Benchmarking of a motion sensing system - CiteSeerX

IOP PUBLISHING

PHYSICS IN MEDICINE AND BIOLOGY

Phys. Med. Biol. 53 (2008) 5845–5857

doi:10.1088/0031-9155/53/20/019

Benchmarking of a motion sensing system for medical imaging and radiotherapy Peter J Barnes1,4 , Clive Baldock1, Steven R Meikle2 and Roger R Fulton1,2,3 1 Institute of Medical Physics, School of Physics A28, University of Sydney, NSW 2006, Australia 2 Discipline of Medical Radiation Sciences and Brain and Mind Research Institute, University of Sydney, NSW 2006, Australia 3 Department of Medical Physics, Westmead Hospital, Sydney, NSW, Australia

E-mail: [email protected] and [email protected]

Received 29 February 2008, in final form 14 August 2008 Published 30 September 2008 Online at stacks.iop.org/PMB/53/5845 Abstract We have tested the performance of an Optotrak Certus system, which optically tracks multiple markers, in both position and time. To do this, we have developed custom code which enables a range of testing protocols, and make this code available to the community. We find that the Certus’ positional accuracy is very high, around 20 μm at a distance of 2.8 m. In contrast, we find that its timing accuracy is typically no better than around 5–10% for typical data rates, whether one is using an ethernet connection or a dedicated SCSI link from the system to a host computer. However, with our code we are able to attach very accurate timestamps to the data frames, and in cases where regularly-spaced data are not an absolute requirement, this will be more than adequate. (Some figures in this article are in colour only in the electronic version)

1. Introduction Breathing and other motions of patients during medical imaging degrade image quality, compromising diagnosis, and displace organs or tumours from their measured locations, complicating treatment (Webb 2006). For example, in whole-body positron-emission tomography (PET) imaging performed for cancer diagnosis and staging, movement of internal structures with respiration causes blur and a consequent signal dilution and loss of detection efficiency. Effective methods for motion compensation in PET brain imaging have recently been developed (Bloomfield et al 2003, Fulton et al 2002, Willowson et al 2006). However 4

Current address: Astronomy Department, University of Florida, Gainesville, FL 32611, USA.

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in whole-body imaging, non-rigid internal respiratory motion presents a much more difficult compensation problem. Despite this, there is considerable interest in respiratory motion compensation due to the widespread use of PET whole-body imaging for the detection and staging of cancer. Removing the effects of patient motion during an imaging or treatment session would yield obvious benefits to the diagnosis and treatment of disease, such as increased conformation of the delivered dose to the planning treatment volume (PTV) and reduction of the dose to healthy tissue. Various schemes have been developed in the last few years to solve the motion problem (see, e.g., Webb 2006). A major focus of this effort has been to accurately track the motion in order to quantify its effects. Such work can broadly be categorized into two types: optical tracking of external markers and direct tracking of internal motions. With the latter, which employs techniques such as fluoroscopy (Berbeco et al 2005c, Kini et al 2001, Seppenwoolde et al 2002, Vedam et al 2003), electromagnetic tracking (Hummel et al 2005, Schicho et al 2005), radio-opaque markers (Berbeco et al 2005b) and others, the uncertainties can be quite small since the motion of the organ is directly observed, or inferred from markers implanted within it. Also, errors are reduced to the instrumental resolution, which is typically ∼1 mm or less. However in this case the patient is often exposed to unwanted side effects from radiation due to fluoroscopic monitoring (Berbeco et al 2005b, Nill et al 2005, Webb 2006). In contrast, when tracking movement with external markers, these are used as indirect proxies for the actual movements of interest that occur in a given organ, such as the lung or liver (Renault et al 2005, Schweikard et al 2004). In this case the external motions must be related to the internal, and while non-trivial, this can be done with a variety of techniques such as gating, anatomical modelling or motion prediction (Berbeco et al 2006, Dawood et al 2007, Gierga et al 2005, Kini et al 2001, Tewatia et al 2006, Vedam et al 2003, Wolthaus et al 2005, Yan et al 2006). Although the corrections so derived do not completely compensate for the motion, they can reduce its effects significantly, resulting in positional errors which can drop from as much as 16 mm, down to as little as 1 mm (Berbeco et al 2006, Jin et al 2007, Kini et al 2001, Schweikard et al 2004, Vedam et al 2003). Moreover, with external markers potential side effects to the patient are minimized. However, internal motion compensation from external data is complex, and obtaining such results often requires sophisticated techniques in data acquisition, analysis and/or modelling. Many of these techniques are still being developed, and if the ultimate metric is to maximize patient benefits, it is not yet clear which technique will give the best outcomes; indeed this may vary depending on the particular application. In the case of optical tracking of external markers, there is much improvement still to be made. In order to further studies of this and other problems, we have recently acquired one such system. The Optotrak Certus (manufactured by Northern Digital Inc. [NDI], Waterloo, Ontario, Canada) is a sophisticated position-sensing camera, which uses active infrared-emitting diodes (IREDs, seen in figure 1(a)) as markers. These markers are detected and their positions measured by the trinocular camera system in the position sensor, seen in figure 1(b). The Certus can measure up to 512 individual markers, and/or a number of rigid bodies with embedded markers, simultaneously. For a smaller number of markers, the Certus does this at ‘frame rates’ above a kilohertz. However, the maximum theoretical frame rate (which is calculable using equation (1)) decreases as the number of markers being tracked increases. Nevertheless with, e.g., 50 individual markers, the maximum frame rate is still ∼88 Hz (see also the discussion following equation (1)). These capabilities are more advanced than many other systems for optical tracking, such as the Polaris system (also manufactured by NDI), which tracks fewer markers at slower frame rates. The Certus is comparable in capability to other passive-marker systems which have recently become commercially available, such as those from Vicon (Oxford, UK) or the ExacTrac system (BrainLab AG,

