Assessment of the Accuracy of Optical Shape Sensing for Needle Tracking Interventions

2017 ◽  
Vol 11 (3) ◽  
Author(s):  
Koushik Kanti Mandal ◽  
Francois Parent ◽  
Raman Kashyap ◽  
Sylvain Martel ◽  
Samuel Kadoury
Keyword(s):  
Optical Fibers ◽  
Liver Tumors ◽  
Tracking System ◽  
Ground Truth ◽  
Guidance System ◽  
Tracking Accuracy ◽  
Percutaneous Procedures ◽  
Fbg Sensors ◽  
Mri Guided ◽  
Similar Accuracy

Accurate needle guidance is essential for a number of magnetic resonance imaging (MRI)-guided percutaneous procedures, such as radiofrequency ablation (RFA) of metastatic liver tumors. A promising technology to obtain real-time tracking of the shape and tip of a needle is by using high-frequency (up to 20 kHz) fiber Bragg grating (FBG) sensors embedded in optical fibers, which are insensitive to external magnetic fields. We fabricated an MRI-compatible needle designed for percutaneous procedures with a series of FBG sensors which would be tracked in an image-guidance system, allowing to display the needle shape within a navigation image. A series of phantom experiments demonstrated needle tip tracking errors of 1.05 ± 0.08 mm for a needle deflection up to 16.82 mm on a ground-truth model and showed nearly similar accuracy to electromagnetic (EM) tracking (i.e., 0.89 ± 0.09 mm). We demonstrated feasibility of the FBG-based tracking system for MRI-guided interventions with differences under 1 mm between tracking systems. This study establishes the needle tracking accuracy of FBG needle tracking for image-guided procedures.

scholarly journals Evaluation of the Path-Tracking Accuracy of a Three-Wheeled Omnidirectional Mobile Robot Designed as a Personal Assistant

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7216
Author(s):  
Jordi Palacín ◽  
Elena Rubies ◽  
Eduard Clotet ◽  
David Martínez
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Tracking System ◽  
Empirical Evaluation ◽  
Ground Truth ◽  
Path Tracking ◽  
Absolute Maximum ◽  
Tracking Accuracy ◽  
Maximum Displacement ◽  
Omnidirectional Mobile Robot

This paper presents the empirical evaluation of the path-tracking accuracy of a three-wheeled omnidirectional mobile robot that is able to move in any direction while simultaneously changing its orientation. The mobile robot assessed in this paper includes a precise onboard LIDAR for obstacle avoidance, self-location and map creation, path-planning and path-tracking. This mobile robot has been used to develop several assistive services, but the accuracy of its path-tracking system has not been specifically evaluated until now. To this end, this paper describes the kinematics and path-planning procedure implemented in the mobile robot and empirically evaluates the accuracy of its path-tracking system that corrects the trajectory. In this paper, the information gathered by the LIDAR is registered to obtain the ground truth trajectory of the mobile robot in order to estimate the path-tracking accuracy of each experiment conducted. Circular and eight-shaped trajectories were assessed with different translational velocities. In general, the accuracy obtained in circular trajectories is within a short range, but the accuracy obtained in eight-shaped trajectories worsens as the velocity increases. In the case of the mobile robot moving at its nominal translational velocity, 0.3 m/s, the root mean square (RMS) displacement error was 0.032 m for the circular trajectory and 0.039 m for the eight-shaped trajectory; the absolute maximum displacement errors were 0.077 m and 0.088 m, with RMS errors in the angular orientation of 6.27° and 7.76°, respectively. Moreover, the external visual perception generated by these error levels is that the trajectory of the mobile robot is smooth, with a constant velocity and without perceiving trajectory corrections.

scholarly journals Estimating intrafraction tumor motion during fiducial-based liver stereotactic radiotherapy via an iterative closest point (ICP) algorithm

