UT modeling based particle filter algorithm for respiratory tumor tracking in robotized radiosurgery

Visual tracking is one of the key tasks in computer vision. The particle filter algorithm has been extensively used to tackle this problem due to its flexibility. However the conventional particle filter uses system transition as the proposal distribution, frequently resulting in poor priors for the filtering step. The main reason is that it is difficult, if not impossible, to accurately model the target's motion. Such a proposal distribution does not take into account the current observations. It is not a trivial task to devise a satisfactory proposal distribution for the particle filter. In this paper we advance a general augmented particle filtering framework for designing the optimal proposal distribution. The essential idea is to augment a second filter's estimate into the proposal distribution design. We then show that several existing improved particle filters can be rationalised within this general framework. Based on this framework we further propose variant algorithms for robust and efficient visual tracking. Experiments indicate that the augmented particle filters are more efficient and robust than the conventional particle filter.

Respiration Motion and Tumor Tracking Techniques.- Tumor Motion Ranges Due to Respiration and Respiratory Motion Characteristics.- Respiratory Motion Tracking for Robotic Radiosurgery.- Patient Alignment and Target Tracking in Radiosurgery of Soft-Tissue Tumors Using Combined Fiducial and Skeletal Structures Tracking Techniques.- Thoraco-Abdominal Dosimetry, Radiobiology, and Imaging.- The Radiobiological Rationale for Hypofractionation of Lung and Liver Tumors.- 18FDG-PET/CT for Treatment Planning and Follow-Up.- Treatment Planning of Abdominal Lesions.- Use of a Target Complexity Index in Radiosurgical Plan Evaluation.- Dose Escalation with the Frameless Respiratory Tumor Tracking System of the CyberKnife for Early Stage Non-Small-Cell Lung Cancer.- Thoraco-Abdominal Fiducial Placement Strategies.- Percutaneous Placement of Fiducial Markers for Thoracic Malignancies.- Use of a Blood Patch Technique to Prevent Pneumothorax During Insertion of Lung Fiducials.- Thoracic Fiducial Placement via Flexible Bronchoscopy.- Endoscopic Ultrasound-Guided Fiducial Placement for CyberKnife Radiosurgery.- Lung Tumor Treatment - Techniques and Experiment.- Stereotactic Body Radiation Therapy in the Treatment of Early Stage Non-Small Cell Lung Cancer.- Dosimetric Considerations for Stereotactic Body Radiotherapy Delivery in the Lung.- Selection of Treatment for Patients with Early Stage Non-Small Cell Lung Cancer in a Multidisciplinary Thoracic Oncology Program.- CyberKnife Frameless Image-Guided High-Dose Fractionated Stereotactic Radiosurgery with the Synchrony Motion Tracking Module in the Treatment of Single Small Peripheral Lung Tumors.- Initial Experience Treating Lung Tumors with the CyberKnife.- Stereotactic Radiosurgery for Early Stage Non-Small Cell Lung Cancer: Rationale, Patient Selection, Results and Complications.- Toxicity and Efficacy of Treating Mediastinal and Hilar Lesions.- Liver and Pancreas Treatment - Techniques and Experiment.- A Review of Stereotactic Body Radiotherapy for Liver and Pancreas.- Multidisciplinary Overview of Local-Regional Therapies for Liver Malignancies.- Stereotactic Radiosurgery for Liver Malignancies Using the CyberKnife.- CyberKnife Radiosurgery for Pancreatic Cancer.- Stereotactic Radiosurgery for Unresectable Adenocarcinoma of the Pancreas: Initial Experience at Sinai Hospital of Baltimore.- Emerging Thoraco-Abdominal Radiosurgery Concepts.- 4D Treatment Optimization and Planning for Radiosurgery with Respiratory Motion Tracking.- Xsight Lung Tracking System: A Fiducial-Less Method for Respiratory Motion Tracking.- CyberKnife Radiosurgery: Quality of Life and Cost Considerations.

