RAMT

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.

Limb exercises are common in physical therapy to improve range of motion (RoM), strength, and flexibility of the arm/leg. To improve therapy outcomes and reduce cost, motion tracking systems have been used to monitor the user's movements when performing the exercises and provide guidance. Traditional motion tracking systems are based on either cameras or inertial measurement unit (IMU) sensors. Camera-based systems face problems caused by occlusion and lighting. Traditional IMU-based systems require at least two IMU sensors to track the motion of the entire limb, which is not convenient for use. In this paper, we propose a novel limb motion tracking system that uses a single 9-axis IMU sensor that is worn on the distal end joint of the limb (i.e., wrist for the arm or ankle for the leg). Limb motion tracking using a single IMU sensor is a challenging problem because 1) the noisy IMU data will cause drift problem when estimating position from the acceleration data, 2) the single IMU sensor measures the motion of only one joint but the limb motion consists of motion from multiple joints. To solve these problems, we propose a recurrent neural network (RNN) model to estimate the 3D positions of the distal end joint as well as the other joints of the limb (e.g., elbow or knee) from the noisy IMU data in real time. Our proposed approach achieves high accuracy with a median error of 7.2/7.1 cm for the wrist/elbow joint in leave-one-subject-out cross validation when tracking the arm motion, outperforming the state-of-the-art approach by more than 10%. In addition, the proposed model is lightweight, enabling real-time applications on mobile devices.Clinical relevance- This work has great potential to improve limb exercises monitoring and RoM measurement in home-based physical therapy. It is also cost effective and can be made available widely for immediate application.

Many motion tracking systems require solving inverse problem to compute the tracking result from original sensor measurements. For real-time motion tracking, such typical solutions as the Gauss-Newton method for solving their inverse problems need an initial value to optimize the cost function through iterations. A powerful initializer is crucial to generate a proper initial value for every time instance and, for achieving continuous accurate tracking without errors and rapid tracking recovery even when it is temporally interrupted. An improper initial value easily causes optimization divergence, and cannot always lead to reasonable solutions. Therefore, we propose a new initializer based on random-forest to obtain proper initial values for efficient real-time inverse problem computation. Our method trains a random-forest model with varied massive inputs and corresponding outputs and uses it as an initializer for runtime optimization. As an instance, we apply our initializer to IM3D[1], which is a real-time magnetic 3D motion tracking system with multiple tiny, identifiable, wireless, occlusion-free passive markers (LC coils).

The authors have developed a new method for real-time tumor tracking with dynamic multileaf-collimator (MLC) motion under condition of free breathing. Unlike other previously proposed tumor-tracking methods, their new method uses a preprogrammed dynamic MLC sequence in combination with real-time dose-rate control. This new scheme circumvents the technical challenge in MLC-based tumor tracking of having to control the MLC motion in real time, based on real-time detected tumor motion. With their new method, the movement of the tumor, as a function of breathing phase, amplitude, or tidal volume, is reflected in the preprogrammed MLC sequence. The irregularity of breathing during treatment is handled by real-time regulation of the machine dose rate, which effectively speeds up or slows down the delivery of radiation as needed. This method is based on the fact that all of the parameters in dynamic radiation delivery, including MLC motion, are enslaved to the cumulative dose, which, in turn, can be accelerated or decelerated by varying the dose rate. Because commercially available MLC systems do not allow the MLC delivery sequence to be modified in real time based on the patient's breathing signal, previously proposed tumor-tracking techniques using a MLC cannot be readily implemented in the clinic today. By using a preprogrammed MLC sequence to handle the required motion, the task for real-time control is greatly simplified. With their new scheme, which they call dose-rate-regulated tracking (DRRT), it is possible to use existing linear accelerators that have dynamic MLC capability to achieve real-time tumor tracking, provided that the beam dose rate can be controlled externally. Tracking-error evaluation for 13 patients out of 14 resulted in a tracking error of less than 1 mm (1 sigma), if the effect of the response time of the treatment machine on the dose-rate modulation can be neglected. Film measurements on a moving phantom with variable breathing patterns and DRRT delivery showed that 97% of the measurement points have gamma values less than 1 (for 3% and 2-mm criteria), while non-DRRT delivery showed only 87%. This study shows that real-time tracking is feasible with DRRT even when the patient breathing frequency is irregular. Effects of the variation of breathing amplitude and of base line drift on the tracking error with DRRT are discussed; pending further study, a criterion is suggested for patient selection in the application of this new technique in the clinic.