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Wu-zhou Li ◽  
Zhi-wen Liang ◽  
Yi Cao ◽  
Ting-ting Cao ◽  
Hong Quan ◽  
...  
Keyword(s):  
Stereotactic Radiotherapy ◽  
Imaging System ◽  
Liver Tumors ◽  
Three Dimensional ◽  
Rigid Motion ◽  
Iterative Closest Point ◽  
Tumor Motion ◽  
Tracking Accuracy ◽  
Icp Algorithm ◽  
Anterior Posterior

Abstract Background Tumor motion may compromise the accuracy of liver stereotactic radiotherapy. In order to carry out a precise planning, estimating liver tumor motion during radiotherapy has received a lot of attention. Previous approach may have difficult to deal with image data corrupted by noise. The iterative closest point (ICP) algorithm is widely used for estimating the rigid registration of three-dimensional point sets when these data were dense or corrupted. In the light of this, our study estimated the three-dimensional (3D) rigid motion of liver tumors during stereotactic liver radiotherapy using reconstructed 3D coordinates of fiducials based on the ICP algorithm. Methods Four hundred ninety-five pairs of orthogonal kilovoltage (KV) images from the CyberKnife stereo imaging system for 12 patients were used in this study. For each pair of images, the 3D coordinates of fiducial markers inside the liver were calculated via geometric derivations. The 3D coordinates were used to calculate the real-time translational and rotational motion of liver tumors around three axes via an ICP algorithm. The residual error was also investigated both with and without rotational correction. Results The translational shifts of liver tumors in left-right (LR), anterior-posterior (AP),and superior-inferior (SI) directions were 2.92 ± 1.98 mm, 5.54 ± 3.12 mm, and 16.22 ± 5.86 mm, respectively; the rotational angles in left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions were 3.95° ± 3.08°, 4.93° ± 2.90°, and 4.09° ± 1.99°, respectively. Rotational correction decreased 3D fiducial displacement from 1.19 ± 0.35 mm to 0.65 ± 0.24 mm (P<0.001). Conclusions The maximum translational movement occurred in the SI direction. Rotational correction decreased fiducial displacements and increased tumor tracking accuracy.

scholarly journals Experimental Evaluation of Computer Vision and Machine Learning-Based UAV Detection and Ranging

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 37
Author(s):  
Bingsheng Wei ◽  
Martin Barczyk
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Tracking System ◽  
Ground Truth ◽  
White Background ◽  
Cascade Classifier ◽  
Detection Algorithms ◽  
Squared Error ◽  
Test Conditions ◽  
Video Feed

We consider the problem of vision-based detection and ranging of a target UAV using the video feed from a monocular camera onboard a pursuer UAV. Our previously published work in this area employed a cascade classifier algorithm to locate the target UAV, which was found to perform poorly in complex background scenes. We thus study the replacement of the cascade classifier algorithm with newer machine learning-based object detection algorithms. Five candidate algorithms are implemented and quantitatively tested in terms of their efficiency (measured as frames per second processing rate), accuracy (measured as the root mean squared error between ground truth and detected location), and consistency (measured as mean average precision) in a variety of flight patterns, backgrounds, and test conditions. Assigning relative weights of 20%, 40% and 40% to these three criteria, we find that when flying over a white background, the top three performers are YOLO v2 (76.73 out of 100), Faster RCNN v2 (63.65 out of 100), and Tiny YOLO (59.50 out of 100), while over a realistic background, the top three performers are Faster RCNN v2 (54.35 out of 100, SSD MobileNet v1 (51.68 out of 100) and SSD Inception v2 (50.72 out of 100), leading us to recommend Faster RCNN v2 as the recommended solution. We then provide a roadmap for further work in integrating the object detector into our vision-based UAV tracking system.

scholarly journals High Precision Optical Tracking System Based on near Infrared Trinocular Stereo Vision

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2528
Author(s):  
Songlin Bi ◽  
Yonggang Gu ◽  
Jiaqi Zou ◽  
Lianpo Wang ◽  
Chao Zhai ◽  
...  
Keyword(s):  
High Precision ◽  
Stereo Vision ◽  
Near Infrared ◽  
Nearest Neighbor ◽  
Tracking System ◽  
Optical Tracking ◽  
Tracking Accuracy ◽  
Optical Tracking System ◽  
Dynamic Tracking ◽  
Trinocular Stereo

