Tracking Uncertainty Research Articles

SU‐E‐J‐188: Theoretical Estimation of Margin Necessary for Markerless Motion Tracking

Purpose:To estimate the margin necessary to adequately cover the target using markerless motion tracking (MMT) of lung lesions given the uncertainty in tracking and the size of the target.Methods:Simulations were developed in Matlab to determine the effect of tumor size and tracking uncertainty on the margin necessary to achieve adequate coverage of the target. For simplicity, the lung tumor was approximated by a circle on a 2D radiograph. The tumor was varied in size from a diameter of 0.1 − 30 mm in increments of 0.1 mm. From our previous studies using dual energy markerless motion tracking, we estimated tracking uncertainties in × and y to have a standard deviation of 2 mm. A Gaussian was used to simulate the deviation between the tracked location and true target location. For each size tumor, 100,000 deviations were randomly generated, the margin necessary to achieve at least 95% coverage 95% of the time was recorded. Additional simulations were run for varying uncertainties to demonstrate the effect of the tracking accuracy on the margin size.Results:The simulations showed an inverse relationship between tumor size and margin necessary to achieve 95% coverage 95% of the time using the MMT technique. The margin decreased exponentially with target size. An increase in tracking accuracy expectedly showed a decrease in margin size as well.Conclusion:In our clinic a 5 mm expansion of the internal target volume (ITV) is used to define the planning target volume (PTV). These simulations show that for tracking accuracies in × and y better than 2 mm, the margin required is less than 5 mm. This simple simulation can provide physicians with a guideline estimation for the margin necessary for use of MMT clinically based on the accuracy of their tracking and the size of the tumor.

Fuzzy feature tracking: Visual analysis of industrial 4D-XCT data

In situ analysis is becoming increasingly important in the evaluation of existing as well as novel materials and components. In this domain, specialists require answers on questions such as: How does a process change internal and external structures of a component? or How do the internal features evolve?In this work, we present a novel integrated visual analysis tool to evaluate series of X-ray Computed Tomography (XCT) data. We therefore process volume datasets of a series of XCT scans, which non-destructively cover the evolution of a process by in situ scans. After the extraction of individual features, a feature tracking algorithm is applied to detect changes of features throughout the series as events. We distinguish between creation, continuation, split, merge and dissipation events. As an explicit tracking is not always possible, we introduce the computation of a Tracking Uncertainty. We visualize the data together with the determined events in multiple linked-views, each emphasizing individual aspects of the 4D-XCT dataset series: A Volume Player and a 3D Data View show the spatial feature information, whereas the global overview of the feature evolution is visualized in the Event Explorer. The Event Explorer allows for interactive exploration and selection of the events of interest. The selection is further used as basis to calculate a Fuzzy Tracking Graph visualizing the global evolution of the features over the whole series.We finally demonstrate the results and advantages of the proposed tool using various real world applications, such as a wood shrinkage analysis and an AlSiC alloy under thermal load.

Probabilistic Collision Avoidance for Vessels

The improvement of collision avoidance for vessels in close range encounter situations is an important topic for maritime traffic safety. Typical approaches generate evasive trajectories or optimise the trajectories of all involved vessels. Such a collision avoidance system has to produce evasive manoeuvres that do not confuse other navigators. To achieve this behaviour, a probabilistic obstacle handling based on information from a radar sensor with target tracking, that considers measurement and tracking uncertainties is proposed. A grid based path search algorithm, that takes the information from the probabilistic obstacle handling into account, is then used to generate evasive trajectories. The proposed algorithms have been tested and verified in a simulated environment for inland waters.

Electromagnetic tracking in surgical and interventional environments: usability study.

Electromagnetic (EM) tracking of instruments within a clinical setting is notorious for fluctuating measurement performance. Position location measurement uncertainty of an EM system was characterized in various environments, including control, clinical, cone beam computed tomography (CBCT), and CT scanner environments. Static and dynamic effects of CBCT and CT scanning on EM tracking were evaluated. Two guidance devices were designed to solely translate or rotate the sensor in a non-interfering fit to decouple pose-dependent tracking uncertainties. These devices were mounted on a base to allow consistent and repeatable tests when changing environments. Using this method, position and orientation measurement accuracies, precision, and 95 % confidence intervals were assessed. The tracking performance varied significantly as a function of the environment-especially within the CBCT and CT scanners-and sensor pose. In fact, at a fixed sensor position in the clinical environment, the measurement error varied from 0.2 to 2.2 mm depending on sensor orientations. Improved accuracies were observed along the vertical axis of the field generator. Calibration of the measurements improved tracking performance in the CT environment by 50-85 %. EM tracking can provide effective assistance to surgeons or interventional radiologists during procedures performed in a clinical or CBCT environment. Applications in the CT scanner demand precalibration to provide acceptable performance.