Benchmarking of a motion sensing system

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Figure 1. (a) Infrared markers and rigid body, magnetic clamp and micrometer stage. The marker measured in figure 3 is in the foreground, wrapped around part of a magnetic clamp which is in turn attached to the top of the stage. The markers measured in figure 4 are suspended on the poster board in the rear. The 4-marker rigid body used as the coordinate reference in both experiments appears to the left, at the base of the poster board. As viewed by the position sensor, the coordinate axes are aligned as follows: x increasing to the right of the micrometer stage, y increasing vertically upwards and z increasing backwards from the stage to the poster board (i.e., away from the position sensor). (b) Certus position sensor (background, mounted on its hydraulic steel stand) and control unit (left foreground, on table top). An ethernet minihub (white) used to connect the system to the LAN lies on top of the control unit.

Heimstetten, Germany) (e.g. Jin et al 2007). Such advanced tracking systems potentially provide a flexible and accurate way of non-invasively measuring patient motion, especially where non-rigid-body movements occur (such as in thoracic motion during breathing). While the external motions that could be tracked with such a system would have to be related to internal organ movements, with appropriate analysis the real prospect exists of correcting for the patient’s motions in the final image. As mentioned above, such motion correction has previously been achieved for head motions in the rigid-body case. These head motion studies used the less capable Polaris tracking system. Here we describe initial results of calibration and benchmarking of the Optotrak Certus system with these goals in mind. 2. Methods 2.1. The Optotrak Certus The Optotrak Certus system is comprised of individual IRED markers, rigid bodies or tools with embedded IRED markers and visible light-emitting diodes, a trinocular camera or position sensor and a control unit which relays information between the markers and the sensor. The Certus works by individually strobing the IRED markers at a high rate (the ‘marker rate’), once each within a ‘frame’ of time, and the sensor tracks these markers by determining their 3D or 6D position within each frame. (The visible LEDs on a rigid body or tool are also strobed,