A high precision optical tracking system (OTS) based on near infrared (NIR) trinocular stereo vision (TSV) is presented in this paper. Compared with the traditional OTS on the basis of binocular stereo vision (BSV), hardware and software are improved. In the hardware aspect, a NIR TSV platform is built, and a new active tool is designed. Imaging markers of the tool are uniform and complete with large measurement angle (>60°). In the software aspect, the deployment of extra camera brings high computational complexity. To reduce the computational burden, a fast nearest neighbor feature point extraction algorithm (FNNF) is proposed. The proposed method increases the speed of feature points extraction by hundreds of times over the traditional pixel-by-pixel searching method. The modified NIR multi-camera calibration method and 3D reconstruction algorithm further improve the tracking accuracy. Experimental results show that the calibration accuracy of the NIR camera can reach 0.02%, positioning accuracy of markers can reach 0.0240 mm, and dynamic tracking accuracy can reach 0.0938 mm. OTS can be adopted in high-precision dynamic tracking.

scholarly journals Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5549
Author(s):  
Ossi Kaltiokallio ◽  
Roland Hostettler ◽  
Hüseyin Yiğitler ◽  
Mikko Valkama
Keyword(s):  
Em Algorithm ◽  
Expectation Maximization ◽  
Tracking System ◽  
Model Parameters ◽  
Calibration Data ◽  
Tracking Accuracy ◽  
Time Period ◽  
The Em Algorithm ◽  
Device Free ◽  
Localization And Tracking

Received signal strength (RSS) changes of static wireless nodes can be used for device-free localization and tracking (DFLT). Most RSS-based DFLT systems require access to calibration data, either RSS measurements from a time period when the area was not occupied by people, or measurements while a person stands in known locations. Such calibration periods can be very expensive in terms of time and effort, making system deployment and maintenance challenging. This paper develops an Expectation-Maximization (EM) algorithm based on Gaussian smoothing for estimating the unknown RSS model parameters, liberating the system from supervised training and calibration periods. To fully use the EM algorithm’s potential, a novel localization-and-tracking system is presented to estimate a target’s arbitrary trajectory. To demonstrate the effectiveness of the proposed approach, it is shown that: (i) the system requires no calibration period; (ii) the EM algorithm improves the accuracy of existing DFLT methods; (iii) it is computationally very efficient; and (iv) the system outperforms a state-of-the-art adaptive DFLT system in terms of tracking accuracy.

scholarly journals Femtosecond-Laser-Assisted Fabrication of Radiation-Resistant Fiber Bragg Grating Sensors

2022 ◽  
Vol 12 (2) ◽  
pp. 886
Author(s):  
Hun-Kook Choi ◽  
Young-Jun Jung ◽  
Bong-Ahn Yu ◽  
Jae-Hee Sung ◽  
Ik-Bu Sohn ◽  
...  
Keyword(s):  
Femtosecond Laser ◽  
Fiber Bragg Grating ◽  
Optical Fibers ◽  
Coming Out ◽  
Single Mode ◽  
Femtosecond Laser Irradiation ◽  
Bragg Grating ◽  
Wavelength Shift ◽  
Radiation Resistant ◽  
Fbg Sensors

This paper demonstrates the fabrication of radiation-resistant fiber Bragg grating (FBG) sensors using infrared femtosecond laser irradiation. FBG sensors were written inside acrylate-coated fluorine-doped single-mode specialty optical fibers. We detected the Bragg resonance at 1542 nm. By controlling the irradiation conditions, we improved the signal strength coming out from the FBG sensors. A significant reduction in the Bragg wavelength shift was detected in the fabricated FBG sensors for a radiation dose up to 105 gray, indicating excellent radiation resistance capabilities. We also characterized the temperature sensitivity of the radiation-resistant FBG sensors and detected outstanding performance.

