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

2021 ◽  
pp. 2140009
Author(s):  
XINRONG CHEN ◽  
FUMING YANG ◽  
ZIQUN ZHANG ◽  
BAODAN BAI ◽  
LEI GUO
Keyword(s):  
Arbitrary Point ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Surface Registration ◽  
Surface Matching ◽  
Probability Density Distribution ◽  
Registration Method ◽  
Structure Information ◽  
Registration Error ◽  
Image Guided

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 Novel Surface Registration for Image-Guided Neurosurgery: Effects of Intervals of Points in Patient Space on Registration Accuracy

2020 ◽  
Vol 10 (6) ◽  
pp. 1466-1472
Author(s):  
Hakje Yoo ◽  
Ahnryul Choi ◽  
Hyunggun Kim ◽  
Joung Hwan Mun
Keyword(s):  
Success Rate ◽  
Point Cloud ◽  
Surgical Navigation ◽  
Spline Interpolation ◽  
Surface Registration ◽  
Registration Accuracy ◽  
Cubic Spline Interpolation ◽  
Registration Error ◽  
Target Registration Error ◽  
Image Guided

Surface registration is an important factor in surgical navigation in determining the success rate and stability of surgery by providing the operator with the exact location of a lesion. The problem of surface registration is that point cloud in the patient space is acquired at irregular intervals due to the operator’s tracking speed and method. The purpose of this study is to analyze the effect of irregular intervals of point cloud caused by tracking speed and method on the registration accuracy. For this study, we created the head phantom to obtain a point cloud in the patient space with a similar object to that of a patient and acquired a point cloud in a total of ten times. In order to analyze the accuracy of registration according to the interval, cubic spline interpolation was applied to the existing point cloud. Additionally, irregular intervals of the point cloud were regenerated into regular intervals. As a result of applying the regenerated point cloud to the surface registration, the surface registration error was not statistically different from the existing point cloud. However, the target registration error significantly lower (p < 0.01). These results indicate that the intervals of point cloud affect the accuracy of registration, and that point cloud with regular intervals can improve the surface registration accuracy.

Application accuracy in frameless image-guided neurosurgery: a comparison study of three patient-to-image registration methods

2007 ◽  
Vol 106 (6) ◽  
pp. 1012-1016 ◽  
Author(s):  
Peter A. Woerdeman ◽  
Peter W. A. Willems ◽  
Herke J. Noordmans ◽  
Cornelis A. F. Tulleken ◽  
Jan Willem Berkelbach van der Sprenkel
Keyword(s):  
Image Registration ◽  
Target Location ◽  
Imaging Modality ◽  
Fiducial Marker ◽  
Optical Tracking ◽  
Surface Matching ◽  
Frameless Stereotaxy ◽  
Anatomical Landmarks ◽  
Registration Method ◽  
Image Guided

Object The aim of this study was to compare three patient-to-image registration methods in frameless stereotaxy in terms of their application accuracy (the accuracy with which the position of a target can be determined intraoperatively). In frameless stereotaxy, imaging information is transposed to the surgical field to show the spatial position of a localizer or surgical instrument. The mathematical relationship between the image volume and the surgical working space is calculated using a rigid body transformation algorithm, based on point-pair matching or surface matching. Methods Fifty patients who were scheduled to undergo a frameless image-guided neurosurgical procedure were included in the study. Prior to surgery, the patients underwent either computerized tomography (CT) scanning or magnetic resonance (MR) imaging with widely distributed adhesive fiducial markers on the scalp. An extra fiducial marker was placed on the head as a target, as near as possible to the intracranial lesion. Prior to each surgical procedure, an optical tracking system was used to perform three separate patient-to-image registration procedures, using anatomical landmarks, adhesive markers, or surface matching. Subsequent to each registration, the target registration error (TRE), defined as the Euclidean distance between the image space coordinates and world space coordinates of the target marker, was determined. Independent of target location or imaging modality, mean application accuracy (± standard deviation) was 2.49 ± 1.07 mm when using adhesive markers. Using the other two registration strategies, mean TREs were significantly larger (surface matching, 5.03 ± 2.30 mm; anatomical landmarks, 4.97 ± 2.29 mm; p < 0.001 for both). Conclusions The results of this study show that skin adhesive fiducial marker registration is the most accurate noninvasive registration method. When images from an earlier study are to be used and accuracy may be slightly compromised, anatomical landmarks and surface matching are equally accurate alternatives.