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but at a fixed rate and only for user verification, i.e. they are not tracked by the Certus.) The ‘frame rate’ is set by the user in hertz; thus each frame occupies a window in time of (1/frame rate) seconds. Because each marker is individually strobed, there is no ambiguity about where any marker is, and so they are numbered and strobed in a fixed sequence, determined by the control unit and software. The control unit is connected to a host computer, which can be a Windows- or Linux-based machine, via any or all of three different ‘channels’ selected by the user: directly to a PCI card on the host computer; through a SCSI adapter and then to a SCSI card on the host computer; or by ethernet to any computer on the local network. NDI provides both pre-packaged software (only for the PC) and an Application Programmer’s Interface (API, written in C for either Windows or Linux) which can be used to send commands to, and receive data from, the Certus system. The API provides different methods in software to retrieve the data, independent of the type of physical connection. The data retrieval can be (a) in buffered mode, where frames of data are accumulated on-board the Certus and then bulk transferred to the host computer at intervals slower than the frame rate, or (b) in real time, where each frame is separately transmitted to the host computer as it is ready. Further, in real time the API calls can be made in ‘blocking’ mode, where each data request waits for the Certus to return a data frame, or ‘non-blocking’ mode, where the control programme can perform other functions between issuing a data request and later retrieval. The Certus returns position information as 3D marker positions, 6D positions and Euler angles in the case of rigid bodies and tools, or as quaternions, again at the user’s option. At the time of acquisition, there was no pre-packaged software for motion detection with the Certus; the existing NDI-provided software was primarily designed for static studies of fixed objects. We therefore used the API to write custom C-code for motion sensing in the Linux environment. Subsequently NDI produced a motion-detection package for Windows, but the latter did not have the capability for timing tests. All test results described below are therefore derived with our custom code, which is available upon request from the authors. 2.2. Position measurements We tested the Certus’ performance in both position and time. For position measurements, we did not attempt a rigorous assessment protocol like that of Hummel et al (2005), since initially we only wanted to approximately verify the manufacturer’s specifications. Neither did we attempt any tests of angular accuracy in the rigid-body case, since for breathing motions individual markers would be used to track a deformable object. Instead, one of the Certus’ active IRED markers was attached firmly to a metal probe, which was part of a Newport (Irvine, California, USA) magnetic clamp. This clamp was in turn magnetically attached to a Newport micrometer stage which could be driven independently in two orthogonal directions. When the stage is placed on a flat surface such as a table top, these two axes define a horizontal plane, as seen in figure 1(a). The stage is driven along each axis by means of micrometer screws, which give positional readings with a precision of ±2 μm. This stage is of a kind used for laser interferometry measurements; its accuracy and orthogonality are reliable to ±2 μm and ±6 × 10−3 degrees. To test the Certus’ positional accuracy and precision, the stage was driven to several positions around an arbitrary, virtual 1.0000 cm square, while the assembly sat, otherwise unsecured, on the table top. This scale was chosen as representative of breathing motions. We took measurements with the Certus of the marker atop the probe and magnetic clamp, and a 4-marker rigid body nearby to provide the reference frame, while simultaneously noting the stage position from the micrometer screws. At each position around the square, the single marker and nearby rigid body were both strobed at 10 Hz and measured for 2.0 s, giving


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20 independent positional measurements at each point. The Certus and micrometer data were transformed to a common coordinate system and differenced, and the data for each position were then averaged. Figures 1(a) and (b) show the experimental setup. 2.3. Timing measurements We tested the Optotrak Certus system’s timing fidelity using both the ethernet and SCSI communication options between the Certus control unit and a Linux-based computer. The PCI option was found not to be usable for our system, due to the lack of an appropriate driver for the dual-processor CPU in the computer. For both links used, we also tested the ‘blocking’ and ‘non-blocking’ real-time data retrieval modes (i.e., buffered data collection was not tested). In each case, we compared the internally-generated Certus timing information (which is really just a sequential frame number) to data from an NTP (network time protocol) server within the building. NTP computer systems are tied indirectly to a worldwide network of atomic time (UTC, or Coordinated Universal Time), and provide relative and absolute time signals within the building they are located, which can be as accurate as (sometimes even more accurate than) the light-travel time over such distances (∼100 m in our case, or 100 μm accuracy quoted by the manufacturer for this distance. 3.2. Timing measurements Typical timing results are shown in figure 3, which shows examples of the timing interval measurements between frames for a few cases. Summary plots of observed versus expected


