GEOMETRIC ACCURACY OF A REAL-TIME TARGET TRACKING SYSTEM WITH DYNAMIC MULTILEAF COLLIMATOR TRACKING SYSTEM

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1 doi: /j.ijrobp Int. J. Radiation Oncology Biol. Phys., Vol. 65, No. 5, pp , 2006 Copyright 2006 Elsevier Inc. Printed in the USA. All rights reserved /06/$ see front matter PHYSICS CONTRIBUTION GEOMETRIC ACCURACY OF A REAL-TIME TARGET TRACKING SYSTEM WITH DYNAMIC MULTILEAF COLLIMATOR TRACKING SYSTEM PAUL J. KEALL, PH.D.,* HERBERT CATTELL, B.E., DAMODAR POKHREL, M.S.,* SONJA DIETERICH, PH.D., KENNETH H. WONG, PH.D., MARTIN J. MURPHY, PH.D.,* S. SASTRY VEDAM, PH.D.,* KRISHNI WIJESOORIYA, PH.D.,* AND RADHE MOHAN, PH.D. *Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA; Varian Medical Systems, Palo Alto, CA; Department of Radiation Medicine, Georgetown University Hospital, Washington, DC; Department of Radiology, Georgetown University Hospital, Washington, DC; and Department of Radiation Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX Purpose: Dynamically compensating for target motion during radiotherapy will increase treatment accuracy. A laboratory system for real-time target tracking with a dynamic MLC has been developed. In this study, the geometric accuracy limits of this DMLC target tracking system were evaluated. Methods and Materials: A motion simulator was programmed to follow patient-derived tumor motion paths, parallel to the leaf motion direction. A target attached to the simulator was optically tracked, and the leaf positions adjusted to continually align the DMLC beam aperture to the target. Analysis of the tracking accuracy was based on video images of the target and beam alignment. The system response time was determined and the tracking error measured. Response time corrected tracking accuracy was also calculated to investigate the accuracy limits of an improved system. Results: The response time of the system is ms. The geometric precision for tracking patient motion is 0.6 to 1.1 mm (1 ) for the 3 patient datasets tested, with tracking errors relative to the original patient motion of 35, 40, and 100%. Conclusions: A DMLC target tracking system has been developed that can account for detected motion parallel to the leaf motion direction. The tracking error has a negligible systematic component. Reducing the response time will further increase the overall system accuracy Elsevier Inc. Tumor tracking, Geometric accuracy, Dynamic multileaf collimator, Dynamic motion compensation. INTRODUCTION Reprint requests to: Paul J. Keall, Ph.D., Radiation Oncology, Stanford University, 875 Blake Wilbur Dr., Stanford, CA Tel: (650) ; Fax: (650) ; Paul.Keall@stanford.edu Supported by Grant Nos. R01CA93626 and R21CA from the National Institutes of Health and by a sponsored research agreement between Varian Medical Systems and VCU. Acknowledgments Dorin Todor helped to write the image analysis software. Hassan Mostafavi and Sergey Povzner gave The multileaf collimator (MLC) is a widely available technology for radiation therapy delivery. The technology to adjust leaf positions during beam delivery based on a predetermined leaf sequence facilitating intensity modulated radiation therapy is also quite mature (1). Another obvious application, though technically challenging, is to use the MLC to continuously realign the radiation beam to the target during therapy, dynamically compensating for any detected target motion. Proof-ofprinciple studies of this approach have been performed, (2 6) however to date no studies have reported on the implementation of dynamic MLC (DMLC) target tracking. Other options for aligning the radiation beam with a moving target during radiotherapy with dynamic motion compensation are robotic control of the linear accelerator (7, 8) (clinically available), block motion (9), and couch motion (10) (investigated, though not clinically available). A system for real-time tracking of targets with DMLC has been developed in a laboratory setting at Virginia Commonwealth University. The aim of the current work was to characterize the limits of geometric accuracy of a DMLC target tracking system. METHODS AND MATERIALS DMLC target tracking system A schematic diagram and photograph of the DMLC target tracking system are shown in Fig. 1. A motion simulator (11, 12) was programmed to follow patient tumor motion paths. Details of timely technical support and advice. Chris Bartee, Mark Hile, and Matthew Schaefer provided engineering support. Elisabeth Weiss and Michelle Svatos offered useful critique on the manuscript. Many others from VCU Radiation Oncology and Varian Medical Systems have also contributed to the VCU 4D Radiotherapy project. Received Feb 8, 2006, and in revised form April 18, Accepted for publication April 18,