Real-time tumor targeting involves the continuous realignment of the radiation beam with the tumor. Real-time tumor targeting offers several advantages such as improved accuracy of tumor treatment and reduced dose to surrounding tissue. Current limitations to this technique include mechanical motion constraints. The purpose of this study was to investigate an alternative treatment scenario using a moving average algorithm. The algorithm, using a suitable averaging period, accounts for variations in the average tumor position, but respiratory induced target position variations about this average are ignored during delivery and can be treated as a random error during planning. In order to test the method a comparison between five different treatment techniques was performed: (1) moving average algorithm, (2) real-time motion tracking, (3) respiration motion gating (at both inhale and exhale), (4) moving average gating (at both inhale and exhale) and (5) static beam delivery. Two data sets were used for the purpose of this analysis: (a) external respiratory-motion traces using different coaching techniques included 331 respiration motion traces from 24 lung-cancer patients acquired using three different breathing types [free breathing (FB), audio coaching (A) and audio-visual biofeedback (AV)]; (b) 3D tumor motion included implanted fiducial motion data for over 160 treatment fractions for 46 thoracic and abdominal cancer patients obtained from the Cyberknife Synchrony. The metrics used for comparison were the group systematic error (M), the standard deviation (SD) of the systematic error (sigma) and the root mean square of the random error (sigma). Margins were calculated using the formula by Stroom et al. [Int. J. Radiat. Oncol., Biol., Phys. 43(4), 905-919 (1999)]: 2sigma + 0.7sigma. The resultant calculations for implanted fiducial motion traces (all values in cm) show that M and sigma are negligible for moving average algorithm, moving average gating, and real-time tracking (i.e., M and sigma = 0 cm) compared to static beam (M = 0.02 cm and sigma = 0.16 cm) or gated beam delivery (M = -0.05 and 0.16 cm at both exhale and inhale, respectively, and sigma = 0.17 and 0.26 cm at both exhale and inhale, respectively). Moving average algorithm sigma = 0.22 cm has a slightly lower random error than static beam delivery sigma = 0.24 cm, though gating, moving average gating, and real-time tracking have much lower random error values for implanted fiducial motion. Similar trends were also observed for the results using the external respiratory motion data. Moving average algorithm delivery significantly reduces M and sigma compared with static beam delivery. The moving average algorithm removes the nonstationary part of the respiration motion which is also achieved by AV, and thus the addition of the moving average algorithm shows little improvement with AV. Overall, a moving average algorithm shows margin reduction compared with gating and static beam delivery, and may have some mechanical advantages over real-time tracking when the beam is aligned with the target and patient compliance advantages over real-time tracking when the target is aligned to the beam.

BackgroundSurface-guided radiation therapy (SGRT) employs a non-invasive real-time optical surface imaging (OSI) technique for patient surface motion monitoring during radiotherapy. The main purpose of this study is to verify the real-time tracking accuracy of SGRT for respiratory motion and provide a fitting method to detect the time delay of gating.MethodsA respiratory motion phantom was utilized to simulate respiratory motion using 17 cosine breathing pattern curves with various periods and amplitudes. The motion tracking of the phantom was performed by the Catalyst™ system. The tracking accuracy of the system (with period and amplitude variations) was evaluated by analyzing the adjusted coefficient of determination (A_R2) and root mean square error (RMSE). Furthermore, 13 actual respiratory curves, which were categorized into regular and irregular patterns, were selected and then simulated by the phantom. The Fourier transform was applied to the respiratory curves, and tracking accuracy was compared through the quantitative analyses of curve similarity using the Pearson correlation coefficient (PCC). In addition, the time delay of amplitude-based respiratory-gating radiotherapy based on the OSI system with various beam hold times was tested using film dosimetry for the Elekta Versa-HD and Varian Edge linacs. A dose convolution-fitting method was provided to accurately measure the beam-on and beam-off time delays.ResultsA_R2 and RMSE for the cosine curves were 0.9990–0.9996 and 0.110–0.241 mm for periods ranging from 1 s to 10 s and 0.9990–0.9994 and 0.059–0.175 mm for amplitudes ranging from 3 mm to 15 mm. The PCC for the actual respiratory curves ranged from 0.9955 to 0.9994, which was not significantly affected by breathing patterns. For gating radiotherapy, the average beam-on and beam-off time delays were 1664 ± 72 and 25 ± 30 ms for Versa-HD and 303 ± 45 and 34 ± 25 ms for Edge, respectively. The time delay was relatively stable as the beam hold time increased.ConclusionsThe OSI technique provides high accuracy for respiratory motion tracking. The proposed dose convolution-fitting method can accurately measure the time delay of respiratory-gating radiotherapy. When the OSI technique is used for respiratory-gating radiotherapy, the time delay for the beam-on is considerably longer than the beam-off.