Toward the development of intrafraction tumor deformation tracking using a dynamic multi-leaf collimator.

Intrafraction deformation limits targeting accuracy in radiotherapy. Studies show tumor deformation of over 10 mm for both single tumor deformation and system deformation (due to differential motion between primary tumors and involved lymph nodes). Such deformation cannot be adapted to with current radiotherapy methods. The objective of this study was to develop and experimentally investigate the ability of a dynamic multi-leaf collimator (DMLC) tracking system to account for tumor deformation. To compensate for tumor deformation, the DMLC tracking strategy is to warp the planned beam aperture directly to conform to the new tumor shape based on real time tumor deformation input. Two deformable phantoms that correspond to a single tumor and a tumor system were developed. The planar deformations derived from the phantom images in beam's eye view were used to guide the aperture warping. An in-house deformable image registration software was developed to automatically trigger the registration once new target image was acquired and send the computed deformation to the DMLC tracking software. Because the registration speed is not fast enough to implement the experiment in real-time manner, the phantom deformation only proceeded to the next position until registration of the current deformation position was completed. The deformation tracking accuracy was evaluated by a geometric target coverage metric defined as the sum of the area incorrectly outside and inside the ideal aperture. The individual contributions from the deformable registration algorithm and the finite leaf width to the tracking uncertainty were analyzed. Clinical proof-of-principle experiment of deformation tracking using previously acquired MR images of a lung cancer patient was implemented to represent the MRI-Linac environment. Intensity-modulated radiation therapy (IMRT) treatment delivered with enabled deformation tracking was simulated and demonstrated. The first experimental investigation of adapting to tumor deformation has been performed using simple deformable phantoms. For the single tumor deformation, the A(u)+A(o) was reduced over 56% when deformation was larger than 2 mm. Overall, the total improvement was 82%. For the tumor system deformation, the A(u)+A(o) reductions were all above 75% and the total A(u)+A(o) improvement was 86%. Similar coverage improvement was also found in simulating deformation tracking during IMRT delivery. The deformable image registration algorithm was identified as the dominant contributor to the tracking error rather than the finite leaf width. The discrepancy between the warped beam shape and the ideal beam shape due to the deformable registration was observed to be partially compensated during leaf fitting due to the finite leaf width. The clinical proof-of-principle experiment demonstrated the feasibility of intrafraction deformable tracking for clinical scenarios. For the first time, we developed and demonstrated an experimental system that is capable of adapting the MLC aperture to account for tumor deformation. This work provides a potentially widely available management method to effectively account for intrafractional tumor deformation. This proof-of-principle study is the first experimental step toward the development of an image-guided radiotherapy system to treat deforming tumors in real-time.

SU‐E‐T‐422: Evaluation of PTV Dose Margins for Lung SBRT On Cyberknife Treatment

Purpose: The purpose of this study is to retrospectively evaluate the PTV margins used on CyberKnife for lung SBRT treatment. Methods: The CyberKnife system uses an active tracking system that models the motion of the target based on information from orthogonal images and the skin markers. Deviations between the model predicted position and the real target position show up when patient breaths irregularly. This discrepancy can be up to 1 cm for some situations. Some previous research suggested that 5 mm or 6 mm PTV margins are needed to cover the target 96∼98% of the time. The CK lung SBRT treatment usually prescribed at 55–70% isodose line (Monte Carlo dose calculation), that means the region covered by the full prescription dose in real delivery will be larger than the planned PTV region when the tracking uncertainty is accounted. This indicates that the PTV margin could be less than 5∼6 mm when the real dose is considered. 20 previous treated lung SBRT patients were examined. The real target location relatives to the model position at that time were extracted and the dose was recalculated based on the real target position using an in‐house developed Monte Carlo program. Results: Analysis of all the CyberKnife 20 patients (94 treatments) showed that by using 2 mm physical PTV margin, the region received full prescription dose was 5.4 mm +/− 1.6mm from the GTV. The planning criteria are 100% GTV volume receives at least 100% prescription dose. By consider the tracking uncertainty, for 2 mm physical PTV margin, 99.5 +/− 0.4% GTV volume still receives at least 100% prescription dose. The maximum dose reduced by about 12% on average. Conclusion: Conclusions: Image tracking log from the CyberKnife indicate that a 5mm margin may be overkill when motion tracking is used.

