INCIDENT POSITION OF REFRACTED LIGHT BEAM FOR REACHING A TARGET POINT

OBJECTIVEThe aim of this study was to compare the accuracy of optical frameless neuronavigation (ON) and robot-assisted (RA) stereoelectroencephalography (SEEG) electrode placement in children, and to identify factors that might increase the risk of misplacement.METHODSThe authors undertook a retrospective review of all children who underwent SEEG at their institution. Twenty children were identified who underwent stereotactic placement of a total of 218 electrodes. Six procedures were performed using ON and 14 were placed using a robotic assistant. Placement error was calculated at cortical entry and at the target by calculating the Euclidean distance between the electrode and the planned cortical entry and target points. The Mann-Whitney U-test was used to compare the results for ON and RA placement accuracy. For each electrode placed using robotic assistance, extracranial soft-tissue thickness, bone thickness, and intracranial length were measured. Entry angle of electrode to bone was calculated using stereotactic coordinates. A stepwise linear regression model was used to test for variables that significantly influenced placement error.RESULTSBetween 8 and 17 electrodes (median 10 electrodes) were placed per patient. Median target point localization error was 4.5 mm (interquartile range [IQR] 2.8–6.1 mm) for ON and 1.07 mm (IQR 0.71–1.59) for RA placement. Median entry point localization error was 5.5 mm (IQR 4.0–6.4) for ON and 0.71 mm (IQR 0.47–1.03) for RA placement. The difference in accuracy between Stealth-guided (ON) and RA placement was highly significant for both cortical entry point and target (p < 0.0001 for both). Increased soft-tissue thickness and intracranial length reduced accuracy at the target. Increased soft-tissue thickness, bone thickness, and younger age reduced accuracy at entry. There were no complications.CONCLUSIONSRA stereotactic electrode placement is highly accurate and is significantly more accurate than ON. Larger safety margins away from vascular structures should be used when placing deep electrodes in young children and for trajectories that pass through thicker soft tissues such as the temporal region.

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Thalamic lesions produced by gamma thalamotomy for movement disorders

Journal of Neurosurgery ◽

10.3171/jns.2002.97.supplement_5.0600 ◽

2002 ◽

Vol 97 ◽

pp. 600-606 ◽

Cited By ~ 33

Author(s):

Chihiro Ohye ◽

Tohru Shibazaki ◽

Jie Zhang ◽

Yoshitaka Andou

Keyword(s):

Mr Imaging ◽

Involuntary Movement ◽

Gamma Knife Radiosurgery ◽

Target Point ◽

High Signal ◽

Thalamic Lesion ◽

Content Type ◽

Stereotactic Thalamotomy ◽

Thalamic Lesions ◽

Fine Print

Object. The treatment of Parkinson disease and other kinds of involuntary movement by gamma knife radiosurgery (GKS) is presented. This is an extension of previous work. The clinical course and thalamic lesions were the main factors examined. Methods. Seventeen new cases were added to the previously reported 36 cases. The course and results for the whole series of 53 patients were examined. Treatment was undertaken using a single 4-mm collimator shot to deliver 130 Gy to the target. The target was determined in the previously treated patients by using classic methods involved in conventional stereotactic thalamotomy with microrecording. More recently, target localization has been performed by relating the target point to the total length of the thalamus. Points may then be defined as percentages of that length measured from the anterior pole. Targets can then be determined in relationship to the appropriate percentage. Thirty-five patients have been followed for more than 2 years and the longest follow up was 8 years. Two kinds of thalamic lesion were seen after GKS. Volumetric analysis on MR imaging revealed that the larger lesion was 400 to 500 mm3 at the beginning and gradually decreased in size. The smaller lesion occupied approximately 200 mm3 and also shrank over several months. Eighty percent of the treated cases showed good results and no significant complications, with the tremor subsiding at 1 year (Type 1). Several cases deviated from this standard course in four different ways (Types 2–5). If tremor persisted, conventional stereotactic thalamotomy with microrecording was performed. During such operations, normal neuronal activity was recorded from the region adjacent to the GKS thalamotomy target. This was the region showing a high signal on MR imaging. The activity patterns included the rhythmical grouped discharge of tremor rhythm. Conclusions. Gamma thalamotomy for functional disorders is still under development, but because the results with careful target planning are satisfactory, there are grounds for increasing optimism.