Clinical Experience with the Calypso® 4D Localization System in Prostate Cancer Patients: Implantation, Tolerance, Migration, Localization and Real Time Tracking

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.

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.

Training home robots to behave like human can help people with their daily chores and repetitive tasks. In this paper, we present Imirok, a system to remotely control robotic arms by user motion using low-cost, off-the-shelf mobile devices and webcam. The motion tracking algorithm detects user motion in real time, without classifier training or predefined action sets. Experimental results show that the system achieves 90% precision and recall rate on motion detection with blank background, and is robust under the change of cluttered background and user-to-camera distance.

Objective. To develop real-time 4D MRI using MR signature matching (MRSIGMA) for volumetric motion imaging in patients with pancreatic cancer on a 1.5T MR-Linac system. Approach. Two consecutive MRI scans with 3D golden-angle radial stack-of-stars acquisitions were performed on ten patients with inoperable pancreatic cancer. The complete first scan (905 angles) was used to compute a 4D motion dictionary including ten pairs of 3D motion images and signatures. The second scan was used for real-time imaging, where each angle (275 ms) was processed separately to match it to one of the dictionary entries. The complete second scan was also used to compute a 4D reference to assess motion tracking performance. Dice coefficients of the gross tumor volume (GTV) and two organs-at-risk (duodenum-stomach and small bowel) were calculated between signature matching and reference. In addition, volume changes, displacements, center of mass shifts, and Dice scores over time were calculated to characterize motion. Main results. Total imaging latency of MRSIGMA (acquisition + matching) was less than 300 ms. The Dice coefficients were 0.87 ± 0.06 (GTV), 0.86 ± 0.05 (duodenum-stomach), and 0.85 ± 0.05 (small bowel), which indicate high accuracy (high mean value) and low uncertainty (low standard deviation) of MRSIGMA for real-time motion tracking. The center of mass shift was 3.1 ± 2.0 mm (GTV), 5.3 ± 3.0 mm (duodenum-stomach), and 3.4 ± 1.5 mm (small bowel). The Dice scores over time (0.97 ± [0.01–0.03]) were similarly high for MRSIGMA and reference scans in all the three contours. Significance. This work demonstrates the feasibility of real-time 4D MRI using MRSIGMA for volumetric motion tracking on a 1.5T MR-Linac system. The high accuracy and low uncertainty of real-time MRSIGMA is an essential step towards continuous treatment adaptation of tumors affected by real-time respiratory motion and could ultimately improve treatment safety by optimizing ablative dose delivery near gastrointestinal organs.

To propose a real-time 3D MRI technique called MR SIGnature MAtching (MRSIGMA) for high-resolution volumetric imaging and motion tracking with very low imaging latency. MRSIGMA consists of two steps: (1) offline learning of a database of possible 3D motion states and corresponding motion signature ranges and (2) online matching of new motion signatures acquired in real time with prelearned motion states. Specifically, the offline learning step (non-real-time) reconstructs motion-resolved 4D images representing different motion states and assigns a unique motion range to each state. The online matching step (real-time) acquires motion signatures only and selects one of the prelearned 3D motion states for each newly acquired signature, which generates 3D images efficiently in real time. The MRSIGMA technique was evaluated on 15 golden-angle stack-of-stars liver data sets, and the performance of respiratory motion tracking with the online-generated real-time 3D MRI was compared with the corresponding 2D projections acquired in real time. The total latency of generating each 3D image during online matching was about 300ms, including acquisition of the motion signature data (~138ms) and corresponding matching process (~150ms). Linear correlation assessment suggested excellent correlation (R2 =0.948) between motion displacement measured from the online-generated real-time 3D images and the 2D real-time projections. This proof-of-concept study demonstrates the feasibility of MRSIGMA for high-resolution real-time volumetric imaging, which shifts the acquisition and reconstruction burden to an offline learning step and leaves fast online matching for online imaging with very low imaging latency. The MRSIGMA technique can potentially be used for real-time motion tracking in MRI-guided radiation therapy.

