Arguably, the best way to accommodate intrafraction

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1 Tracking Moving Organs in Real Time Martin J. Murphy In an ideal radiotherapy procedure, the treatment system would continuously adapt the radiation beam delivery to changes in the tumor position. The development of such a tracking capability has been underway for more than 10 years, beginning with the CyberKnife image-guided radiosurgery system. In that time, much has been learned about the nature of tumor motion and the technical issues that it presents to a practical realtime tracking system. In this article, I will review the From the Department of Radiation Oncology, Stanford University, Stanford, CA; and Accuray Incorporated, Sunnyvale, CA. Supported by the NIH under grant number R41CA , by Stanford University as part of the BIO-X Interdisciplinary Research Initiative, and by Accuray Incorporated of Sunnyvale CA. Address reprint requests to Martin J. Murphy, PhD, 2004 Elsevier Inc. All rights reserved /04/ $30.00/0 doi: /j.semradonc basic concepts behind existing and proposed radiotherapy beam-tracking systems, show clinical evidence of the types of movement that are encountered in realtime tracking situations, describe the corresponding technical problems and solutions, and discuss the unresolved issues in making real-time tumor tracking a practical response to tumor motion Elsevier Inc. All rights reserved. Arguably, the best way to accommodate intrafraction tumor motion would be to dynamically shift the dose in space so as to follow the tumor s changing position. This will be referred to as real-time tracking. Under ideal conditions, continuous real-time tracking can eliminate the need for a tumor motion margin in the dose distribution while maintaining a 100% beam duty cycle for efficient dose delivery. To succeed, a tracking system must be able to do 4 things: (1) determine the tumor position, (2) anticipate the tumor motion to allow for time delays in realignment of the beam, (3) reposition the beam, and (4) adapt the dosimetry to allow for changing tumor and critical structure configurations. All of this must be done automatically and in real time; real time refers to time scales that are short compared with the period over which the tumor moves appreciably. This article will review current techniques to accomplish each of these tasks, show data from clinical examples of realtime tracking, discuss known and potential difficulties, and offer recommendations for clinical and engineering research to further develop the technique. Intrafraction tumor motion can be of 2 types: stochastic (ie, random in time and direction) and systematic. Systematic motion can consist of slow, quasi-static changes in position due to effects such as muscle fatigue as well as rapid, cyclic changes caused by respiration and heartbeat. One should also distinguish between situations in whichh the entire patient moves with respect to the beam and situations in which the target organ moves not only with respect to the beam but also relative to other organs. If organs move significantly relative to one another, it becomes important to consider the dosimetric consequences. Finally, one should distinguish between short fractions (eg, conventional radiotherapy) and long fractions (as in radiosurgery). The duration of the fraction influences both the effect of motion and the techniques for tracking it. If patient movement is slow or intermittent, the response time of the tracking and beam positioning systems is not important. If the motion is fast relative to the speed of beam adjustment (as with respiration) then it becomes important to recognize and accommodate the delay in beam repositioning by predicting the future position of the target. This is only possible if the movement is systematic; fast random motions cannot be effectively predicted. Therefore, large and rapid stochastic movements cannot be effectively compensated via real-time tracking. In real-time tracking, the choice of method to measure tumor position is largely independent of the choice of method to realign the beam. Therefore these 2 aspects of the technique can be developed separately. On the other hand, both tumor measurement and beam alignment methods depend on the characteristics of tumor motion. We will emphasize techniques for tracking respiratory motion, which involves large and fast periodic movement of organs relative to one another, making it the most demanding target tracking scenario. Seminars in Radiation Oncology, Vol 14, No 1 ( January), 2004: pp

2 92 Martin J. Murphy Methods of Maintaining Beam Alignment in Real Time Real-time tracking requires the ability to automatically adjust the position of the beam relative to the moving target, allowing for all of the target s significant degrees of freedom. There are 4 possible ways to do this: (1) shift the patient using a remotely-controlled couch, (2) shift the beam by physically repositioning the radiation source (eg, a linear accelerator), (3) redirect the beam electromagnetically (for charged-particle beams), and (4) shift the aperture of a remotelycontrolled collimator. All of these methods require connecting a system for measuring tumor position through a real-time control loop to the beam alignment system. Because moving tumors have more degrees of freedom than a conventional gantry-mounted linear accelerator (LINAC) and couch, real-time tracking requires specialized beam/patient positioning tools. Automatic couch repositioning has been studied by Bel et al 1 and others. This approach is completely