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W-Peakedness: A Simulation based Study

Pakistan Journal of Engineering Technology & Science ◽

10.22555/pjets.v1i1.135 ◽

2015 ◽

Vol 1 (1) ◽

Author(s):

Suboohi Safdar ◽

Dr. Ejaz Ahmed

Keyword(s):

Probability Distributions ◽

Extensive Study ◽

Descriptive Statistics ◽

Target Point ◽

Density Functions ◽

Central Value ◽

Distribution Shape ◽

Simulation Based ◽

Different Shapes ◽

Kurtosis Coefficient

Kurtosis is a commonly used descriptive statistics. Kurtosis “Coefficient of excess” is critically reviewed in different aspects and is called as, measuring the fatness of the tails of the density functions, concentration towards the central value, scattering away from the target point or degree of peakedness of probability distribution. Kurtosis is referred to the shape of the distribution but many distributions having same kurtosis value may have different shapes while Kurtosis may exist when peak of a distribution is not in existence. Through extensive study of kurtosis on several distributions, Wu (2002) introduced a new measure called “W-Peakedness” that offers a fine capture of distribution shape to provide an intuitive measure of peakedness of the distribution which is inversely proportional to the standard deviation of the distribution. In this paper the work is extended for different others continuous probability distributions. Empirical results through simulation illustrate the proposed method to evaluate kurtosis by W-peakedness

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A survey of the illumination from diagnostic X-ray light-beam diaphragm systems

British Journal of Radiology ◽

10.1259/0007-1285-68-807-311 ◽

1995 ◽

Vol 68 (807) ◽

pp. 311-313

Author(s):

L R Bridge ◽

J E Ison

Keyword(s):

Light Beam ◽

X Ray

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A new method on motion planning for mobile robots using jump point search and Bezier curves

International Journal of Advanced Robotic Systems ◽

10.1177/17298814211019220 ◽

2021 ◽

Vol 18 (4) ◽

pp. 172988142110192

Author(s):

Ben Zhang ◽

Denglin Zhu

Keyword(s):

Motion Planning ◽

Initial Point ◽

Target Point ◽

Jump Point ◽

Bezier Curves ◽

Bézier Curves ◽

Front End ◽

Planning Algorithm ◽

Point Search ◽

Path Planning Algorithm

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on motion planning systems that meet the shortest path and obstacle avoidance requirements. This article proposes a novel path planning algorithm based on jump point search and Bezier curves. The proposed algorithm consists of two main steps. In the front end, the improved heuristic function based on distance and direction is used to reduce the cost, and the redundant turning points are trimmed. In the back end, a novel trajectory generation method based on Bezier curves and a straight line is proposed. Our experimental results indicate that the proposed algorithm provides a complete motion planning solution from the front end to the back end, which can realize an optimal trajectory from the initial point to the target point used for robot navigation.

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Using a Bessel light beam as an ultrashort period helical undulator

Physical Review Accelerators and Beams ◽

10.1103/physrevaccelbeams.20.070701 ◽

2017 ◽

Vol 20 (7) ◽

Author(s):

B. C. Jiang ◽

Q. L. Zhang ◽

J. H. Chen ◽

Z. T. Zhao

Keyword(s):

Light Beam ◽

Bessel Light Beam

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A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

Journal of Marine Science and Engineering ◽

10.3390/jmse9030252 ◽

2021 ◽

Vol 9 (3) ◽

pp. 252

Author(s):

Yushan Sun ◽

Xiaokun Luo ◽

Xiangrui Ran ◽

Guocheng Zhang

Keyword(s):

Path Planning ◽

Obstacle Avoidance ◽

Autonomous Underwater Vehicles ◽

Optimal Path ◽

Small Scale ◽

Target Point ◽

Safe Driving ◽

Policy Gradient ◽

Planning Algorithm ◽

Path Planning Algorithm

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.

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