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Figure 3. Typical performance of the Optotrak Certus shown as the time interval between frames versus the sequential NTP time call number (note that there are two NTP calls per frame in these plots). See the text for further details. (a) Ethernet link, non-blocking calls. (b) SCSI link, non-blocking calls.

performance in each of the four data collection modes (ethernet/blocking, ethernet/nonblocking, SCSI/blocking, SCSI/non-blocking) are given in figure 4, while residuals of all of these modes (in the sense observed minus commanded frame rates) are shown in figure 5. Unlike the static position results above, for both the ethernet and SCSI connections, and whether using blocking or non-blocking data retrieval, the Certus’ timing fidelity was quite poor compared to the factory specifications, and we identified a number of problems in each case. However it is important to note that, because the root cause of the problems outlined below was not identified, work to improve the Certus’ timing performance is ongoing. We first briefly discuss plots showing some typical timing results. In figure 3 we give the measured time interval between frames, shown as black crosses, for two cases: a commanded frame rate of 85 Hz with the ethernet link and non-blocking calls (figure 3(a)), and a commanded frame rate of 80 Hz with the SCSI link and non-blocking calls (figure 3(b)). These intervals are expected to be close to (commanded frame rate)−1 , and this expected value is indicated as a red line in the plots. The black crosses also represent the time taken to process the complete data retrieval loop in the code, i.e. the time taken to request the data, wait for its return, and process the results before looping back to the next request. For comparison, we also show in these plots (as green crosses) the timing intervals for data retrieval only in each loop, i.e. excluding the time taken for the code’s loop control and processing. The green crosses therefore represent the time each frame retrieval request spends waiting for the API calls to return the data (which we call the ‘waiting time’), while the difference between the black and green crosses represents the time taken for the main programme to issue the API data request (in non-blocking mode) and perform the loop control and processing until the wait for the data frame return begins (we call this the ‘loop time’). Note in both figures 3(a) and 3(b) that the first timing interval (i.e., between frames 1 and 2) is often widely discrepant from subsequent values. This is attributed to startup latencies in the hardware and/or code at the beginning of a data collection experiment, and is excluded from all analysis. We now turn to a discussion of the significant features of these plots. In general, we noted important differences in the Certus’ performance when using the ethernet link compared to that with the SCSI link. For example, from figure 3 we see the loop time is ∼0.1 ms for the ethernet link, but is ∼1.5 ms with the SCSI link (see below for a further discussion of

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Figure 4. Frame rates and timing statistics of data frames retrieved from the Certus. Each data point is an average of many measurements, with the error bars showing rms variations from the mean realized rates. Data collected with a different number of individual markers are colour coded to differentiate the points, but it is clear that the number of markers has little effect on the overall trends. (a) Results using the ethernet connection and blocking API calls between the Certus control unit and the host computer; the black dotted line shows the expected equality between the realized and commanded frame rates. Note the apparent limit of ∼100 Hz to the realized frame rate with this option, as well as systematic artefacts due to an apparent ‘frame lapping’ effect (see the text). Note also that the performance in this case is erratic, especially at the higher rates, as shown by the random jitter in the realized rates. (b) Similar to (a) but with non-blocking API calls. (c) Similar to (a) but using the SCSI connection and blocking calls. This is clearly more reliable than the ethernet option (evidenced by the virtual lack of timing jitter), and the maximum realized frame rate has increased as well, but is still not as fast as expected from equation (1). (d) Similar to (c) but with non-blocking calls.

this behaviour). Much less noteworthy were differences in performance when comparing data retrieval using blocking calls with equivalent non-blocking calls. Also, varying the number of individual markers being activated had no discernible effect on the overall trends in the Certus’ performance. We therefore focus on the communications link differences in the discussion following. 3.2.1. Ethernet performance. With the ethernet connection, we found the timing to have two significant problems (see figures 3(a), 4(a) and (b)). The first is that at any commanded frame rate, the mean time interval taken for the Certus to return successive frames of position