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

2019 ◽  
Vol 17 (4) ◽  
pp. 403-412 ◽  
Author(s):  
Federico G Legnani ◽  
Andrea Franzini ◽  
Luca Mattei ◽  
Andrea Saladino ◽  
Cecilia Casali ◽  
...  
Keyword(s):  
Diagnostic Yield ◽  
Surface Registration ◽  
Reference Image ◽  
Stereotactic Brain Biopsy ◽  
Tissue Samples ◽  
Registration Method ◽  
Image Guided ◽  
Guided Biopsy ◽  
Surgical Data ◽  
Intracranial Lesions

Abstract BACKGROUND 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. OBJECTIVE 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. METHODS 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. RESULTS 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). CONCLUSION 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.

scholarly journals Image-Guided Endoscopic Sinus Surgery with 3D Volumetric Visualization of the Nasal Cavity and Paranasal Sinuses: A Clinical Comparative Study

2021 ◽  
Vol 11 (8) ◽  
pp. 3675
Author(s):  
Sang-Jeong Lee ◽  
Ji-Yong Yoo ◽  
Sung-Keun Yoo ◽  
Ryun Ha ◽  
Dong-Hyuk Lee ◽  
...  
Keyword(s):  
Comparative Study ◽  
Nasal Cavity ◽  
Paranasal Sinuses ◽  
Endoscopic Sinus Surgery ◽  
Sinus Surgery ◽  
Registration Method ◽  
Registration Error ◽  
Image Guided ◽  
System P ◽  
Leave One Out

(1) Background: The purpose of this study was to develop an image-guided endoscopic sinus surgery (IGESS) system, named Medigator®, based on the leave-one-out registration strategy and three-dimensional (3D) volumetric visualization of the nasal cavity and paranasal sinuses. (2) Methods: A phantom was designed and fabricated using a 3D printer. We then performed a phantom-based accuracy evaluation to validate the performance of the developed registration method. We included 11 patients who underwent IGESS for clinical study to compare the performance of the developed IGESS system with that of a commercialized system. (3) Results: The fiducial registration error (FRE) was 0.14 mm, and the target registration error (TRE) was 0.82 ± 0.50 mm by the phantom-based evaluation. As a result of the clinical comparative study, the average registration times were 36.04 ± 4.7 and 89.35 ± 26.1 s for the developed and commercialized systems, respectively (p < 0.05). The image loading time of the developed system was also shorter than that of the commercialized system (p < 0.05). The average accuracy score of the developed system was not significantly different from that of the commercialized system (p > 0.05). (4) Conclusions: The developed system provided an accurate point-to-point registration method based on the leave-one-out strategy. According to the results of the clinical comparative study, we demonstrated that the developed system showed reliable potential for clinical application.

Malleable Registration Mask: Application of a Novel Registration Method in Image Guided Sinus Surgery

2001 ◽  
Vol 15 (4) ◽  
pp. 219-224 ◽  
Author(s):  
Ford D. Albritton ◽  
Todd T. Kingdom ◽  
John M. DelGaudio
Keyword(s):  
Skull Base Surgery ◽  
Sinus Surgery ◽  
Initial Error ◽  
Anatomic Landmarks ◽  
Reliable Technique ◽  
Registration Method ◽  
Registration Error ◽  
Image Guided ◽  
Ideal Technique ◽  
The Ideal

Image-guided systems are becoming more widely used in endoscopic sinus and skull base surgery. All systems require initial registration to correlate the CT scan images to the patient's anatomy. Multiple registration techniques can be used. The ideal technique is one that is easy, reproducible, and provides the most accurate registration in the least amount of time. This study used an optical-based image-guided system (LandmarX) to test a unique mask registration technique and compared it to a previously used anatomic registration technique. Twenty-one patients were scanned with the mask and underwent surgery. Registration was performed using both the registration mask and the anatomic landmarks. Mean registration error and time were recorded. Results are reported for 20 patients. Mean registration error for the mask technique was 0.96 mm and took a mean of 41 seconds. Anatomic registration error using five or six points resulted in a mean initial error of 2.08 mm and took 31.2 seconds. Mean final anatomic registration error was 1.53 mm, requiring reregistration of a mean of 4.6 points, and took 106 seconds. Statistically significant differences were obtained between the two techniques with regard to registration error and time to final registration. We found that the registration mask technique is a more reliable technique in ease, accuracy, and time of registration. This technique should be especially beneficial to the less experienced image-guided surgeon.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

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