Trajectory Of Moving Target Research Articles

Accuracy of Target Motion Trajectory in 4-Dimensional Cone Beam Computed Tomography for Lung Cancer Patients

Accuracy of Target Motion Trajectory in 4-Dimensional Cone Beam Computed Tomography for Lung Cancer Patients

The accuracy of extracted target motion trajectories in four-dimensional cone-beam computed tomography for lung cancer patients

PurposeTo quantify the accuracy of extracted target motion trajectories in dual-source four-dimensional cone-beam computed tomography (4D-CBCT) by comparison with the actual three-dimensional (3D) target motion acquired simultaneously during 4D-CBCT scan. Materials and methods4D-CBCT scans were performed for 19 different sinusoidal-like patterns and 13 lung cancer patients with implanted markers. Internal (In) or external (Ex) surrogates with amplitude (Amp)- or phase (Ph)-based sorting were used for the reconstructions. The targets were a pseudo-tumor and implanted marker for the phantom and clinical studies, respectively. The accuracy was evaluated by determining the maximum error (MaxEi) between the 3D target position extracted from 4D-CBCT and the actual 3D target position detected by fluoroscopy in each ith phase (0⩽i⩽7). ResultsMedian peak-to-peak target displacements in the superior–inferior (SI) direction were 20.6 and 20.6mm in the phantom and clinical studies, respectively. In the phantom and clinical studies, the maximum of median MaxEis in the SI direction was 4.6 and 9.2mm in the In_Ph reconstruction. In the clinical study, the maximum of median MaxEis was observed during the end-inhalation phase among all reconstruction approaches. ConclusionsThis study showed the magnitude of underestimation toward the inferior direction of target motion in clinical 4D-CBCT.

Validation of moving target doses using ArcCHECK Planned Dose Perturbation Algorithm

 The aim of the study is to use Planned Dose Perturbation (PDP™) measurement-guided reconstruction method to estimate dynamic 4D dose and evaluate both 3D dose and DVH changes caused by target motion resulting from respiration. Five patients of Ca lung were selected for the study. Target and Organ at risks were (OARs) delineated on 4DCT data set of each patient. Dose of moving target vs. stationary target were simulated and compared for OARs and target by analyzing 3D dose and DVH(Dose Volume Histogram).There was almost 3.5% higher target maximum doses measured with moving target after applying the target motion trajectory data as compared to the stationary target whereas OARs doses were comparable. The results, clinically signifies the importance of motion management in lung tumors.

Pretraining Cortical Thickness Predicts Subsequent Perceptual Learning Rate in a Visual Search Task.

We report that preexisting individual differences in the cortical thickness of brain areas involved in a perceptual learning task predict the subsequent perceptual learning rate. Participants trained in a motion-discrimination task involving visual search for a "V"-shaped target motion trajectory among inverted "V"-shaped distractor trajectories. Motion-sensitive area MT+ (V5) was functionally identified as critical to the task: after 3 weeks of training, activity increased in MT+ during task performance, as measured by functional magnetic resonance imaging. We computed the cortical thickness of MT+ from anatomical magnetic resonance imaging volumes collected before training started, and found that it significantly predicted subsequent perceptual learning rates in the visual search task. Participants with thicker neocortex in MT+ before training learned faster than those with thinner neocortex in that area. A similar association between cortical thickness and training success was also found in posterior parietal cortex (PPC).

基于机载激光雷达成像的动目标轨迹检测模型

基于机载激光雷达成像的动目标轨迹检测模型

Control of rock sediment shapes for wheel loaders

A method of making desired rock sediment shapes on a truck bed is developed by designing target motion trajectories of a wheel loader bucket. The behavior of the rocks in the bucket and on the truck bed in loading operation is analyzed by camera images and simulations. By combining both behavior models obtained by the analyses, an algorithm of designing the bucket motion to make desired rock sediment shapes is proposed, and its effectiveness is verified by fundamental experiments.

Optimization of acquisition parameters and accuracy of target motion trajectory for four-dimensional cone-beam computed tomography with a dynamic thorax phantom.