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data is only approximately the inverse of the commanded frame rate, with variations from this rate of several per cent. This variability seems to be quite erratic and unpredictable, since repeat experiments with the same setup did not give reproducible results. Further, this problem is worse as the commanded frame rate approaches 100 Hz. Even for a single data collection at one frame rate, the interval can be well behaved for part of the collection, and then ‘drop’ some frames for another part of the collection. Overall, the typical timing error is 100–200 μs, which should be compared to the figure quoted by the manufacturer for the accuracy of the Certus’ internal clock of ±100 ppm (or ±1 μs for a commanded frame rate of 100 Hz). Our much larger value is not likely to be due to latencies in the local ethernet connection, since these were measured as described in section 2.3, and ranged up to ∼50 μs, but were typically < 5 μs. Instead, we speculate that the timing jitter comes from either the ethernet implementation in the API or the ethernet hardware in the control unit, since this behaviour is not observed with the SCSI link (see below). Second, at high commanded frame rates with the ethernet option (above ∼85 Hz for both blocking and non-blocking calls), the data delivery through the API could not keep up with the time interval corresponding to the frame rate. There seems to be a basic incompatibility between attempts by the API to fulfil the data request at the required rate, and its ability to fulfil those requests. Thus while accepting instructions to retrieve data at a faster rate, the relevant routine in the API nevertheless seemed to have an intrinsic ‘speed limit’ of a mean ∼12 ms response time. At frame rates faster than the above thresholds, the API tries to retrieve data faster than this limit but does not always succeed, with the result that occasional frames are missed altogether, and only the next frame is retrieved instead. In this case, intervals of double (1/frame rate) are interspersed among the normal single frame intervals, and the missed frame is ‘dropped’ from the output data stream. The data call effectively ‘laps’ the data return in this case. For example, at a commanded frame rate of 100 Hz, the control program only retrieves about 8 out of every 10 frames. Thus while individual frames of marker positions that were retrieved from the Certus had reliable NTP timestamps attached to them, there were regular instances where whole frames of data were not extracted. This problem got worse until a commanded frame rate of ∼133 Hz, when 1 frame in 2 was being dropped. The 50% drop rate continued up to ∼160 Hz, and then even more frames were dropped, keeping the ‘realized’ frame rate always below 100 Hz. NDI informed us that tests of the PCI communications option revealed no frame dropping, again suggesting a limit intrinsic to the ethernet channel. However the SCSI connection also shows frame dropping behaviour at fast commanded frame rates (see below), so the problem may actually lie in the API. 3.2.2. SCSI performance. With the SCSI channel, the erratic response of the ethernet connection disappeared and the speed limit improved to ∼8 ms, but frame dropping behaviour at high commanded rates continued, and in addition another problem emerged (described below). Thus, the SCSI channel was faster than the ethernet, but still not up to the rate predicted by equation (1) (see figures 3(b), 4(c) and 4(d)). We found the maximum realized frame rate to be ∼120 Hz; at commanded rates higher than 110 Hz, frame dropping as with the ethernet link began, and increased until only 1 in 2 frames were being returned above 135 Hz (commanded). This 50% drop rate continued up to a commanded/realized frame rate of ∼230/115 Hz, at which point more frame dropping began. This behaviour is possibly related to the additional problem seen in the SCSI data, namely that the time intervals for the API to return frames of data to the control program seem to be quantized, in units of ∼1.3 ms. For example, for a commanded frame rate of 80 Hz (figure 3(b)), the measured time intervals between successive frames of data clustered around values of either 11.5 ms or 12.8 ms, not 12.5 ms as would be expected. However the

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Figure 5. Percentage differences between the commanded and mean realized frame rates, as a function of the commanded frame rate. The error bars indicate the rms variation in timing regularity for each point. The colour code indicates our four groups of timing experiments.