Neural-Fuzzy Digital Strategy of Continuous-Time Nonlinear Systems Using Adaptive Prediction and Random-Local-Optimization Design

A tracking problem, time-delay, uncertainty and stability analysis of a predictive control system are considered. The predictive control design is based on the input and output of neural plant model (NPM), and a recursive fuzzy predictive tracker has scaling factors which limit the value zone of measured data and cause the tuned parameters to converge to obtain a robust control performance. To improve the further control performance, the proposed random-local-optimization design (RLO) for a model/controller uses offline initialization to obtain a near global optimal model/controller. Other issues are the considerations of modeling error, input-delay, sampling distortion, cost, greater flexibility, and highly reliable digital products of the model-based controller for the continuous-time (CT) nonlinear system. They are solved by a recommended two-stage control design with the first-stage (offline) RLO and second-stage (online) adaptive steps. A theorizing method is then put forward to replace the sensitivity calculation, which reduces the calculation of Jacobin matrices of the back-propagation (BP) method. Finally, the feedforward input of reference signals helps the digital fuzzy controller improve the control performance, and the technique works to control the CT systems precisely.

Learning Control for a Robotic Manipulator with Input Saturation

In this paper, adaptive neural-network control is designed for an n-DOF robotic manipulator system. In the tracking control design, both uncertainties and input saturation are considered. Stability of the closed-loop system is analyzed via the Lyapunov's direct method. The uncertain system is approximated by the radial basis function neural-networks (RBFNN) and the input saturation is solved by adding an auxiliary signal. Simulation studies are conducted to examine the effectiveness of the proposed model-based and RBFNN control.

Kalman Filter for Robot Vision: A Survey

Kalman filters have received much attention with the increasing demands for robotic automation. This paper briefly surveys the recent developments for robot vision. Among many factors that affect the performance of a robotic system, Kalman filters have made great contributions to vision perception. Kalman filters solve uncertainties in robot localization, navigation, following, tracking, motion control, estimation and prediction, visual servoing and manipulation, and structure reconstruction from a sequence of images. In the 50th anniversary, we have noticed that more than 20 kinds of Kalman filters have been developed so far. These include extended Kalman filters and unscented Kalman filters. In the last 30 years, about 800 publications have reported the capability of these filters in solving robot vision problems. Such problems encompass a rather wide application area, such as object modeling, robot control, target tracking, surveillance, search, recognition, and assembly, as well as robotic manipulation, localization, mapping, navigation, and exploration. These reports are summarized in this review to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed in an abstract level.

Learning Where to Attend with Deep Architectures for Image Tracking

We discuss an attentional model for simultaneous object tracking and recognition that is driven by gaze data. Motivated by theories of perception, the model consists of two interacting pathways, identity and control, intended to mirror the what and where pathways in neuroscience models. The identity pathway models object appearance and performs classification using deep (factored)-restricted Boltzmann machines. At each point in time, the observations consist of foveated images, with decaying resolution toward the periphery of the gaze. The control pathway models the location, orientation, scale, and speed of the attended object. The posterior distribution of these states is estimated with particle filtering. Deeper in the control pathway, we encounter an attentional mechanism that learns to select gazes so as to minimize tracking uncertainty. Unlike in our previous work, we introduce gaze selection strategies that operate in the presence of partial information and on a continuous action space. We show that a straightforward extension of the existing approach to the partial information setting results in poor performance, and we propose an alternative method based on modeling the reward surface as a gaussian process. This approach gives good performance in the presence of partial information and allows us to expand the action space from a small, discrete set of fixation points to a continuous domain.

