 1.72 ± 0.31 mm, respectively, and the average registration time was 21.4 s. 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.

CONCLUSIONS

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.

An Automatic Spatial Registration Method for Image-Guided Neurosurgery System

OBJECTIVE

This study aimed to investigate the feasibility of an automatic marker-free patient-to-image spatial registration method based on the 4-points congruent sets (4PCS) and iterative closest point (ICP) algorithm for the image-guided neurosurgery system (IGNS).

METHODS

A portable scanner was used to obtain the point cloud of the patient's entire head. The 4PCS algorithm, which is resilient to noise and outliers, automatically registered the point cloud in the patient space to the surface reconstructed from the patient's preoperative images in the image space without any assumptions about initial alignment. A variant of the ICP algorithm was then used to finish the fine registration. Two phantoms and 3 patients' experiments were performed to demonstrate the effectiveness of the proposed method.

RESULTS

In the phantom experiments, the mean target registration error of 15 targets on the surface of the rigid and the elastic phantoms were 1.02 ± 0.18 mm and 1.27 ± 0.36 mm, respectively. In the clinical experiments, the mean target registration error of 7 targets on the first, second and third patient's head were 1.88 ± 0.19 mm, 1.84 ± 0.19 mm, and 1.89 ± 0.18 mm, respectively, which was sufficient to meet clinical requirements. The registration accuracy and registration time using the proposed method are better than that using the method based on manually coarse registration and automatic fine registration.

CONCLUSIONS

It is feasible to use the automatic spatial registration method based on the 4PCS and ICP algorithm for the IGNS. Moreover, it can replace the spatial registration method based on manually selected anatomical landmarks combined with the automatic fine registration in the currently used IGNS.

The possibilities of a portable low-budget three-dimensional stereophotogrammetry system in neonates: a prospective growth analysis and analysis of accuracy

Background

With the technical development, portable three-dimensional (3D) photogrammetry systems are becoming more en vogue because of cost-effectiveness and comparable accuracy to common stationary 3D systems. The purpose of the study was to evaluate the feasibility and accuracy of a low-budget portable system for 3D image acquisition with special regard to the gracile nasal region in neonates. Furthermore, the study aimed to establish a 3D data set of the first 180 days post partum.

Methods

Thirty-three healthy, full-term newborn were enrolled and 3D photographs were prospectively taken monthly with a portable low-budget 3D stereophotogrammetry system (FUEL3D® SCANIFY®) for six months. In the third month, age-matched and corresponding 3D models were acquired by taking an impression of the perinasal area. The resulting plaster models were scanned (3Shape D700, 3Shape® A/S, Denmark). Three examiners analyzed independently 21 defined landmarks of the generated Standard Tessellation Language files with regard to accuracy by using 3dMDvultus™ software. A semi-automatic 3D best-fit analysis of 3D photo and plaster models were performed by using Geomagic® and the Root Mean Squared (RMS) errors were calculated.

Results

Statistically significant changes of midfacial distances and angles with a focus on nasal growth during the first 180 days postpartum could be specified in absolute and relative dimensions. Best-fit analysis in the third month revealed a RMS error of 0.72 ± 0.22 mm with a mean standard deviation of 0.71 ± 0.21 mm.

Conclusions

The analyzed portable 3D stereophotogrammetry system is a feasible methodology with good accuracy, even in newborn. A description of the growth as well as the establishment of a 3D data set was performed. Its implementation for basic documentation for example in cleft patients is possible and might reduce the need for impressions and facilitate the communications with parents and the interdisciplinary team.

Chances and limitations of a low-cost mobile 3D scanner for breast imaging in comparison to an established 3D photogrammetric system

BACKGROUND

In search of new possibilities in 3D surface imaging, several nonmedical scanning systems have been assessed for their implementation in plastic surgery. The aim of this study was to compare a new affordable 3D imaging consumer product with an established medical 3D imaging system for objective 3D breast imaging.

METHOD

We compared a low-cost mobile, handheld scanner against an established medical 3D surface imaging system. Forty-two female patients who underwent different types of breast surgery were captured in a 3D view with both devices. Digital breast measurement, volume measurement, and breast surface-to-surface analysis were done using Mirror software. Repeatability was assessed by repeated 3D scans of the torso and surface-to-surface analysis.

RESULTS

Digital breast measurement showed low differences with good-to-excellent correlation between both devices. Mean breast volume difference was small (-5.11 ± 32.10 mL) within the 95% limits of agreement. Surface-to-surface analysis yielded a higher surface deviation in the lower breast quadrants (1.62 ± 0.80 mm root mean square [RMS] error and 1.81 ± 0.88 mm RMS error) than in the upper breast quadrants. Repeatability was satisfactory with a mean of 0.636 ± 0.279 mm RMS error.

CONCLUSION

Affordable mobile surface scanners may offer new perspectives in the future for 3D breast imaging. Although surface acquisition was sufficient for breast measurements in comparison to an established system, the lack of appropriate medical software for patient consultation next to moderate texture quality needs to be improved for wider acceptance in plastic surgery.