scholarly journals Real-Time Tumor Motion Tracking in 3D Using Planning 4D CT Images during Image-Guided Radiation Therapy

Algorithms ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 155
Author(s):  
Jang-Hwan Choi ◽  
Sooyeul Lee
Keyword(s):  
Real Time ◽  
Motion Tracking ◽  
Tracking Error ◽  
Structural Similarity ◽  
Tracking Accuracy ◽  
Image Guided Radiation Therapy ◽  
Tumor Tracking ◽  
Respiratory Phase ◽  
4D Computed Tomography ◽  
Using Data

In this paper we propose a novel method for tracking the respiratory phase and 3D tumor position in real time during treatment. The method uses planning four-dimensional (4D) computed tomography (CT) obtained through the respiratory phase, and a kV projection taken during treatment. First, digitally rendered radiographs (DRRs) are generated from the 4DCT, and the structural similarity (SSIM) between the DRRs and the kV projection is computed to determine the current respiratory phase and magnitude. The 3D position of the tumor corresponding to the phase and magnitude is estimated using non-rigid registration by utilizing the tumor path segmented in the 4DCT. This method is evaluated using data from six patients with lung cancer and dynamic diaphragm phantom data. The method performs well irrespective of the gantry angle used, i.e., a respiration phase tracking accuracy of 97.2 ± 2.5%, and tumor tracking error in 3D of 0.9 ± 0.4 mm. The phantom study reveals that the DRRs match the actual projections well. The time taken to track the tumor is 400 ± 53 ms. This study demonstrated the feasibility of a technique used to track the respiratory phase and 3D tumor position in real time using kV fluoroscopy acquired from arbitrary angles around the freely breathing patient.

scholarly journals The use of a cascaded Kinect and electromyography gesture decoding algorithm in an initial robot-aided hand neurorehabilitation

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401775196 ◽  
Author(s):  
Ping Wang ◽  
Yabo Wang ◽  
He Huang ◽  
Feng Ru ◽  
Quan Pan
Keyword(s):  
Control Algorithm ◽  
Motion Tracking ◽  
Simulation Experiment ◽  
Tracking Error ◽  
Torque Control ◽  
Tracking Accuracy ◽  
Rehabilitation Robots ◽  
Machine Interface ◽  
Repetitive Activities ◽  
Active Assistance

In order to improve the neurological recovery of hand neurorehabilitation, target-oriented, intensive, repetitive activities of daily living are used, such as training with recognition of hand gestures during robot-aided exercise. In this article, a cascade control algorithm integrating electromyography bio-feedback into hand gesture recognition is proposed. The outer loop is the trajectory motion tracking with Kinect-based gesture decoding classifier, and the inner loop is torque control with electromyography bio-feedback in the real time. This proposed method improves the tracking accuracy. The tracking error is effectively reduced from 70.56 to 28.07 in the simulation experiment. The initial test proves that the proposed method with additional torque control allows active assistance on the human–machine interface of other rehabilitation robots in future.

scholarly journals Evaluation of real time motion tracking accuracy of customised IMU sensor for application in a mobile badminton virtual reality training system

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Zahari Taha ◽  
Mohd Yashim Wong ◽  
Hwa Jen Yap ◽  
Amirul Abdullah ◽  
Wee Kian Yeo
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Motion Tracking ◽  
Training System ◽  
Microsoft Kinect ◽  
Measurement Unit ◽  
Tracking Accuracy ◽  
Improve Performance ◽  
Motion Data ◽  
Time Motion

Immersion is one of the most important aspects in ensuring the applicability of Virtual Reality systems to training regimes aiming to improve performance. To ensure that this key aspect is met, the registration of motion between the real world and virtual environment must be made as accurate and as low latency as possible. Thus, an in-house developed Inertial Measurement Unit (IMU) system is developed for use in tracking the movement of the player’s racquet. This IMU tracks 6 DOF motion data and transmits it to the mobile training system for processing. Physically, the custom motion is built into the shape of a racquet grip to give a more natural sensation when swinging the racquet. In addition to that, an adaptive filter framework is also established to cope with different racquet movements automatically, enabling real-time 6 DOF tracking by balancing the jitter and latency. Experiments are performed to compare the efficacy of our approach with other conventional tracking methods such as the using Microsoft Kinect. The results obtained demonstrated noticeable accuracy and lower latency when compared with the aforementioned methods.

scholarly journals Markerless Lung Tumor Motion Tracking by Dynamic Decomposition of X-Ray Image Intensity

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Noriyasu Homma ◽  
Yoshihiro Takai ◽  
Haruna Endo ◽  
Kei Ichiji ◽  
Yuichiro Narita ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Lung Tumor ◽  
Tumor Motion ◽  
Image Intensity ◽  
X Ray ◽  
Image Guided Radiation Therapy ◽  
Fluoroscopic Image ◽  
Tumor Tracking ◽  
Markerless Tracking ◽  
Ill Posed

We propose a new markerless tracking technique of lung tumor motion by using an X-ray fluoroscopic image sequence for real-time image-guided radiation therapy (IGRT). A core innovation of the new technique is to extract a moving tumor intensity component from the fluoroscopic image intensity. The fluoroscopic intensity is the superimposition of intensity components of all the structures passed through by the X-ray. The tumor can then be extracted by decomposing the fluoroscopic intensity into the tumor intensity component and the others. The decomposition problem for more than two structures is ill posed, but it can be transformed into a well-posed one by temporally accumulating constraints that must be satisfied by the decomposed moving tumor component and the rest of the intensity components. The extracted tumor image can then be used to achieve accurate tumor motion tracking without implanted markers that are widely used in the current tracking techniques. The performance evaluation showed that the extraction error was sufficiently small and the extracted tumor tracking achieved a high and sufficient accuracy less than 1 mm for clinical datasets. These results clearly demonstrate the usefulness of the proposed method for markerless tumor motion tracking.