TH‐C‐BRC‐09: Tracking Moving Tumors with a Scanned Carbon Beam: Robustness to Changing Target Motion Characteristics and Tracking Uncertainties

Purpose: Beam tracking of moving tumors with scanned carbon ion radiotherapy achieves uniform target dose by adapting carbon pencil beams to follow moving tissues using real‐time lateral magnetic deflection and range modulation. The purpose of the current study was to establish the robustness of target dose coverage that can be achieved in the presence of changing target motion characteristics and uncertainties in the motion tracking system. Methods: A 4D research treatment planning system code for ion therapy was extended to compute the resulting target dose coverage when various random and systematic uncertainties were introduced to ideal tracking dose simulations. Treatment plans were prepared using the in‐ house code for a hypothetical sphere target volume with sinusoidal motion in a water phantom. Uncertainties were simulated by shifting pencil beam coordinates and modifying target motion signals. To simulate imperfect tracking, we considered variations to the following: (1) respiratory period, (2) starting respiratory phase, (3) target position drift, (4) random tracking offsets, (5) range tracking wedge acceleration, and (6) external respiratory motion signal phase delay. We also considered imperfect tracking with combinations of varying target motion characteristics and tracking uncertainties. A total of 13713 dose distributions were computed. For all cases, mean, standard deviation, minimum, and maximum of target dose were analyzed. Results: For perfect tracking of the moving sphere, mean and standard deviation of target dose were 98.2 and 0.8 % of the prescription dose, respectively. Of the variations considered, systematic drifts of target position of up to 5 mm had the most significant effect on target coverage ((6.6 ± 20.3) % decrease), and range tracking wedge accelerations above 1.0 g had the least effect on target dose ((0.0 ±1.1) %). Conclusions: The results provide insight into beam tracking precision requirements and the need to monitor and mitigate changes in patient motion.Work supported by research grant from the Deutscher Akademischer Austausch Dienst, Bonn, Germany.

TH-E-220-09: Dose Reduction in X-Ray Fluoroscopic Tracking via Online Exposure Modulation

Purpose: A growing class of complex X-ray fluoroscopy guided procedures, particularly in interventional cardiology, neuroradiology, and in real-time tumor-tracking radiotherapy and radiosurgery involve long treatment durations. There is increasing concern regarding the amount of imaging dose delivered to the patient in these procedures. Recently, a method has been proposed for modulating imaging parameters using feedback of tracking precision. We evaluate such a method on respiratory motion data in the context of tracking a fiducial under X-ray fluoroscopy and present an online framework for modulating exposure to reduce imaging dose. Methods: A Teflon sphere (1.5mm in diameter, 1000HU) was imaged at tube currents ranging from 0.5 mA to 0.9mA at a fixed energy of 100 kVp. The images were acquired on a Varian Paxscan 4030A (2048×1536) under the kV imaging geometry typically found on kV enabled medical linear accelerator systems (1000 mm source to axis distance, 1550 mm source to detector distance). This set of images was then used to generate motion sequences using published respiratory motion data, which were then composited to include a regions of high and low noise. The sphere was then tracked in these images using a particle filter. Tracking uncertainty from the particle filter was used to modulate the tube current such that the desired operating tracking precision was maintained while minimizing the imaging dose used. Performance was assessed using entrance surface exposure and mean squared error as metrics. An online framework for tracking objects of interested is developed that modulates the tube current in near real time. Results: Comparison of the exposure modulation framework with a similar system employing fixed exposure reveals a dose savings of up to 27.4% when using an operating uncertainty of 0.2 mm. Conclusions: This work presents a near real-time framework with a potential for dose reduction in X- ray fluoroscopic tracking procedures.

Minimizing dose during fluoroscopic tracking through geometric performance feedback