feasible for making intermittent adjustments to the patient s position in response to slow random or systematic movements. For respiratory motion, 2 complications arise. First, the patient would be in continuous motion during treatment, which would present problems of patient comfort and treatment tolerance. Second, the nature of the control loop driving the couch position is such that runaway positive feedback can occur. Avoiding this requires careful engineering of the control process. Shifting the aperture of a multileaf collimator (MLC) is under study by Keall et al, 2 Jiang et al, 3 Neicu et al, 4 and others. If the MLC is part of a conventional gantry-mounted LINAC, then beam alignment can be maintained only in the plane of the treatment field. Uncompensated out-of-plane translation represents only a small effect compared with in-plane translations, but if the tumor rotates out-of-plane the dose conformality can be compromised. Timing of the aperture alignment response must allow for the speed of the position measurement system, delays in the control link to the MLC, and speed limitations of the collimator leaves. Multileaf collimator leaf speeds can range from 0.5 to 2.5 cm/s; tumors can move at comparable speeds during respiration. For step-andshoot radiotherapy, the moving leaves only need to keep up with respiratory motion. In intensitymodulated radiation therapy, the target movement can be in phase with the preprogrammed leaf motion, requiring considerably greater leaf speeds that may at times exceed the MLC speed limit. Charged-particle beams can be almost instantaneously redirected by electromagnetic beam steering. This allows a proton beam to maintain continuous target alignment with a lag time that depends only on the timing of the position measurement system. However, the extremely limited availability of proton therapy resources keeps this approach out of reach for most clinicians. Automatic repositioning of an x-ray linear accelerator has been clinically implemented with the CyberKnife radiosurgery system. 5 In this approach, a lightweight 6-MV X-band LINAC is mounted to an industrial robotic arm that can freely move and orient the x-ray beam with six degrees of freedom. A dual-camera x-ray imaging system monitors the target position and automatically sends position information to the robotic arm, which redirects the treatment beam as the target moves. The robotic arm can move the LINAC several centimeters per second, which allows it to keep up with tumors that move with breathing. In principle, it can adapt to the full 3-dimensional motion of the target, although in some cases breathing-induced target rotations exceeding a few degrees could demand excessive response speeds. The CyberKnife was the first clinical radiotherapy system to use real-time motion compensation and remains the only such system in routine use. Its pioneering use for frameless image-guided cranial, 6 spine, 7-9 pancreatic, 10 and lung 11,12 radiosurgery has provided a wealth of data on the experience of tracking moving targets in a wide variety of treatment scenarios. Imaging To Determine the Tumor Position All approaches to real-time tracking require that one measure the target position on a time scale faster than the motion itself. Most commonly, the measurement is made via radiographic imaging. If the movement is fast and periodic, this can be supplemented by simultaneous measurements of other signals that are correlated with the motion, as for example respiratory movement of the chest

3 Tracking Moving Organs 93 and abdomen. This can reduce the imaging frequency and the resultant radiation dose. Research is being directed toward ultrasonic tumor localization 13,14 and the use of small implanted radiofrequency devices that can be magnetically tracked from outside the patient, 15 but these methods have not yet been reduced to clinical practice. There are 4 ways to locate the target site via radiography: (1) image the tumor itself, (2) image anatomical structures that are rigidly connected to the tumor (eg, bony landmarks), (3) detect artificial fiducials that are implanted in or near the tumor, and (4) track surrogate organs that move in synchrony with the tumor. The choice of method depends on the nature of the motion and the location and visibility of the tumor. Lesions within the cranium and along the spine are fixed with respect to skeletal structures. This is the basis for frame-based radiosurgery, which immobilizes the bony anatomy and thus the treatment site at the beam isocenter. In frameless image-guided radiosurgery, the skeletal features are located radiographically and used as targeting landmarks for real-time tracking. 5 The target position is found by registering the targeting radiographs to the computed tomography (CT) study used for treatment planning. This can be done by matching the radiographs to digitally reconstructed radiographs calculated from the CT data. 16 Image registration based on anatomic features is computationally intensive, making it difficult to acquire new target positions in less than a few seconds. Fortunately, the movements of most cranial and spine patients are infrequent and can be tracked by episodic (intermittent) rather than continuous imaging (see section 3 later). Tumors in soft tissue present a greater challenge to tracking during treatment. Occasionally, the tumor can be discerned in radiographs, as with the lung tumor in Figure 1. (This tumor also has targeting fiducials implanted in it.) In this case, the tumor position and orientation can be found by registering its outline to