Our purpose in this study was to evaluate the performance of four-dimensional computed tomography (4D-CBCT) and to optimize the acquisition parameters. We evaluated the relationship between the acquisition parameters of 4D-CBCT and the accuracy of the target motion trajectory using a dynamic thorax phantom. The target motion was created three dimensionally using target sizes of 2 and 3cm, respiratory cycles of 4 and 8s, and amplitudes of 1 and 2cm. The 4D-CBCT data were acquired under two detector configurations: "small mode" and "medium mode". The projection data acquired with scan times ranging from 1 to 4min were sorted into 2, 5, 10, and 15 phase bins. The accuracy of the measured target motion trajectories was evaluated by means of the root mean square error (RMSE) from the setup values. For the respiratory cycle of 4s, the measured trajectories were within 2mm of the setup values for all acquisition times and target sizes. Similarly, the errors for the respiratory cycle of 8s were <4mm. When we used 10 or more phase bins, the measured trajectory errors were within 2mm of the setup values. The trajectory errors for the two detector configurations showed similar trends. The acquisition times for achieving an RMSE of 1mm for target sizes of 2 and 3cm were 2 and 1min, respectively, for respiratory cycles of 4s. The results obtained in this study enable optimization of the acquisition parameters for target size, respiratory cycle, and desired measurement accuracy.

Research of Object Tracking Algorithm Based on BRISK

In view of the problems that high complexity, large calculation and the difficulty to apply to real-time systems in the current moving target tracking algorithm, this paper introduce the BRISK feature extraction algorithm, and proposed the object tracking algorithm based on BRISK. Set up the background model and use the background difference method to detect the moving target template. Then match in the next frame and track the target. In order to reduce the search feature matching area, further improve the real-time of the algorithm, we also introduce the kalman filter algorithm to estimate the target motion trajectory. The experimental result show that comparing with the SURF, SIFT feature tracking algorithm, the algorithm of this paper has greatly improved in real-time.

A Research on Moving Target Trajectory Simulation and Tracking in Wide Area Surveillance Ground Moving Target Indication Mode

由于可以在较短时间内对大范围区域进行动目标监视，广域监视动目标检测(Wide Area Surveillance, WAS-GMTI)模式已经成为机载监视雷达的一种重要工作模式。目前具备WAS-GMTI工作模式的系统较少，实际数据较难获得，严重影响了对WAS-GMTI中相关处理算法的验证。该文提出一种基于电子地图路网信息的WAS-GMTI动目标点迹仿真方法。该方法根据载机和电子地图路网信息直接产生动目标信息，避免了仿真场景回波所需的大量运算，而且产生的动目标信息可以合理地融合到真实地理环境中。此外，联合对同一动目标的多次观测信息，该文还给出了一种动目标的轨迹关联方法。仿真结果验证了该文所提方法的有效性。

Video Image Tracing Based on Improved SIFT Feature Matching Algorithm

In this paper, we focus on the extraction problem of the target motion trajectory and make deep research. For Euclidean distance of SIFT feature matching algorithm is not adaptively adjustable, a improved SIFT feature matching algorithm based on multi-objective optimization is proposed. The optimization model is established to reduce mismatch rate, which consider Euclidean distance between correlation coefficient and feature point as the goal function and the confidence degree is taken as the constraint condition. Finally, The experiment shows that the method proposed in this paper can accurately achieve the measurement of the target motion trajectory, and is of strong adaptability and reliable.

Ground Moving Target Trajectory Reconstruction in Single-Channel Circular SAR

Synthetic aperture radar (SAR) has become an important technique for generating high-resolution images of the ground because of its all-weather capabilities. SAR imaging of stationary scenes is nowadays well mastered. Moving targets induce a delocalization and a defocusing effect in the azimuth direction in a SAR image. This latter effect can be used to detect moving targets, to image them, and to estimate their azimuthal velocity, but the main limitation is the impossibility to estimate the full target velocity vector, because of the Doppler shift dependence on azimuthal position and radial velocity. In this paper, we analyze the performances of a method that reconstructs the real target trajectory given the apparent positions of the moving target measured on SAR images acquired along a circular trajectory. We first outline the steps of this trajectory reconstruction methodology, then we perform a mathematical analysis of this methodology, and finally, we present some results on real data, around two French cities.

Motion Control Technology Research Based on PVT Algorithm

This paper discussed the mathematical nature of the motion control PVT motion patterns, derived the control algorithm, presented the fitting error analysis, proposed curve fitting scheme in the PVT motion mode. The simulation results show that this scheme can obtain more accurate target motion trajectory curve.