relative proportion of these intervals was such that the mean interval was indeed very close to 12.5 ms. Therefore the SCSI channel produces frames at the correct rate, but only in the mean. No matter what the commanded frame rate (i.e. even at relatively slow frame rates), only time intervals at the quantized amounts were found, and it was their relative proportions which always made the mean realized rate close to the commanded one. The fastest realized rate, ∼130 Hz, is then a consequence of the fact that 7.6 ms is apparently the fastest return speed available to the API for the SCSI connection. Above 130 Hz, no faster return speed is available to average with the 7.6 ms interval to give the commanded rate, and instead 1 in 2 frames are dropped. Note also in figure 3(b) that the loop time has increased to ∼1.5 ms, from the ∼0.1 ms value exhibited in figure 3(a). This again is not intrinsic to the control programme, but is a feature of the non-blocking calls when using the SCSI link. In the non-blocking case, the data request to the API is issued separately from the data retrieval call. The larger loop time in figure 3(b) is entirely due to the data request call for the SCSI link + non-blocking calls case. (Recall that for blocking calls, there is no separate data request call.) The same API call in the ethernet case is much faster, as shown in figure 3(a). We speculate that this is likely due to the same origin as the larger timing interval quantization with the SCSI link, although the true cause is not known. Upon review, it was noted that the ethernet link possibly also shows a similar time interval quantization, although at a much finer level. Thus in figure 3(a), one can see that most of the measured frame intervals cluster around values of 11.60, 11.73, 11.86 or 12.00 ms, but average to the expected 11.77 ms. In other words, the timing interval appears to be quantized in units of ∼0.13 ms, or about ten times smaller than the SCSI timing quantization. Again, the origin of this behaviour is not known. It is clear from the above that, in our hands, the Certus’ timing performance was only reliable below a practical limit of ∼85 Hz for the commanded frame rate. In figure 5 we summarize the overall performance of the Certus for all tested connections. Here we show as


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a percentage the residuals between the commanded and mean realized frame rates, as well as the rms variations from these means, up to this practical limit. The trends discussed above are now made clear. Because of the fixed timing quantization for both the SCSI and ethernet connection, the rms uncertainties increase linearly with commanded frame rate. This trend for the ethernet grows more slowly than for the SCSI because the quantization in the former case is much finer. However the ethernet performance is much more erratic than with the SCSI, as shown by the frequent large excursion in the red and green error bars above the slowly increasing trend. The blue error bars grow faster, but less erratically, because of the relative lack of timing jitter with the SCSI. From this graph it is easy to select, for a given application, a limiting frame rate for a desired maximum rms fluctuation in the timing. 4. Discussion and conclusions Recent studies of breathing motion have focused on a number of aspects of the problem for medicine. For example, models of lung movement and radiation gating during IMRT (Dietrich et al 2005), attempts to use internal markers to track organ movement (Berbeco et al 2005b), and online correction of tumour motion with fluoroscopy (Berbeco et al 2005c, Nill et al 2005) all deal with correcting for organ motion during breathing. Each attempt to accommodate this problem has its tradeoffs. To avoid the problems of internal markers and the tracking thereof, attempts have been made to use external markers as proxies for the breathing cycle. Until recently, past studies typically used only one external marker. Attempts to remove, via any software or analysis technique, the actual breathing movements from the imaging data have been limited (Vedam et al 2003). More recent work, however, has started along the approach of tracking multiple external markers to more thoroughly measure respiration and other motions (Jin et al 2007). The ability of the Optotrak Certus to track multiple markers with high precision and at high rates offers a powerful tool for detailed characterization of external breathing motions. We find that in a static case, at a distance from the position sensor of ∼3 m, the Certus can obtain measurement accuracy and precision on the order of 20 μm. This is well within the manufacturer’s specifications at this distance. For clinical or research situations where timing is not an issue, and tracking accuracy is paramount, the Certus can produce very satisfactory results. This is the case for static studies, or even when motion is involved but only the motion’s positional information is required (Barnes et al 2006), i.e. where the timing of the motion is not important. In contrast, we find that the timing performance of the Certus, whether used through an ethernet or SCSI communication channel to its host computer, is not very reliable below roughly a 5% accuracy level. This will be a problem, for example, in situations where high-precision velocities are required, or where otherwise timing behaviour is critical to the object under study. However, we have developed a method to obtain accurate absolute and relative timestamps for each frame of positional data returned by the Certus, which we are making available to the community upon request. This is useful in situations where, as long as the timing information is accurate, the data frames do not need to be regularly spaced in time (e.g., if the data will be rebinned into longer intervals than those between data frames). One example of such a situation is in time-resolved CT or PET scans, where there are also individual frames of data, or the data are binned in list mode with their own timestamps (e.g. Dawood et al 2007). As long as each Certus time interval can be accurately tagged by bracketing timestamps, and the CT or PET frames (which are typically more widely spaced in time) can be similarly tagged, then the Certus data can be unambiguously assigned to the correct imaging frame, despite the slight irregularity of the positional data in time. At frame