the treatment planning CT in the same manner as for a skeletal landmark (using the fiducials as secondary targeting landmarks). In most instances, the tumor itself cannot be seen well enough in the tracking radiograph to segment and register. Instead, surrogate landmarks must be used. Figure 1. A radiographic image of a lung tumor containing 4 gold fiducials, taken with a real-time amorphous silicon imaging system during a CyberKnife lung radiosurgery treatment. If the tumor movement is closely connected to the diaphragm, whose edge can easily be seen in a radiograph, then the diaphragm can be used as a surrogate landmark. Resipiratory motion of the liver and pancreas can offer this possibility. For other soft tissue tumors, it is necessary to implant artificial markers in or near the tumor (Fig 1). Markers for this purpose can be small cylindrical gold seeds 0.8 mm in diameter by 3 to 5 mm long, inserted percutaneously using a needle under image guidance. Such markers have been used for the prostate and lung. 11,12,20 For the pancreas, 2-mm diameter gold balls have been sutured to the tumor during exploratory laparoscopy. 10 The lower spine can be difficult to discern clearly in radiographs. Fiducials implanted in the spine can be used to provide enhanced targeting landmarks. This method has been used for the CyberKnife 8 and is also being investigated by others. 21 Fiducial-based guidance has the advantage that the fiducials are comparatively easy to locate with automatic image processing tools, and the position determination involves only a simple algebraic calculation. Thus, the time needed to make a position determination can be on the order of 50 msec, enabling real-time fluoroscopic

4 94 Martin J. Murphy tracking of the markers. 19,20,22 Their disadvantage is in the invasiveness of the procedure, which risks infection, and the possibility that the fiducials can move around in tissue between the time they are located in the planning CT study and the time they are used for targeting. If only 1 fiducial is used, there is no way to tell if it has moved. Furthermore, 1 fiducial gives only translational information; rotations cannot be measured. If 3 or more fiducials are used, rotations can be measured and migration can be detected. Additional fiducials, however, involve additional risk. Stochastic Motion Frameless image-guided radiosurgical treatments for cranial and spine tumors present examples of stochastic patient movement that can be tracked in quasi real time. The duration of a radiosurgical fraction allows the target position to be observed for 15 to 30 minutes, during which movement patterns can emerge. The strategy for tracking random movements assumes that most movements are small enough to be ignored and that large movements are infrequent. Rather than spend time and diagnostic imaging dose continuously observing the target position when it is not making consequential movements, the patient is instead imaged at intervals that are timed to detect and correct for the occasional large movement before a significant amount of dose has been misdirected. The frequency of imaging is related to the frequency of large movements. Imaging at intervals yields a random sample of the movements, which when collected into a frequency distribution can be used to estimate the optimal imaging frequency. The CyberKnife radiosurgery system uses the strategy of periodic intrafraction imaging to track cranial and spine tumors. During treatment, the cranial and cervical spine patients are restrained using an AquaPlast mask (AquaPlast Corp, Wyckoff, NJ), whereas the thoracic and lumbar spine patients lie supine and unrestrained in a conformable plastic foam cradle. This confines movement to a range that can be reliably tracked while avoiding the severe limitations of stereotactic frames. The tracking system records the target coordinates each time an image is acquired to update the target position. Figure 2. The record of episodic position measurements obtained while tracking the position of the cranium during a typical image-guided radiosurgery treatment. The imaging interval is approximately 1 minute. Figure 2 is an example of the target coordinates observed at 1-minute intervals during a typical 30-minute cranial CyberKnife treatment. It shows 2 features that are characteristic of most frameless cranial and spine treatments: (1) the fluctuations from one measurement to the next are usually very small and randomly directed, but (2) there can be slow systematic changes in mean position. If the beam is realigned after each measurement, then only the intermeasurement fluctuations perturb the dose alignment. The amount of perturbation depends on the relative frequencies of movement versus measurement, which provides a basis for optimizing the imaging interval. The position record for each treatment is a quasi-periodic sampling of actual movements. If a large number of these records are collected into a frequency distribution of position fluctuations, it is possible to estimate the actual pattern and frequency of patient movement by statistical inference. For 250 cranial patients treated with the CyberKnife at Stanford University, the observed fluctuations from 1 minute to the next followed an approximately Poisson distribution with a mean of 0.8 mm. 23 If we assume that the patient moves randomly in any direction with some average frequency and that the imaging system adds a random measurement error of 0.3 mm per axis, 24 then we can simulate the collection of position measurements and compare the results to the observed data. This allows us to model the frequency of movement and its effect on dose alignment. We find that the cranial position