There is a growing concern regarding the dose delivered during x-ray fluoroscopy guided procedures, particularly in interventional cardiology and neuroradiology, and in real-time tumor tracking radiotherapy and radiosurgery. Many of these procedures involve long treatment times, and as such, there is cause for concern regarding the dose delivered and the associated radiation related risks. An insufficient dose, however, may convey less geometric information, which may lead to inaccuracy and imprecision in intervention placement. The purpose of this study is to investigate a method for achieving the required tracking uncertainty for a given interventional procedure using minimal dose. A simple model is used to demonstrate that a relationship exists between imaging dose and tracking uncertainty. A feedback framework is introduced that exploits this relationship to modulate the tube current (and hence the dose) in order to maintain the required uncertainty for a given interventional procedure. This framework is evaluated in the context of a fiducial tracking problem associated with image-guided radiotherapy in the lung. A particle filter algorithm is used to robustly track the fiducial as it traverses through regions of high and low quantum noise. Published motion models are incorporated in a tracking test suite to evaluate the dose-localization performance trade-offs. It is shown that using this framework, the entrance surface exposure can be reduced by up to 28.6% when feedback is employed to operate at a geometric tracking uncertainty of 0.3 mm. The analysis reveals a potentially powerful technique for dynamic optimization of fluoroscopic imaging parameters to control the applied dose by exploiting the trade-off between tracking uncertainty and x-ray exposure per frame.

Nanoscale 3D Tracking with Conjugated Polymer Nanoparticles

Small ( approximately 15 nm diameter), highly fluorescent conjugated polymer nanoparticles were evaluated for nanoscale 2D and 3D tracking applications. Nanoparticles composed of conjugated polymers possess high absorption cross sections, high radiative rates, and low or moderate aggregation quenching, resulting in extraordinarily high fluorescent brightness. The bright fluorescence ( approximately 200 000 photons detected per particle per 20 ms exposure) yields a theoretical particle tracking uncertainty of less than 1 nm. A lateral tracking uncertainty of 1-2 nm was determined from analysis of trajectories of fixed and freely diffusing particles. Axial (Z) position information for 3D particle tracking was obtained by defocused imaging. Nanoscale tracking of single particles in fixed cells was demonstrated, and a range of complex behaviors, possibly due to binding/unbinding dynamics, were observed.

A Three-Dimensional Numerical Study of the Gas/Particle Interactions in an Industrial-Scale Spray Dryer for Milk Powder Production

Gas/particle interaction plays an important role in modern spray dryers and may have influences on wall deposition, agglomeration, powder degradation, etc. In the present study, the three-dimensional (3-D) transient multiphase flow in an industrial-scale spray dryer has been investigated using the CFD package FLUENT. The Eulerian–Lagrangian approach and two-way coupling method were used in the simulations. The reaction engineering approach (REA model) for milk particles has been implemented. Some new characteristics of the gas flow pattern and the particle behavior (e.g., temperature–time profiles) were identified from the numerical results; for example, the milk particles flow in such a way that makes the central jet oscillation more nonlinear. The discrete phase enhances the turbulence near the air/droplet inlet but damps it downstream. The transient turbulent flow causes significant uncertainties in the particle tracking, which presented some challenges in simulations. The study has highlighted the importance in performing 3-D transient simulations in order to understand the industrial-scale dryers.

Tracking uncertainties in the causal chain from human activities to climate

Attribution of climate change to individual countries is a part of ongoing policy discussions, e.g., the Brazil proposal, and requires a quantifiable link between emissions and climate change. We present a constrained propagation of errors that tracks uncertainties from human activities to greenhouse gas emissions, to increasing abundances of greenhouse gases, to radiative forcing of climate, and finally to climate change, thus following the causal chain for greenhouse gases emitted by developed nations since national reporting began in 1990. Errors combine uncertainties in the forward modeling at each step with top‐down constraints on the observed changes in greenhouse gases and temperatures. Global surface temperature increased by +0.11 °C in 2003 due to the developed nations' emissions of Kyoto greenhouse gases from 1990 to 2002. The uncertainty range, +0.08 °C to +0.14 °C (68% confidence), is large considering that the developed countries emissions are well known for this period and climate system modeling uncertainties are constrained by observations.

