Evaluation of pattern based point clouds for patient registration — A phantom study

Author(s):  
Franziska S. Goerlach ◽  
Johannes Merkle ◽  
Tobias Lueddemann ◽  
Tim C. Lueth
2017 ◽  
Vol 42 (5) ◽  
pp. E8 ◽  
Author(s):  
Francesco Cardinale ◽  
Michele Rizzi ◽  
Piergiorgio d’Orio ◽  
Giuseppe Casaceli ◽  
Gabriele Arnulfo ◽  
...  

OBJECTIVEThe purpose of this study was to compare the accuracy of Neurolocate frameless registration system and frame-based registration for robotic stereoelectroencephalography (SEEG).METHODSThe authors performed a 40-trajectory phantom laboratory study and a 127-trajectory retrospective analysis of a surgical series. The laboratory study was aimed at testing the noninferiority of the Neurolocate system. The analysis of the surgical series compared Neurolocate-based SEEG implantations with a frame-based historical control group.RESULTSThe mean localization errors (LE) ± standard deviations (SD) for Neurolocate-based and frame-based trajectories were 0.67 ± 0.29 mm and 0.76 ± 0.34 mm, respectively, in the phantom study (p = 0.35). The median entry point LE was 0.59 mm (interquartile range [IQR] 0.25–0.88 mm) for Neurolocate-registration–based trajectories and 0.78 mm (IQR 0.49–1.08 mm) for frame-registration–based trajectories (p = 0.00002) in the clinical study. The median target point LE was 1.49 mm (IQR 1.06–2.4 mm) for Neurolocate-registration–based trajectories and 1.77 mm (IQR 1.25–2.5 mm) for frame-registration–based trajectories in the clinical study. All the surgical procedures were successful and uneventful.CONCLUSIONSThe results of the phantom study demonstrate the noninferiority of Neurolocate frameless registration. The results of the retrospective surgical series analysis suggest that Neurolocate-based procedures can be more accurate than the frame-based ones. The safety profile of Neurolocate-based registration should be similar to that of frame-based registration. The Neurolocate system is comfortable, noninvasive, easy to use, and potentially faster than other registration devices.


2003 ◽  
Author(s):  
Hai Sun ◽  
David W. Roberts ◽  
Alex Hartov ◽  
Kyle R. Rick ◽  
Keith D. Paulsen

Author(s):  
Daisuke Deguchi ◽  
Marco Feuerstein ◽  
Takayuki Kitasaka ◽  
Yasuhito Suenaga ◽  
Ichiro Ide ◽  
...  

2021 ◽  
Vol 15 ◽  
Author(s):  
Wenjie Li ◽  
Jingfan Fan ◽  
Shaowen Li ◽  
Zhaorui Tian ◽  
Zhao Zheng ◽  
...  

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.


2015 ◽  
Vol 53 (08) ◽  
Author(s):  
R Kubale ◽  
T Fuhrmann ◽  
A Arslanow ◽  
F Frenzel ◽  
P Minko ◽  
...  

1999 ◽  
Vol 38 (03) ◽  
pp. 200-206 ◽  
Author(s):  
Y. Ogushi ◽  
Y. Okada ◽  
M. Kimura ◽  
I Kumamoto ◽  
Y. Sekita ◽  
...  

AbstractQuestionnaire surveys were sent to hospital managers, designed to shape the policy for future hospital information systems in Japan. The answers show that many hospitals use dedicated management systems, especially for patient registration and accounting, and personnel, food control, pharmacy and financial departments. In many hospitals, order-entry systems for laboratory tests and prescriptions are well developed. Half of the hospitals have patient databases used for inquiries of basic patient information, history of outpatient care and hospital care. The most obvious benefit is the reduction of office work, due to effective hospital information system. Many hospital managers want to use the following sub systems in the future for automatic payment, waiting time display, patient records search, automatic prescription verification, drug side-effect monitoring, and graphical display of patient record data.


2020 ◽  
Author(s):  
PF Costa ◽  
F Süßelbeck ◽  
A Bramer ◽  
M Conti ◽  
M Weber ◽  
...  

Author(s):  
SM Dudea ◽  
C Botar-Jid ◽  
D Dumitriu ◽  
A Ciurea ◽  
A Chiorean ◽  
...  

Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.


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