Predictive encoding of moving target trajectory by neurons in the parabigeminal nucleus

Intercepting momentarily invisible moving objects requires internally generated estimations of target trajectory. We demonstrate here that the parabigeminal nucleus (PBN) encodes such estimations, combining sensory representations of target location, extrapolated positions of briefly obscured targets, and eye position information. Cui and Malpeli (Cui H, Malpeli JG. J Neurophysiol 89: 3128-3142, 2003) reported that PBN activity for continuously visible tracked targets is determined by retinotopic target position. Here we show that when cats tracked moving, blinking targets the relationship between activity and target position was similar for ON and OFF phases (400 ms for each phase). The dynamic range of activity evoked by virtual targets was 94% of that of real targets for the first 200 ms after target offset and 64% for the next 200 ms. Activity peaked at about the same best target position for both real and virtual targets. PBN encoding of target position takes into account changes in eye position resulting from saccades, even without visual feedback. Since PBN response fields are retinotopically organized, our results suggest that activity foci associated with real and virtual targets at a given target position lie in the same physical location in the PBN, i.e., a retinotopic as well as a rate encoding of virtual-target position. We also confirm that PBN activity is specific to the intended target of a saccade and is predictive of which target will be chosen if two are offered. A Bayesian predictor-corrector model is presented that conceptually explains the differences in the dynamic ranges of PBN neuronal activity evoked during tracking of real and virtual targets.

Prediction of Target Motion Drives Oculomotor Response during Target Occlusions

Prediction of object motion allows overcoming of object occlusions in its trajectory. In this study pseudorandom target trajectory with occlusions of 500 ms was used. It was found that for a period up to 200 ms after target occlusion, oculomotor system was driven by a short-term memory of the pre-occlusion target motion trajectory. We conclude, that when visual system is no longer able to predict location of occluded target, there is a tendency to shift gaze away from previous target location, similarly, away from the edges of the screen. We suggest that this is due to probability, accumulated in long-term memory, which supports expectation that the target should reappear near the centre of the screen. This behaviour supports basic principles of Bayesian decision theory. DOI: http://dx.doi.org/10.5755/j01.eee.18.9.2807

Research on Automatic Target Tracking Based on PTZ System

This paper studies an algorithm of automatic target tracking based on PTZ system. Select the tracking target and set up the target motion trajectory in the video screen. Along the motion trajectory, the system controls the PTZ rotation automatically to track the target real-timely. At the same time, it adjusts the zoom to enlarge or reduce to make sure the target can display on the video screen center clearly at the suitable size. By testing on groups of video, verify the effectiveness of the automatic target tracking algorithm.

A Novel Motion Parameter Estimation Algorithm of Fast Moving Targets via Single-Antenna Airborne SAR System

A novel parameter estimation algorithm of fast moving targets using single-antenna airborne synthetic aperture radar (SAR) databased on desampling and Radon transform (RT) is introduced in this letter. First, the dual-channel data are constructed by desampling the single-antenna airborne SAR data in the azimuth direction. Then, the clutter and the spectrum aliasing of the moving target can be cancelled by coherent subtracting. As a result, the moving target trajectory exhibits a single curve in both the range-compressed and the range-Doppler domains. Second, range cell migration correction is adopted to eliminate the range curve and parts of the range walk. Owing to the Doppler ambiguity, the moving target trajectory becomes a straight line. Third, the desampled Doppler ambiguity number and the Doppler rate of the moving target can be calculated by the slope of the line, which is measured by RT. Finally, along- and across-track velocities of the moving target are further obtained. The effectiveness of the proposed scheme is validated by the simulated and real data.

Target‐specific optimization of four‐dimensional cone beam computed tomography

Under-sampling artifacts are a major problem in four-dimensional cone-beam computed tomography (4D-CBCT) and may compromise evaluation of target motion. Several studies have addressed scan parameter selection for minimizing under-sampling artifacts; however, the role of the target characteristics in scan parameter selection has not been investigated. In this work, the authors evaluated 4D-CBCT performance by assessing the accuracy of target motion measurements for various target sizes and motions. The results may serve as patient-specific guidelines for scan parameters selection in 4D-CBCT. The authors acquired 4D-CBCT scans of a moving phantom consisting of six water-filled sphere targets of sizes 10-37 mm, using various scan times ranging from 30 s to 3 min., setting the motion to 3-s and 6-s periods. The authors used automatic image registration to extract the target motion trajectories and evaluated these measurements for various target sizes and motions over various combinations of scan parameters including scan time, detector configuration, number of respiration phases, and reconstruction filters. The most important object parameter to 4D-CBCT performance was the period of motion. Measurements for the 6-s motion were always systematically less accurate than measurements for the 3-s motion for 34 of 36 objects of various sizes and periods of motion. The 6-s motion required a greater scan time than the 3-s motion for equivalent measurement accuracy. The second most influential parameter was the target size. For the 3-s period of motion, objects larger than 13 mm were tracked with sub-millimeter accuracy with a 1-min scan time. For the 6-s period of motion, objects larger than 22-mm were tracked with sub-millimeter accuracy with a 1.5-min scan time. For all sizes and motions, temporal blurring was observed when the number of phases was fewer than 8. Offset detector configuration provided the same performance as centered detector except for small targets (20 mm.) at short scan times (≤1 min). Finally, reconstruction filtering and number of respiratory phases did not affect performance. Scan time should be set according to target size and motion. The authors have provided figures that provide the minimum scan time needed to achieve the particular motion measurement accuracy for a particular size and motion period.