2 1580 I. J. Radiation Oncology Biology Physics Volume 65, Number 5, 2006 recorded by the same device. Image analysis software was written using Matlab (Mathworks, Natick, MA) to segment the target and leaf reflective markers. Example images of the video files analyzed to obtain the data are shown in Fig. 2. A motion simulator was programmed to reproduce either periodic sinusoidal motion or nonperiodic patient motion. A sinusoidal curve with a 2 cm range of motion and a6speriod was used for the response time calculation, motion calibration, and accuracy determination under known conditions. Three representative patient motion examples, covering a range of breathing types and tumor locations, were simulated. The tumor motion data were acquired under IRB protocol at Georgetown University Hospital from Cyberknife (Accuray, Sunnyvale, CA) Synchrony treatments. Note that the motion data are periodically measured using orthogonal X-ray, and predicted between X-ray images based on an external optical signal (7). Brief details of the patient tumor motion are given below: Patient 1 was a middle-aged man with a lower lobe lung tumor. The tumor motion was regular with a range of motion of 10 to 15 mm. Patient 2 was a middle-aged woman with an advanced-stage right central lung tumor. The tumor motion was small and very irregular, with a motion period of approximately 1 s. Patient 3 was an elderly woman with a lower lobe lung tumor. The tumor motion was fairly regular with a range of motion of 6 to 10 mm. Fig. 1. Experimental set-up of the dynamic multileaf collimator (DMLC) target tracking system. (a) Schematic diagram; (b) labeled photograph. the patient data are given below. The motion was optically detected using the Varian (Palo Alto, CA) Real-Time Position Management (RPM) system in which images acquired with a camera (labeled as camera 1 in Fig. 1a) of a marker box with two infrared reflecting markers are segmented and positions calculated. These positions were sent via serial port to a computer. Software was written to calculate the required leaf positions to track the target based on the incoming position signal. These leaf positions were then communicated via ethernet to the MLC controller, which actuated the mechanical leaf motion of the MLC (Varian Millennium 120-leaf). For the studies described herein leaf sequences describing a circle were used, though the choice of leaf shape could be chosen to fit an arbitrary target. A separate beam s eye view camera (camera 2 in Fig. 1a) was used to synchronously measure the target and leaf motion. The camera-mlc-target distances (18 cm and 17 cm respectively) were a similar ratio to the actual source-mlc-target distances in a linear accelerator. Video images of the target motion, and DMLC motion were recorded and analyzed. The target for these studies was a reflective marker. To facilitate obtaining the DMLC position, reflective markers were placed on 2 of the leaves, 1 on each leaf bank, and the 2 marker positions averaged. The DMLC positions and the target motion were both measured by segmenting each image frame from the camera above the motion simulator. Thus the 2 signals (DMLC and target motion) were simultaneously The DMLC tracking system currently only accounts for motion in the superior-inferior (SI) direction, parallel to the leaf motion; the RPM system only tracks motion in the anterior-posterior (AP) direction. For these reasons only the SI component of the patient data were used (this has the largest magnitude of the 3 dimensions) to simulate both SI and AP motion so that the RPM system could acquire the motion data, and the DMLC tracking system compensate for it. For each of the cases, video images were acquired for approximately 60 s. Fig. 2. Images of the dynamic multileaf collimator (DMLC) target tracking for (a) downward target motion (inhale to exhale), (b) no motion (end exhale), and (c) upward target motion (exhale to inhale). The red circles/crosses are the segmented leaf and target center positions, the yellow circle/cross is the DMLC targeting center position. The DMLC is seen to be lagging behind the target motion because of the system response time.