Source specific fecal bacteria modeling using soil and water assessment tool model

Fecal bacteria can contaminate water and result in illness or death. It is often difficult to accurately determine sources of fecal bacteria contamination, but bacteria source tracking can help identify non-point sources of fecal bacteria such as livestock, humans and wildlife. The Soil and Water Assessment Tool (SWAT) microbial sub-model 2005 was used to evaluate source-specific fecal bacteria using three years (2004–2006) of observed modified deterministic probability of bacteria source tracking data, as well as measure hydrologic and water quality data.This study modeled source-specific bacteria using a model previously calibrated for flow, sediment and total fecal coliform bacteria (FCB) concentration. The SWAT model was calibrated at the Rock Creek sub-watershed, validated at the Deer Creek sub-watershed, and verified at the Auburn sub-watershed and then at the entire Upper Wakarusa watershed for predicting daily flow, sediment, nutrients, total fecal bacteria, and source-specific fecal bacteria. Watershed characteristics for livestock, humans, and wildlife fecal bacterial sources were first modeled together then with three separate sources and combinations of source-specific FCB concentration: livestock and human, livestock and wildlife and human and wildlife. Model results indicated both coefficient of determination (R2) and Nash-Sutcliffe Efficiency Index (E) parameters ranging from 0.52 to 0.84 for daily flow and 0.50–0.87 for sediment (good to very good agreement); 0.14–0.85 for total phosphorus (poor to very good agreement); −3.55 to 0.79 for total nitrogen (unsatisfactory to very good agreement) and −2.2 to 0.52 for total fecal bacteria (unsatisfactory to good agreement). Model results generally determined decreased agreement for each single source of bacteria (R2 and E range from −5.03 to 0.39), potentially due to bacteria source tracking (BST) uncertainty and spatial variability. This study contributes to new knowledge in bacteria modeling and will help further understanding of uncertainty that exists in source-specific bacteria modeling.

Tracking Uncertainty with Probabilistic Logic Circuit Testing

The diverse nature of the faults and defects that may occur at nanoscale ranges necessitates new techniques for ATPG. This article proposes an efficient technique that relies on a probabilistic approach to detect and diagnose nontraditional faults and defects.

Concepts in Sediment Budgets

ROSATI, J.D., 2005, Concepts in sediment budgets. Journal of Coastal Research, 21(2), 307‐322. West Palm Beach (Florida), ISSN 0749-0208. The sediment budget is fundamental in coastal science and engineering. Budgets allow estimates to be made of the volume or volume rate of sediment entering and exiting a defined region of the coast and the surplus or deficit remaining in that region. Sediment budgets have been regularly employed with variations in approaches to determine the sources and sinks through application of the primary conservation of mass equation. Historically, sediment budgets have been constructed and displayed on paper or maps. Challenges in constructing a sediment budget include determining the appropriate boundaries of the budget and interior cells; defining the possible range of sediment transport pathways, and the relative magnitude of each; representing the uncertainty associated with values and assumptions in the budget; and testing the sensitivity of the series of budgets to variations in the unknown and temporally-changing values. These challenges are usually addressed by representing a series of budget alternatives that are ultimately drawn on paper, maps, or graphs. Applications of the methodology include detailed local-scale sediment budgets, such as for an inlet or beach fill project, and large-scale sediment budgets for the region surrounding the study area. The local-scale budget has calculation cells representing features on the order of 10s to 100s of meters, and it must be shown separately from the regional sediment budget, with cells ranging from 100s of meters to kilometers. This paper reviews commonly applied sediment budget concepts and introduces new considerations intended to make the sediment budget process more reliable, streamlined, and understandable. The need for both local and regional sediment budgets is discussed, and the utility of combining, or collapsing, cells is shown to be beneficial for local budgets within a regional system. Collapsing all cells within the budget creates a ‘‘macrobudget,’’ which can be applied to check for overall balance of values. An automated means of changing the magnitude of terms, while maintaining the same dependency on other values within the sediment budget, is presented. Finally, the need for and method of tracking uncertainty within the sediment budget, and a means for conducting sensitivity analyses, are discussed. These new concepts are demonstrated within the Sediment Budget Analysis System with an application for Long Island, New York, and Ocean City Inlet, Maryland.

A Critical Study of Decentralized Controllers for a Multivariable System

AbstractIndustrial processes are naturally multivariable in nature. A comparison of multivariable decentralized controllers has been performed and a multivariable four interconnected water tank system, which exhibits a RHP (Right Half Plane) zero is considered for decentralized control. The characteristics of a four‐tank system are discussed and a decentralized PI (Proportional‐Integral) controller is designed for the system based on various control algorithms. The algorithms are simulated using MATLAB and the performances are compared for reference tracking, disturbance rejection and model uncertainty cases. The comparison of the results show that the controllers based on Gershgorin band and Ideal relay method are suitable for the control of a four‐tank system.