Rapid learning of pursuit target motion trajectories revealed by responses to randomized transient sinusoids

When humans pursue sinusoidal target motion they rapidly learn to track with minimal phase error despite inherent visuo-motor processing delays; prior evidence suggests that prediction might even occur within the first cycle. Here, this has been examined by evoking reactive responses to single cycle stimuli having randomised periodicity and peak velocity. Periodicity was varied within three specific ranges with differing average periodicity. Initial responses in the first half-cycle were remarkably similar within periodicity ranges, irrespective of target velocity or frequency, but differed between ranges. In contrast, in the second half-cycle eye velocity closely matched the target in velocity and timing, irrespective of differences in eye velocity in the first half. Abrupt transitions occurred between first and second half-cycles, consistent with the hypothesis that target motion information is sampled and stored within the first half-cycle, irrespective of actual eye velocity evoked, and then released as a predictive estimate in the second half.

SU-D-213CD-03: Live Video-Guided Volumetric Tracking of Respiration Motion.

To introduce video surface imaging guidance in synchronization with 4D cone-beam CT (CBCT) scans, and in combination with respiration- gated or target-tracked dose delivery to treat mobile tumors, without collaterally damaging nearby critical structures. The approach uses the concept that the integral of balanced forces over the moving surfaces is directly proportional to the lung volume changes. The respiratory motions, representing the lung volume variations, were measured with the dynamic volume under the moving surfaces of the thorax and abdomen. Sequential surface images on several patients and volunteers were acquired for the feasibility study. Respiratory motions were repeatedly measured on volunteers undertaking a quiet (normal) or a forced (deep) breath. The dynamic volume under the moving surfaces were robustly fitted with a linear trend and a trigonometric wave function that was compared with the fitted curves for target moving trajectories derived from forty 4D-CBCT scans. A large chest wall superior-outward movement was the unique characteristic of a forced breath that had doubled the volume variations and elongated the respiration period from quiet breath of ∼4 seconds to >6 seconds. Under a quiet breath, target motion trajectories could be easily described by single sine functions that were consistent with dynamic surface volume modeling except for having different motion amplitudes. The accuracy in synchronization of the real-time surface motion with respiration motion was within the measurement uncertainty of ∼2 mm. The analytical results with a hypothetical single sine platform allow us to accurately predict internal target motion with use of real-time video images. Synchronization of dynamic volume with respiratory motion appears applicable for association of 4D medical imaging with 4D videoimaging.

SU-E-J-129: Target-Specific Optimization of Four-Dimensional Cone Beam Computed Tomography.

Undersampling artifacts are a major problem in four-dimensional cone-beam computed tomography (4D-CBCT) and may compromise evaluation of target motion. The role of the target characteristics in scan parameter selection has not been previously investigated. In this work, we evaluated 4D-CBCT performance by assessing the accuracy of target motion measurements for various target sizes and motions. The results may serve as patient-specific guidelines for selection of scan parameters in 4D-CBCT. We acquired 4D-CBCT scans of a moving phantom consisting of six water-filled sphere targets of sizes 10 to 37 mm, with various scan times ranging from 30 sec to 3 min. Two different (3-sec and 6-sec) periods of motions were tested. We used automatic image registration to extract the target motion trajectories and evaluated the accuracy of the measurement for the various target sizes and motions for various combinations of scan parameters. The most important object parameter to 4D-CBCT performance was the period of motion. Measurements for the 6-sec motion were always systematically less accurate than measurements for the 3-sec motion for 34 of 36 target sizes/motions for any given scan time. For any given target size, a 45-sec scan for the 3-sec motion yielded nearly equivalent accuracy to a 3-min scan for a 6-sec motion. The second most important parameter was the target size. A 1-min scan was needed for sub-mm motion measurement accuracy of 28-mm target, whereas a 1.5-min scan was needed for the 22-mm target. For all sizes and motions, temporal blurring was observed when the number of phases was fewer than 8. Scan time should be set according to target size and motion. We have provided figures that provide the minimum scan time needed to achieve the particular motion measurement accuracy for a particular size and period of motion.