In this paper we propose a particle swarm optimization with resampling for marker-less body tracking. The resampling is employed to select a record of the best particles according to the weights of particles making up the swarm. The algorithm better copes with noise and reduces the premature stagnation. Experiments on 4-camera datasets show the robustness and accuracy of our method. It was evaluated on nine sequences using ground truth provided by Vicon. The full body motion tracking was conducted in real-time on two PC nodes, each of them with two multi-core CPUs with hyper-threading, connected by 1 GigE.KeywordsParticle Swarm OptimizationHuman MotionMotion CaptureGood ParticleBody TrackingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

At image-guided radiotherapy, technique, different imaging, and monitoring systems are utilized for (i) organs border detection and tumor delineation during the treatment planning process and (ii) patient setup and tumor localization at pretreatment step and (iii) real-time tumor motion tracking for dynamic thorax tumors during the treatment. In this study, the effect of fuzzy logic is quantitatively investigated at different steps of image-guided radiotherapy. Fuzzy logic-based models and algorithms have been implemented at three steps, and the obtained results are compared with commonly available strategies. Required data are (i) real patients treated with Synchrony Cyberknife system at Georgetown University Hospital for real-time tumor motion prediction, (ii) computed tomography images taken from real patients for geometrical setup, and also (iii) tomography images of an anthropomorphic phantom for tumor delineation process. In real-time tumor tracking, the targeting error averages of the fuzzy correlation model in comparison with the Cyberknife modeler are 4.57 mm and 8.97 mm, respectively, for a given patient that shows remarkable error reduction. In the case of patient geometrical setup, the fuzzy logic-based algorithm has better influence in comparing with the artificial neural network, while the setup error averages is reduced from 1.47 to 0.4432 mm using the fuzzy logic-based method, for a given patient.Finally, the obtained results show that the fuzzy logic based image processing algorithm exhibits much better performance for edge detection compared to four conventional operators. This study is an effort to show that fuzzy logic based algorithms are also highly applicable at image-guided radiotherapy as one of the important treatment modalities for tumor delineation, patient setup error reduction, and intrafractional motion error compensation due to their inherent properties.

Designing a real-time visual tracking system to catch goldfishes is a very complex task because a fish’s precise relative 3D positions inside a water tank are difficult to determine. In this paper, we present a new visual servo control system using stereo vision to acquire the 3D positions of goldfishes. The continuously adaptive mean shift (CAMSHIFT) algorithm and optical flow vision tracking methods are applied to provide feedback of a tracked fish’s real-time position and motion trajectory in a water tank at a high frame-rate. Two low-cost, well calibrated cameras are used to track the goldfishes and a 5-axis robot manipulator is used to catch a specified goldfish. The designed visual tracking and servo system is less sensitive to lighting influences and thus performs more efficiently. Experiments of the proposed method yielded very good results as the real-time 3D vision successfully tracked two fishes and guided the manipulator to catching one of them in a net attached to its end-effector.

This paper demonstrates a capability to use a developed embedded sensor suite to consistently track the position, motion behavior, and orientation of a canine. Quantifying and recording canine position and motion in real time provides a useful mechanism for objective analysis of canine trials and missions. We provide a detailed description of the sensor equipment, including the global position satellite (GPS) receiver and antenna, accelerometers, gyroscopes, and magnetometers. Sensors beyond GPS provide for higher frequency readings, a tolerance to GPS loss, and the ability to characterize canine orientation. We demonstrate integrating sensor measurements using an Extended Kalman Filter (EKF) to estimate the canine position and velocity during temporary GPS loss. The system supports the remote actuation of tone and vibration commands and reports commands in real time alongside sensor data. This extends the range at which a handler could monitor a canine and allows enhanced trial analysis using raw sensor data and visualizations. To illustrate the system capabilities, we performed a case study in the remote command and navigation of a trained canine by a professional trainer. The results of this case study are analyzed in terms of canine trial success, motion behavior analysis, and in the context of simulated GPS losses. We discuss other potential applications of the system in autonomous canine command, canine motion analysis, and non-canine applications.

Science and technology are ever more frequently used in sports for achieving the competitive advantage. Motion tracking systems, in connection to the biomechanical biofeedback, help in accelerating motor learning. Requirements about various parameters important in real-time biofeedback applications are discussed. Special focus is given on feedback loop delays and its real-time operation. Optical tracking and inertial sensor tracking systems are presented and compared. Real-time sensor signal acquisitions and real-time processing challenges, in connection to biomechanical biofeedback, are presented. This paper can serve as a starting point for determining the adequate combination of technical equipment and its specifications that work favorably for the operation of the planned real-time biofeedback application.