The underwater target tracking environment usually has the characteristics of strong nonlinearity and non-Gaussian. The target tracking problem usually uses the nonlinear filtering algorithm, which combines the nonlinear measurement model with the linear system dynamics. The primary goal of target tracking is to extract accurate information about the real-time state of the target from the noise nonlinear observation obtained by the sensor. A new Gaussian Hermitt Kalman particle filter algorithm (GHKF-PF) is used to improve the tracking accuracy of the particle filter algorithm (PF). GHKF-PF uses the GHKF sampling method to sample particles from the posterior distribution of the target, calculates the mean and covariance for each particle, and uses the mean and variance to guide sampling. In the simulation experiment, the uniform linear motion model of underwater 3D target in the environment of Gaussian mixture noise (GMN) is established, and the application of GHKF-PF algorithm in 3D space is realized, and the accuracy is higher than that of PF. In order to further verify the effectiveness of the algorithm, a six-dimensional uniform acceleration motion model is established and compared with the extended particle filter (EPF) algorithm and the unscented particle filter (UPF) algorithm. The simulation results show that the performance of the GHKF-PF algorithm is similar to that of the UPF algorithm and better than the EPF algorithm.

To deal with the problem of ballistic reentry target trajectory tracking, a novel particle filter algorithm is proposed to mitigate the particle degeneracy. It can eliminate the inefficient particles by judging the degree of particle degradation at each time and generates new particles based on the estimation state values and their covariance according to the degeneracy ratio of particles. Compared with Standard Particle Filter (SPF) algorithm and Unscented Particle Filter (UPF) algorithm, simulation results show that the proposed algorithm can achieve better tracking performance with lower computational burden for the trajectory reentry target tracking model. Especially in the case of ballistic target maneuver, the performance stability of the proposed algorithm is even more significant.

In this paper, a modified particle swarm optimization (PSO) approach, particle swarm optimization with<i> ε- greedy</i> exploration εPSO), is used to tackle the object tracking. In the modified εPSO algorithm, the cooperative learning mechanism among individuals has been introduced, namely, particles not only adjust its own flying speed according to itself and the best individual of the swarm but also learn from other best individuals according to certain probability. This kind of biologically-inspired mutual-learning behavior can help to find the global optimum solution with better convergence speed and accuracy. The εPSO algorithm has been tested on benchmark function and demonstrated its effectiveness in high-dimension multi-modal optimization. In addition to the standard benchmark study, we also combined our new εPSO approach with the traditional particle filter (PF) algorithm on the object tracking task, such as car tracking in complex environment. Comparative studies between our εPSO combined PF algorithm with those of existing techniques, such as the particle filter (PF) and classic PSO combined PF will be used to verify and validate the performance of our approach.

In order to solve the problem of particle dilution in traditional particle filter (PF) estimation of Ni-Cd Alkaline battery state of charge (SOC), a bat algorithm optimized particle filter was proposed to estimate SOC. The particles are represented as bat individuals, and the predatory process of bat population is simulated to solve the problem of particle dilution in particle filter technology; the theoretical model of battery state space is constructed by combining the second-order Thevenin battery model, and the related parameters of battery are identified; the SOC is estimated by using BA-PF algorithm and standard PF algorithm under pulse current condition. The experimental results show that, compared with the traditional PF algorithm, the SOC estimation accuracy of Ni-Cd alkaline battery based on BA-PF is less than 2%, and it has good adaptability and stability for nonlinear and non-Gaussian characteristics of Ni-Cd alkaline battery.