5 Tracking Moving Organs 95 records observed at Stanford are consistent with a random patient movement rate of about once every 2 minutes. Under these conditions, the small fluctuations ( 0.5 mm) are mainly associated with measurement uncertainties, whereas the fluctuations greater than 1.0 mm are mainly associated with patient movement. For radiosurgical treatments, a target shift of more than 1 mm is usually considered significant. From the simulation, we estimate that if the imaging interval is approximately 2 minutes then the percentage of dose that is misdirected by more than 1 mm will be about 11%, whereas 2% of the dose will be misdirected by more than 1.5 mm and less than 1% will be off target by more than 2 mm. If the imaging interval is reduced to 1 minute, then the mistargeted dose will be reduced by half. Reducing the imaging interval further will have a diminishing effect on dose accuracy because the effect of large movements will have been almost eliminated, whereas measurement error will begin to account for most of the remaining target position fluctuations. We conclude that when using noninvasive restraints, an image-guided tracking interval of 1 to 2 minutes is sufficient to maintain a radiosurgical standard of targeting precision for most cranial and spine patients. Systematic Movement Image-guided radiosurgery can require 30 minutes per fraction to deliver the prescribed dose of radiation. During this period, the random cranial and spine fluctuations can be accompanied by a systematic change of the mean target position. This is apparent in Figure 2, in which the left/ right position of the cranium drifts 2 mm during the fraction. An uncorrected systematic drift in the mean target position will result in a progressively greater offset of the dose from its intended placement. Quasi real-time tracking can control this offset before it becomes significant. The Stanford CyberKnife data for cranial treatments show that for half of the patients the net change in target position over a 30-minute fraction was at least 3 times the average fluctuation. This represents a systematic target shift that is correctable by real-time tracking. If the net offset is accumulated from systematic small shifts, then imaging the target and realigning the beam every 1 to 2 minutes will reduce the effect of the systematic error to less than the random error. If the offset is abrupt, then an imaging interval of 1 minute will on average reduce the mistargeted dose to 2% or less of the total dose. Respiratory Motion Tumors in the lung, pancreas, liver, kidneys, and elsewhere can move up to several centimeters with breathing. This impacts all forms of external-beam radiotherapy. Compensation via dose margins will expose healthy tissue and limit tumor control probabilities, whereas motion control via breath holding or beam gating prolongs the treatment process. Respiratory motion therefore presents a particularly compelling case for realtime tracking. However, the speed of breathinginduced target motion poses 2 fundamental tracking problems: (1) measuring the tumor position in near real time and (2) predicting the tumor position to allow for time lag in the corrective beam response. The basic regularity of breathing offers hope that these 2 problems can be handled through a combination of observation and inference, but the irregular details of individual breathing behavior 25,26 can make it difficult for tracking to compete in accuracy with either breath holding or beam gating. 4,27 Determining the Tumor s Position The speed of breathing-induced target motion requires near-continuous position information. Ideally, the tumor itself would be observed continuously using, for example, fluoroscopic imaging. Shirato et al 20 have implemented this method for intrafraction tracking for conformal lung radiotherapy. During each fraction, dual fluoroscopes track a fiducial implanted in the tumor, providing 3-dimensional (3D) coordinates at 30 frames per second. The position information is used to trigger a beam gate. This has provided detailed 3D data on lung tumor trajectories. 22 Among other significant features, the tumor trajectories often follow different paths during inhalation and exhalation (hysteresis), which indicates that 2 or more driving mechanisms, operating at different phases, are responsible for the tumor s motion. This is a significant characteristic of respiratory motion that must be accommodated in tracking algorithms. Even with high imaging efficiency and the lowest possible exposure, fluoroscopic tracking delivers a skin dose of up to 2 cgy per minute of

6 96 Martin J. Murphy Figure 3. (A) The relationship between the cranial-caudal position of the pancreas and the cranial-caudal position of an external skin marker for a patient, measured fluoroscopically during free breathing. (B) For another patient, the relationship between the cranial-caudal position of a middle-lobe lung tumor and a surgical clip near the sternum, measured fluoroscopically during free breathing. treatment. 