Research on Underwater Target Tracking Based on Contour Detection

2011 ◽  
Vol 317-319 ◽  
pp. 890-896
Author(s):  
Ming Jun Zhang ◽  
Yuan Yuan Wan ◽  
Zhen Zhong Chu
Keyword(s):  
Prediction Model ◽  
Target Tracking ◽  
Tracking System ◽  
Contour Detection ◽  
Motion Prediction ◽  
Moving Target ◽  
Contour Extraction ◽  
Tracking Accuracy ◽  
Tracking Method ◽  
Underwater Target

The traditional centroid tracking method over-relies on the accuracy of segment, which easily lead to loss of underwater moving target. This paper presents an object tracking method based on circular contour extraction, combining region feature and contour feature. Through the correction to circle features, the problem of multiple solutions causing by Hough transform circle detection is avoided. A new motion prediction model is constructed to make up the deficiency that three-order motion prediction model has disadvantage of high dimension and large calculation. The predicted position of object centroid is updated and corrected by circle contour, forming prediction-measurement-updating closed-loop target tracking system. To reduce system processing time, on the premise of the tracking accuracy, a dynamic detection method based on target state prediction model is proposed. The results of contour extraction and underwater moving target experiments demonstrate the effectiveness of the proposed method.

A Study of Tracking System Using Multiple Emitter Towers

2013 ◽  
Vol 694-697 ◽  
pp. 927-935 ◽  
Author(s):  
Yi Sun ◽  
Tao Ma ◽  
Chia Yung Han ◽  
Joseph Ross ◽  
William Wee
Keyword(s):  
Coordinate Transformation ◽  
Motion Tracking ◽  
Tracking System ◽  
Transformation Method ◽  
Tracking Accuracy ◽  
Working Space ◽  
Frontal Part ◽  
Motion Tracking System ◽  
Coordinate Transformation Method

This paper presents a simple and accurate coordinate transformation method for extending the tracking space of the Intersense IS-900 spatial and motion tracking system using multiple pre-configured emitter towers to form the emitter constellation, but without resorting to the use of a surveyor machine. The proposed approach uses the differences of positional coordinate readings from each emitter tower among a set of commonly viewed spatial points to calculate the parameters needed to define the coordinate transformation. By applying this method, the tracking accuracy using the entire emitter constellation can be achieved by less than 0.5 inches error in most of the working space, and as low as 0.2 inches error in the frontal part of the working space.

scholarly journals Development of a magnetic tracking system for monitoring soil movements induced by geohazards

2019 ◽  
Vol 92 ◽  
pp. 17007 ◽  
Author(s):  
Xiaoyu Chen ◽  
Rolando P. Orense
Keyword(s):  
Magnetic Fields ◽  
Permanent Magnets ◽  
Tracking System ◽  
Soil Liquefaction ◽  
Small Scale ◽  
Interior Point Algorithm ◽  
Tracking Accuracy ◽  
Tracking Errors ◽  
Magnetic Tracking ◽  
Soil Movements

In the study of geotechnical hazards, such as soil liquefaction and landslides, the analysis of soil movements is always one of the major preoccupations. An efficient movement sensing technique requires the tracking of subsurface soil for the purpose of examining the mechanism involved. A magnetic tracking system is therefore proposed, with permanent magnets as trackers and magnetometers as receivers. When permanent magnets, deployed within the soil to serve as excitation sources, move with soil body during a geotechnical event, they generate static magnetic fields whose flux densities are related with the positions and orientations of the magnets. Magnetometers are used as receivers to detect the generated magnetic fields, which can be further used in calculating the magnets' locations and orientations based on appropriately developed algorithms. Comparison between situations where the trackers are exposed to air and embedded within soil was conducted to evaluate the influence of soil (wet and dry) on the tracking accuracy. Also, multi-objective tracking is realized by using the particle swarm optimization (PSO) technique combined with interior-point algorithm. The tracking errors are evaluated and applications of the proposed system in small-scale laboratory tests for geohazards are discussed.

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