To solve the problem of time-consuming, laborious, and low accuracy in magnetic map construction, a trajectory guided 2-D magnetic map construction method is proposed in this article. The absolute displacement calculated by the inertial navigation system is mapped to the real trajectory to construct the magnetic map. The precision of the constructed magnetic map can reach 0.1141 m, and the mapping efficiency is greater than 1800 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{\mathbf {2}}$ </tex-math></inline-formula> /h. Moreover, the cost of sensor is only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 15.4. To deal with the instability problem that the traditional particle filter (PF) in pedestrian dead reckoning (PDR) suffers from, this article proposes an integrated PF (IPF) algorithm. The algorithm uses two assumptions: 1) all noises obey Gaussian distribution and 2) the initial position and the results obtained from IPF are reliable enough. The particle distribution in the case of infinite particles is simulated, and similar particles are integrated. The algorithm breaks through the limit of particle number of PF and greatly reduces the randomness of PF algorithm. The real-world experiments show that the proposed positioning method can achieve an average positioning error of 0.32 m.

To measure respiratory motion of the thoracic wall region in dogs using a real-time motion tracking system and compare the amount of respiratory motion between dogs positioned with and without a vacuum-formable cushion. 8 healthy adult mixed-breed dogs (median weight, 23 kg). Dogs were anesthetized and positioned in sternal and dorsal recumbency with and without a vacuum-formable cushion. Three-dimensional movement of anatomic landmarks was measured with a real-time motion capture system that tracked the locations of infrared light-emitting diodes attached externally to the dorsal or ventral and lateral aspects of the thoracic wall. Dogs positioned in sternal recumbency had significantly less cranial-to-caudal and left-to-right respiratory motion at the lateral aspect of the thoracic wall, compared with dogs positioned in dorsal recumbency, whether or not a cushion was used. For dogs treated in sternal recumbency, use of a cushion significantly increased the peak displacement vector (overall movement in 3-D space) for 3 of 4 marker locations on the dorsal thoracic wall. As respiratory rate increased, respiratory motion at the lateral and ventral aspects of the thoracic wall decreased when data for all dogs in dorsal recumbency were evaluated together. Associations between respiratory rate and respiratory motion suggested that the use of rapid, shallow ventilation may be beneficial for dogs undergoing highly conformal radiation treatment. These results provide a basis for further research on respiratory motion in anesthetized dogs.

We present the parallel particle filtering (PPF) software library, which enables hybrid shared-memory/distributedmemory parallelization of particle filtering (PF) algorithms combining the Message Passing Interface (MPI) with multithreading for multi-level parallelism. The library is implemented in Java and relies on OpenMPI's Java bindings for inter-process communication. It includes dynamic load balancing, multi-thread balancing, and several algorithmic improvements for PF, such as input-space domain decomposition. The PPF library hides the difficulties of efficient parallel programming of PF algorithms and provides application developers with a tool for parallel implementation of PF methods. We demonstrate the capabilities of the PPF library using two distributed PF algorithms in two scenarios with different numbers of particles. The PPF library runs a 38 million particle problem, corresponding to more than 1.86 TB of particle data, on 192 cores with 67% parallel efficiency.

Most of the countries have paid great attention to flood water level monitoring and tracking because flood may damages people's life and property. Since flood water level fluctuate highly nonlinear, it is very difficult to predict the flood water level. The particle filter algorithm is well known as a very effective solution for handling nonlinear problems. Thus, in this paper, this algorithm is applied to predict the flood water level. There are many variations of particle filter. This paper proposes Sequential Importance Sampling (SIS) particle filter to solve the above mentioned problem. SIS is the basic particle filter. However, the problems with SIS particle filter are the particle degeneration phenomenon, when after a few iterations only a few particles have nonzero weight. So, Sampling Importance Resampling (SIR) particle filter is also introduced as the improved particle filter. From the simulation results using Matlab, SIR particle filter outperforms SIS particle filter by comparing the Root Mean Square Error (RMSE) value.