20 For some treatment scenarios (especially hypofractionated radiotherapy and radiosurgery), the fluoroscopic method would deliver too much imaging dose. This has led to investigation of ways to supplement imaging data with other signals of the tumor position. The general strategy is to establish a correlation between the position of the tumor, as seen during radiography, and some other passive measure of respiration, such as movement of the chest and abdomen or spirometry of air flow. Then episodic imaging is used during treatment to monitor tumor motion, whereas the correlated respiratory signal is used to continuously interpolate the target position between image acquisitions. If the correlation is robust and reasonably stationary, the imaging frequency can be reduced from 30 frames per second to once every few seconds. The correlated tracking strategy has been used to track lung tumors using the CyberKnife 11,28 and is being investigated for MLCbased tracking. 2 Its feasibility depends on the strength and stability of the correlation. This has motivated clinical studies of respiratory motion for the pancreas and lung. 12,29 Figure 3A shows a measurement of pancreas motion during quiet breathing correlated with external abdominal movement, observed over a 180-second time interval using a fluoroscope. The pancreas motion was detected by observing fiducials sutured to the tumor while abdominal motion was signaled via external markers attached to the skin. These measurements show that pancreas motion is very closely correlated with external abdominal movement. The correlation is linear, stable over the period of imaging, and shows no evidence of phase shifts between the tumor and external marker (which would spread out the correlation). The tumor position can be inferred from the external marker position with submillimeter accuracy. Observations of several pancreas patients consistently showed a similar degree of correlation. This is the ideal situation for correlated tumor tracking, and we conclude that the pancreas is a good candidate for the technique. The motion of lung tumors is much more varied and complex. Tumors in the lower lobe near the diaphragm can show close and stable correlations with the external movement of the abdomen, but middle and upper lobe tumors are not mechanically coupled in any simple way with either the chest or the abdomen. This can lead to tumor/chest movement relationships such as in Figure 3B. For this patient, a middle lobe tumor s movement in the inferior/superior direction was measured synchronously with the inferior/superior movement of a surgical clip near the sternum. The apparent lack of correlation between clip and tumor is because of several effects: there

7 Tracking Moving Organs 97 is a phase difference between the clip and tumor movements, the relative amplitude of the 2 objects movement changes in time (the motion is nonstationary), and the tumor position is also perturbed by the heartbeat. If the tumor position is to be predicted by tracking an external respiration signal, then their relationship must be monitored on a time scale of a few seconds by a tracking algorithm that can adapt to constantly changing phase and amplitude relationships between the 2 objects. This is a situation that requires the use of adaptive control filters or other sophisticated algorithms. 29 Predicting Respiratory Movement Real-time respiratory tracking requires that beam realignment be synchronized with a target that is continuously moving at up to 2 cm per second. At such speeds, a time delay of 100 ms between measuring the target position (by whatever means) and enacting the beam shift will cause the beam to lag by up to 2 mm behind the actual target position. A systematic tracking error of 2 mm can be significant in hypofractionated therapy and radiosurgery. Therefore, to maintain optimal accuracy, the tracking algorithm must be able to predict the future position of the target by an amount equal to the delay. The control loop of any real-time tracking system for radiotherapy will almost inevitably contain delays of more than 50 to 500 ms (fluoroscopic frame intervals alone are 31 ms), which makes target movement prediction a necessary part of the tracking process. Superficially, the respiratory cycle has a regularity that suggests that predicting up to several hundred milliseconds in advance should be feasible. The actual problem is complicated by several facts: (1) the breathing cycle is not strictly regular, but varies in amplitude and period from 1 cycle to the next 25,26,30 ; (2) most of the target motion occurs during a short segment of the cycle; (3) the target position measurements have uncertainties; and (4) if the target position is being deduced from a surrogate breathing signal, the uncertainty in predicting the future tumor position is compounded by the uncertainty in relating target and surrogate motion. Figure 4 shows an example of a measurement of lung tumor position and simultaneous skin marker movement over time, showing all of these complications. Figure 4. The cranial-caudal position versus time of an external chest marker during free breathing, measured simultaneously with the cranial-caudal position of a middle-lobe lung tumor. There are 2 basic strategies to predict the breathing motion: develop a mathematical model that represents the respiratory cycle as a parameterized periodic function 30,31 or use an empirical algorithm that predicts future movements based on a running sample of past movements. The empirical approach uses signal-processing algorithms known as adaptive filters. For lung tumor tracking Seppenwoolde et al 22 and Neicu et al 4 have used the model-based approach. Murphy et al 29 has investigated the adaptive filter approach. Adaptive filters are commonly used in realtime control processes that must synchronize a response to input signals produced by mechanisms that are completely unknown, or very complex, or noisy and variable. This can make modelbased prediction inaccurate or