3 Dynamic multileaf collimator tracking system P. J. KEALL et al EXPERIMENTAL PROCEDURES First, the uncertainty of the system needed to be established, to ensure the tracking errors found were credible. Next, we were interested in determining the system response time to understand to what extent the tracking error is limited by the present technologic implementation. The response time (or system latency), i.e., the time for the DMLC to respond to a given target motion, is the sum of the time for the tasks of image acquisition, segmentation and processing by the RPM system, leaf position calculation, data transfer, and leaf motion execution. We passively tracked the target first to determine the alignment between the DMLC and the target. The response time was estimated and the response time corrected average DMLC position was matched to the average target position. The subsequently recorded target and DMLC positions were used for analysis. This scenario is not dissimilar to what would occur for patient applications the DMLC would passively track the patient motion without radiation delivery until beam-target alignment occurred at which time radiation delivery would be initiated. This approach would also be used for resumption of an interruption during delivery (e.g., an interruption caused by a cough). Determining the uncertainty of the tracking system in the absence of target motion To determine the uncertainty of the measurement system, video images were recorded with the target static. The target and leaf MLC positions were segmented in the images using customwritten software. The high contrast of the image of the reflective markers used made the segmentation process relatively trivial. No relative motion of the two inputs was expected; any observed motion is an estimate of the system measurement uncertainty, which was used to put the subsequently analyzed tracking errors in context. Calibrating motion and calculating system response time The motion simulator was programmed to exhibit sinusoidal motion with a known range of motion (2 cm). Tracking the sinusoidal motion enabled the calibration of the requested leaf position change based on the incoming position information from the RPM system. The system response time is the time taken to complete the feedback loop shown in Fig. 1a. The effect of the response time on the DMLC position can be seen in Fig. 2, where the DMLC position is lagging behind the target position when in motion. Figure 3 also shows the DMLC positions are slightly offset from the target positions. The system response time was determined by computing the phase difference from a sinusoidal fit to the target motion and DMLC motion. Characterizing geometric tracking accuracy To determine the alignment between the DMLC and the target, both the DMLC and target motion were recorded for 6 s. The choice of 6 s is arbitrary, and is used to yield a reasonable estimate of the target position for alignment. The response time corrected (see following) average DMLC position was matched to the average target position over this time period. Subsequent recordings of the target position (beyond 6 s) and the DMLC positions were used for analysis. The analysis included computing the tracking error (difference between DMLC and target position), response time corrected tracking error (difference between response time corrected DMLC and target position) and the time integrated distributions (probability density functions) of these parameters. The response time corrected DMLC positions were computed by shifting the DMLC positions in the time axis by 2 image frames (130 ms). This data yields an estimate of the system accuracy if the feedback loop shown in Fig. 1 were completed in 30 ms. It should be noted that the response time corrected data cannot be realized with the current system, however is included to estimate the accuracy limits of an improved DMLC tracking system. RESULTS Determining the uncertainty of the tracking system in the absence of target motion With the target static, the system detected target motion of less than 0.1 mm (1 ), and recorded a tracking error of less than 0.15 mm (1 ). This measurement error is an estimate of the accuracy attainable with the current set-up, and is much lower than the tracking errors measured in their presence of motion (described below) indicating that the following results are reasonable estimates of their true values. Calculating system response time By computing the phase difference based on a sinusoidal fit to the target motion and DMLC motion for repeat tracking of sinusoidal motion, the average system response time was computed to be ms. This average value varies because of discrete events such as the RPM image acquisition (33-ms period), leaf position calculations (20-ms period) and the MLC cycle period (50-ms period). Characterizing geometric tracking accuracy The target tracking accuracy of the DMLC for sinusoidal and patient motion can be seen in Fig. 3. The DMLC is observed to lag behind the target (as expected based on the response time measurements). Because of this response time, the tracking error is largest when the target velocity is highest. If this response time were significantly reduced, the response time corrected DMLC curve matches very closely with the target motion. The resultant response time corrected tracking errors are very small. By integrating over time, probability density functions (pdfs) of these tracking errors were generated and are displayed in Fig. 4. The tracking error distribution is significantly lower than the initial target motion distribution; however, still has an appreciable width. Reducing the response time would further reduce the tracking error. The pdfs were quantified based on their means and standard deviations. The results are shown in Table 1. There are several interesting features. In the first column, the mean displacements are small, though nonzero, indicating that a shift had taken place since the initial alignment of the DMLC and target in the first few seconds. For Patient 3 a 1-mm mean difference was observed. Had the data acquisition been for longer than a minute, larger systematic differences may have been observed. The systematic tracking error is very small in all cases. For Patient 2, though the