liuyujiao2005@126.com Abstract Aiming at a nonlinear/non-Gaussian filter problem, the data processing of a single-yarn strength testing system, a filtering method based on the particle filter algorithm is put forward. This paper expounds the principle, and the working process and the procedures of particle filter. It discusses in detail the application of particle filter in the single-yarn strength testing, the modeling of testing system state equation, and the implementation of this algorithm in a project. It also indicates that particle filter algorithm used to process single-yarn strength test data achieves a good effect and satisfactory filtering precision by simulated tests. Finally, this paper predicts that particle filter will play an important role in general data processing and analyzing. The detection of the yarn quality is very important for cotton production. Yarn strength is the reflection of inner quality of yarn and is the necessary condition of yarn's processing performance and end-use. The unit of single-yarn strength is Newton (N) per centimeter (CN). By stretching testing with a single-yarn strength tester, the yarn's characteristic parameters will be obtained, and the physical index of breaking strength, breaking elongation, fracture strength, fracture time and strength tensile curves can be determined, so the quality of yarn will be ascertained (1) . This paper majors in the research of the parameter of the single-yarn strength of yarn. The strength of yarn will increase with increasing within a certain twist; however the strength of yarn will decrease when the twist exceeds the critical value. The relationship between yarn elongation and tension is often nonlinear. When the yarn quality is tested by single-yarn strength tester, the interference noise of test environment in the industrial field is non-Gaussian. So the processing of single-yarn strength testing data is a typical nonlinear and non-Gaussian filtering problem. The traditional digital filtering methods normally only filter out the caused by single reason and can't achieve satisfactory results for complex noise. In order to solve this problem, a large number of nonlinear recursive filtering algorithm such as extended Kalman filtering (EKF), the modified gain Kalman filter (MGEKF), U Kalman filter (UKF) algorithm come forth in recent years, and these filters are based on particular assumption to ensure the optimality. The actual data is usually very complex, and contains many factors such as non-Gaussian, nonlinear, high dimensional and noise; in this case, the Kalman filter generally can't get the analytical solution. This is a common problem and appears in many different kinds of fields (2-11) . Now, a new sequential Monte Carlo particle filter algorithm based on Bayesian principles has been paid more attention. The particle filter technology realize the recursive Bayesian filter with non- parametric Monte Carlo simulation method, which can fit to any state space model and the nonlinear systems which can't be expressed by traditional Kalman filter, and its accuracy approach the accuracy of optimal estimation (12) . This paper places emphasis on introducing the principle and method of the particle filter, and the application of the nonlinear data processing.

Purpose: As more techniques to monitor or compensate for patient respiratory motion before or during radiation treatment are used, analyzing the motion also becomes important. The starting point to analyze the respiratory motion data is breaking it into individual respiratory cycles to obtain the information on the motion, such as peak-to-trough distance and period. The purpose of this study was to develop a robust algorithm to separate patient respiratory motion data into individual respiratory cycles and to test it with a large dataset of patient respiratory tumor motion. Method and Materials: The 3D tumor motion data from 160 treatment fractions for 46 thoracic and abdominal cancer patients treated with Cyberknife Synchrony system was used. From the filtered motion data, smoothed curve (smoothed by moving average of 25 points) and a baseline (smoothed by moving average of 200 points) were generated and the intersections between these two curves were searched. Starting from the intersection where breathing is from a trough to a peak, a peak point was determined as a maximum point between adjacent intersections, and a trough point was determined as a minimum point between next adjacent intersections. Peak-to-trough distance and peak-to-peak period was calculated. Results and discussion: By using the smoothed data and a baseline, even very irregular respiratory tumor motion was correctly separated into individual cycles. However, there are some outliers where the algorithm failed to determine correct peak and trough points. For 160 treatment fractions, mean, minimum, and maximum values of mean respiratory peak-to-trough distance were 0.47 cm, 0.02 cm, and 1.44 cm, and those of mean respiratory peak-to-peak period were 3.79 sec, 2.18 sec, and 6.37 sec. Conclusions: This robust, general algorithm is useful to break patient respiratory tumor motion data into individual cycles, thus enables us to obtain the information on the respiratory tumor motion.

A novel infrared moving object tracking method based on particle filter and Mean Shift algorithm was presented.Firstly,it utilized the intensity distribution to represent infrared object,and constructed the observation probability model by statistical histogram.Then,Mean Shift algorithm was incorporated into the propagating process of particle filter,which induced particles distributing within the local area of observation.Compared to the conventional particle filter,the proposed method used much fewer particles to maintain the multi-mode distribution,and overcame the degeneration problem effectively.Experimental results on sequential images show that our method can track steadily when the object moves fast or is occluded,the overall performance of the proposed method is better than traditional particle filter algorithm.