impractical. Adaptive filtering is based on the empirical characteristics of the signal itself rather than a model of its source. (Strictly speaking, not all adaptive filters are entirely empirical; Kalman filters use source models combined with empirical sampling.) In its simplest form, a predictive filter collects samples of a signal for a period of time and makes a best estimate of the next discrete sample of the signal from a weighted linear combination of the past samples. If the signal is stationary (ie, has an unchanging time average), the filter will rapidly converge to an optimal estimate of the signal at any future point in time. If the signal is not stationary, a more sophisticated adaptive filter is required. These algorithms continuously adjust their coefficients to the timechanging characteristics of the signal as it is observed.

8 98 Martin J. Murphy Respiration presents a situation for adaptive filtering because, as Figure 4 shows, the pattern of respiration is often not stationary. One consequence of nonstationarity is that the filter can no longer predict arbitrarily far into the future no matter how much data it collects from past signals; instead the accuracy diminishes as one looks further ahead. This has important consequences for real-time respiratory tracking. Preliminary studies of respiration prediction with adaptive filters 29 have shown that the standard deviation in estimating the peak-to-peak amplitude of external chest motion can range from 4% for 200-ms lead time to 9% at 500 ms to 14% at 800 ms, whereas the error in estimating the peak-topeak position of a lung tumor can increase from 8% at 200-ms lead time to 17% at 500 ms and up to 24% at 800 ms. An SD of 36% corresponds to no predictive ability at all (ie, the estimate is no better than a time average of the signal). This strongly suggests that delays in respiratory tracking systems should be kept well below 500 ms. Discussion Image-guided cranial and spine radiosurgery requires the highest precision standard for tracking of the tumor. We find that if conformable patient supports and noninvasive restraints are used in place of a stereotactic frame then it is usually sufficient to detect and correct for residual motion at approximately 1-minute intervals. This is completely feasible with commonly used imaging systems, image registration algorithms, and beam/patient positioning technology. Therefore, we conclude that this form of real-time tracking has been completely reduced to practice and should be considered to be the clinical state of the art. The tracking is real time only in the sense that the time scale for corrective response is short compared with the frequency of clinically significant movements. For respiratory motion, we find that the time scales for tumor motion and corrective response closely approximate what we understand to be continuous real-time tracking and that these time scales present significant technical challenges. For example, most patients will have breathing cycles of 3 to 4 seconds during which the tumor can move 1 to 3 cm. Most of the motion occurs in less than 1 second. Thus, the detection and response to motion must occur in much less than 1 second. This requires fast position measurement and algorithms that can accurately predict the breathing cycle up to several hundred milliseconds in advance. The physiology of breathing makes this nontrivial for many patients. We conclude that the general problem of timing in respiratory tracking remains at the edge of our technical ability. Until it becomes possible to track tumors in 3D using ultrasound or electromagnetic sensors, radiography will remain the predominant tool for finding and monitoring the tumor position. This raises issues related to the accumulation of radiographic dose during treatment. One commonly used criterion for judging imaging dose in a radiotherapy context is to compare it with the leakage and scatter dose from the therapy beam. If it is less than the primary beam background, it can be considered to be another, lower-level source of background. However, the spectrum and geometry of the diagnostic dose is very different from the treatment beam background, making this a difficult criterion to interpret. Thus, this question remains unresolved, although some limiting situations can be defined. A typical image-guided cranial or spine radiosurgery treatment calls for a pair of positioning images every minute. The spine is much harder to discern than the cranium so it sets the upper bound on imaging dose. If fiducials are implanted in the spine, they can be detected with exposures of about 10 to 20 mrad. A typical radiosurgery fraction of 1,500 to 3,000 cgy will last 30 minutes and involve perhaps 30 radiographic image pairs, for a total diagnostic dose of about 0.6 to 1.2 cgy. The background from an x-ray LINAC is typically about 0.2% of the primary beam (ie, 3-6 cgy). In this scenario, the imaging dose is less than (but not much less than) the leakage dose. Shirato et al 20 have measured the imaging dose delivered during continuous fluoroscopy to track tumors. To detect fiducials in the lung, they use entrance doses of 2 cgy per fraction for 30 fractions. The total therapeutic dose is 6,000 cgy so the diagnostic dose is 5 times the leakage dose. This represents what is most likely an upper limit for diagnostic imaging dose. If the lung treatment were performed in 1 fraction, the imaging dose would be 60 cgy all at once, which would be too much. This has motivated research into ways to supplement imaging data with other tumor position information.