4 1582 I. J. Radiation Oncology Biology Physics Volume 65, Number 5, 2006 Fig. 3. Position vs. time plots of the target position (red solid line), the dynamic multileaf collimator (DMLC) position (blue dashed line) and the response time corrected DMLC position (black dash dot line) for (a) a sinusoidal curve and (c) Patient I data. Position vs. time plots of DMLC tracking error (blue solid line) and the response time corrected DMLC tracking error for the sinusoidal curve (b) and Patient 1 data (d). target motion was on average small, because of the extremely high frequency and irregular breathing motion the tracking error was the same as the target motion indicating no benefit of tracking for this patient. The tracking error for patients 1 and 3 were 40% and 35% of the target motion respectively. Correcting for the response time of the system yields significantly lower tracking errors, of the order of 0.3 mm (1 ). DISCUSSION The geometric accuracy of a DMLC target tracking system to track sinusoidal and patient motion has been investigated. The observed tracking errors for the patient data, 0.6 to 1.1 mm (1 ) are encouraging, and give an estimate of what geometric errors may be expected of such a system when clinically implemented. The magnitude of tracking error is approximately equal to that calculated by Vedam et al. (13) for a response time of 160 ms ( 1 mm), though the patient data were different in these studies. The lack of improvement in the tracking results for Patient 2 are a reminder to be careful; indeed if the response time were longer it is likely the use of tracking would have reduced targeting accuracy over not tracking. Overall, the tracking errors are random in nature with a small systematic com-

5 Dynamic multileaf collimator tracking system P. J. KEALL et al Fig. 4. Probability density functions for (a) a sinusoidal curve and (b) Patient 1 data. Each plot shows three curves: the input target motion (red solid), the dynamic multileaf collimator (DMLC) tracking error (blue dashed line) and the response time corrected DMLC tracking error (black dash dot line). ponent. According to published margin formulas, random errors are less deleterious than systematic errors as shown, for example, in the table in van Herk et al. (14). However, their magnitude should still be quantified and accounted for appropriately. The dynamic motion compensation offered by DMLC-based target tracking means that if variations in the patient s initial treatment position are observed during treatment then the tracking system can automatically account for the change in position. An important component of any dynamic compensation system is the target position monitoring system. Any errors in this process will manifest themselves as tracking errors. The motion detection system used here was optically based. As the target was visible for the experiments, the input signal was highly correlated with the target motion. The use of optical motion signals as a surrogate for internal motion should be used with caution, as variations in correlation and phase shifts (15 20) have been observed between monitoring systems and internal structures. An ideal position monitoring system would have high accuracy, high update frequency, low processing time and give a large volume of information about the target (and normal structure) positions. The motion detection system used in the current work was limited to one dimension, thus the target tracking was also limited to accounting for motion along the leaf motion direction. Tumor motion has been observed to be predominantly in one direction, (21) however hysteresis was observed in some cases. For DMLC tracking it would be prudent to align the DMLC with the major axis of tumor motion. High-frequency (relative to target motion) 3D motion detection systems are available or becoming available, based on dual fluoroscopic, (22) combinedfluoroscopicandoptical,(7, 8) and electromagnetic technology (23). These 3D motion detection systems would integrate well with a DMLC tracking system. The results of the hypothetical response time corrected tracking error scenario show a clear pathway for where to focus future development efforts. An alternative to reducing the response time to improve accuracy is to incorporate motion prediction algorithms (13, 24 26). On reviewing results published by Vedam et al. (13), at the response time measured here (160 ms) motion prediction is likely to reduce the tracking error by 30%. The patient motion data used was well within the mechanical velocity and acceleration constraints of the DMLC Table 1. Mean (x ) and standard deviation ( ) of the target motion, tracking error and response-time corrected tracking error over a 60-s tracking period Target displacement Tracking error Tracking error (response-time corrected) Data source x (mm) (mm) x (mm) (mm) x (mm) (mm) Sine curve Patient Patient Patient

6 1584 I. J. Radiation Oncology Biology Physics Volume 65, Number 5, 2006 for measurements of the same MLC type, (27) and the addition of a beam hold during extremely rapid target motion would not have significantly increased the accuracy of the system. Such a beam hold would be desirable for the clinical implementation of DMLC tumor tracking to avoid treating during times of rapid target motion, e.g., coughing. The DMLC tracking system investigated here was only applied to lung applications, since this is one of the most challenging sites for tracking. However the technology is general enough to account for detected motion for other sites, e.g., prostate, pancreas, and liver. CONCLUSIONS A DMLC target tracking system has been developed that can account for detected motion parallel to the leaf direction. The response time of the current system is 160 ms. 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