scholarly journals The Effect of Respiratory Baseline Drift on the Real-Time Tumor Tracking Accuracy for Liver Tumors

2016 ◽  
Vol 96 (2) ◽  
pp. E144
Author(s):  
S.H. Park ◽  
J. Kim ◽  
J.E. Lee ◽  
M.K. Kang ◽  
J.W. Lee ◽  
...  
Keyword(s):  
Real Time ◽  
Liver Tumors ◽  
Tracking Accuracy ◽  
Baseline Drift ◽  
The Real ◽  
Tumor Tracking

Impacts of respiratory phase shifts on motion‐tracking accuracy of the CyberKnife Synchrony™ Respiratory Tracking System

2019 ◽  
Vol 46 (9) ◽  
pp. 3757-3766 ◽  
Author(s):  
Yuichi Akino ◽  
Hiroya Shiomi ◽  
Iori Sumida ◽  
Fumiaki Isohashi ◽  
Yuji Seo ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Tracking System ◽  
Phase Shifts ◽  
Tracking Accuracy ◽  
Respiratory Phase ◽  
Respiratory Tracking

Improvement of tracking accuracy and stability by recursive image processing in real-time tumor-tracking radiotherapy system

2012 ◽  
Author(s):  
Naoki Miyamoto ◽  
Kenneth Sutherland ◽  
Ryusuke Suzuki ◽  
Taeko Matsuura ◽  
Chie Toramatsu ◽  
...  
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Tracking Accuracy ◽  
Tumor Tracking ◽  
Accuracy And Stability

scholarly journals Technical Description, Phantom Accuracy, and Clinical Feasibility for Single-Session Lung Radiosurgery Using Robotic Image-Guided Real-time Respiratory Tumor Tracking

2007 ◽  
Vol 6 (4) ◽  
pp. 321-328 ◽  
Author(s):  
A. Muacevic ◽  
C. Drexler ◽  
B. Wowra ◽  
A. Schweikard ◽  
A. Schlaefer ◽  
...  
Keyword(s):  
Real Time ◽  
Lung Tumor ◽  
Tracking Error ◽  
Single Session ◽  
Primary Tumors ◽  
Image Guided ◽  
Tumor Tracking ◽  
Robotic Radiosurgery ◽  
Respiratory Tracking

To describe the technological background, the accuracy, and clinical feasibility for single session lung radiosurgery using a real-time robotic system with respiratory tracking. The latest version of image-guided real-time respiratory tracking software (Synchrony®, Accuray Incorporated, Sunnyvale, CA) was applied and is described. Accuracy measurements were performed using a newly designed moving phantom model. We treated 15 patients with 19 lung tumors with robotic radiosurgery (CyberKnife®, Accuray) using the same treatment parameters for all patients. Ten patients had primary tumors and five had metastatic tumors. All patients underwent computed tomography-guided percutaneous placement of one fiducial directly into the tumor, and were all treated with single session radiosurgery to a dose of 24 Gy. Follow up CT scanning was performed every two months. All patients could be treated with the automated robotic technique. The respiratory tracking error was less than 1 mm and the overall shape of the dose profile was not affected by target motion and/or phase shift between fiducial and optical marker motion. Two patients required a chest tube insertion after fiducial implantation because of pneumothorax. One patient experienced nausea after treatment. No other short-term adverse reactions were found. One patient showed imaging signs of pneumonitis without a clinical correlation. Single-session radiosurgery for lung tumor tracking using the described technology is a stable, safe, and feasible concept for respiratory tracking of tumors during robotic lung radiosurgery in selected patients. Longer follow-up is needed for definitive clinical results.

Decoupling motion tracking control for 4WD autonomous vehicles based on the path correction

2021 ◽  
pp. 095440702110159
Author(s):  
Yinghong Yu ◽  
Yinong Li ◽  
Yixiao Liang ◽  
Ling Zheng ◽  
Yue Ren
Keyword(s):  
Autonomous Vehicles ◽  
Motion Tracking ◽  
Tracking Error ◽  
Inverse System ◽  
Tracking Accuracy ◽  
Dynamic Coupling ◽  
Long Distance ◽  
Correction Model ◽  
Decoupling Method ◽  
The Neural Network

Since one control loop input disturbs the control of another loop, the dynamic coupling of the longitudinal and lateral directions adversely affects the motion tracking accuracy of autonomous vehicles. With the ability to minimize the interactions between the longitudinal and lateral dynamics, the inverse system learned by the neural network is an effective way to decouple vehicle dynamics. After tracking the vehicle states projected from the desire motion, the dynamic decoupling and the motion tracking are both realized. However, the accumulation of vehicle state tracking errors causes the stable yaw tracking error and the lateral tracking divergence. To solve the accompanying problem, a path correction model is designed to periodically update the desired vehicle states. Moreover, the applicability of the inverse system decoupling method is improved in this paper, because the method usually adopted in distributed drive electric vehicles is applied to four-wheel driving vehicles representing the traditional driving form. Simulation results indicate that the decoupling motion tracking method with the path correction model is suitable for long-distance and complex conditions and has the highest comprehensive tracking accuracy compared with the integrated MPC (model predictive control) and the pure pursuit in the dynamic coupling conditions.

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