9 Tracking Moving Organs 99 When tracking respiratory motion, if the imaging frequency is less than about 2 images per second, then the tumor position must be interpolated using some external signal of the respiratory cycle. This requires that the tumor motion be correlated to other movements. Clinical data show that the motion of the pancreas usually has a clear and stable correlation with abdominal surface motion. We conclude that pancreas tracking can be done using a combination of radiographic imaging and external respiratory tracking. The relationship between lung tumor motion and external breathing signals is much more complex than for the pancreas, involving timechanging periods, amplitudes, and phase differences. We conclude that deducing lung tumor position using surrogate breathing signals is at the frontier of real-time tracking and has not yet been reduced to routine clinical practice. The improvement of tracking algorithms for lung tumors requires the acquisition and analysis of considerably more clinical data for tumor motion. In these studies, it is essential that the motion of the tumor itself be observed and measured; any assumption that the tumor moves in strict synchrony with other anatomy is unfounded. It is tempting to assume that if one observes a stable and linear correlation between tumor and external marker motion in a pretreatment fluoroscopic study, then the patient will exhibit the same behavior during treatment, allowing one to dispense with intra-fraction imaging and base the beam tracking solely on the external respiration signal. This assumption is often contradicted by observations of respiratory motion. We conclude that respiratory motion tracking systems should have an intrafraction imaging capability. Sometimes the tumor will rotate during breathing. Evidence for this is seen in fluoroscopic lung and pancreas studies. If the tumor shape is highly irregular, rotations can have a significant effect on dose coverage. An MLC that tracks the tumor can potentially have its aperture reconfigured to compensate for in-plane rotations, but this will require a level of sophistication that is far beyond what is presently being developed. On the other hand, because of the radial distance from target to LINAC, some respiratory rotations can be too large for a robotmounted LINAC to follow. Thus, large rotational movements during respiration are potentially untrackable using present technology. Summary Real-time tracking has been shown to be a feasible and effective technique to deal with some common treatment scenarios. In its primary clinical application, image-guided tracking allows neurosurgeons to perform cranial radiosurgery without the severe constraints imposed by a stereotactic frame. Head movement can be constrained by simple noninvasive restraints, and the residual target motion can be compensated by a tracking system that makes episodic measurements and corrections for the target position. This was the first and remains the most common application of real-time tracking. Despite heroic efforts, 32 spine sites are very difficult to immobilize and thus are rarely treated with conventional frame-based radiosurgery. With the development of image-guided radiosurgery, it has become possible to treat sites along the spine without rigid immobilization. As with the cranium, these sites only move sporadically for a patient resting in simple conformable supports and thus can be tracked using simple episodic imaging and tracking. Image-guided spine radiosurgery thus represents a new treatment option specifically enabled by real-time tracking technology. The success of image-guided cranial and spine radiosurgery has stimulated the development of more sophisticated tracking procedures to deal with the more complex problem of respiratory target motion. This is where the developmental frontier presently lies. The basic problem of respiratory motion presents a situation that appears to be solvable in principle by real-time tracking, but the details of the motion present many practical challenges. Thus, the current view is one of guarded optimism that lung treatments for at least some patients can be conducted during unconstrained free breathing without resort to motion margins in the dose plan. To make this a reality will require more analysis of tumor motion data and more study of the influence of moving organs on the planned dosimetry. Motion algorithms developed for real-time tracking will have applicability to beam gating techniques as well, thus increasing the utility of the research.

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