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rates appropriate for measuring breathing movements of resting patients (< 100 Hz), a timing measurement accuracy of better than 50 μs will be more than adequate. With the Optotrak Certus, we will vastly increase the tracking accuracy of external motions during breathing, compared to previous work. The Optotrak Certus system will provide us with the detailed data needed as input to the latest models of internal motion, with the ultimate goal of simply and noninvasively removing the effects of motion on medical imaging, diagnosis and treatment. Acknowledgments This work was supported by the Cancer Council New South Wales. References Barnes P, Fulton R, Meikle S and Baldock C 2006 Benchmarking of an Optotrak Certus system for motion sensing during medical imaging Australas. Phys. Eng. Sci. Med. 29 397–8 Berbeco R I, Mostafavi H, Sharp G C and Jiang S B 2005 Towards fluoroscopic respiratory gating for lung tumours without radiopaque markers Phys. Med. Biol. 50 4481–90 Berbeco R I, Neicu T, Rietzel E, Chen G T Y and Jiang S B 2005 A technique for respiratory-gated radiotherapy treatment verification with an EPID in cine mode Phys. Med. Biol. 50 3669–79 Berbeco R I, Nishioka S, Shirato H and Jiang S B 2006 Residual motion of lung tumors in end-of-inhale respiratory gated radiotherapy based on external surrogates Med. Phys. 33 4149–56 Bloomfield P M, Spinks T J, Reed J, Schnorr L, Westrip A M, Liveratos L, Fulton R and Jones T 2003 The design and implementation of a motion correction scheme for neurological PET Phys. Med. Biol. 48 959–78 Dawood M, Büther F, Lang N, Schober O and Schäfers K P 2007 Respiratory gating in positron emission tomography: a quantitative comparison of different gating schemes Med. Phys. 34 3067–76 Dietrich l, Tücking T, Nill S and Oelfke U 2005 Compensation for respiratory motion by gated radiotherapy: an experimental study Phys. Med. Biol. 50 2405–14 Fulton R R, Meikle S R, Eberl S, Pfeiffer J, Constable C and Fulham M J 2002 Correction for head movements in positron emission tomography using an optical motion tracking system IEEE Trans. Nucl. Sci. 49 116–23 Gierga D P, Brewer J, Sharp G C, Betke M, Willett C G and Chen G T Y 2005 The correlation between internal and external markers for abdominal tumors: implications for respiratory gating Int. J. Radiat. Oncol. Biol. Phys. 61 1551–8 Hummel J B, Bax M R, Figl M L, Kang Y, Maurer C, Birkfellner W W, Bergmann H and Shahidi R 2005 Design and application of an assessment protocol for electromagnetic tracking systems Med. Phys. 32 2371–9 Jin J-Y, Ajlouni M, Ryu S, Chen Q, Li S and Movsas B 2007 A technique of quantitatively monitoring both respiratory and nonrespiratory motion in patients using external body markers Med. Phys. 34 2875–81 Kini V R, Vedam S S, Keall P J and Mohan A R 2001 A dynamic non-invasive technique for predicting organ motion in respiratory-gated radiotherapy of the chest Int. J. Radiat. Oncol. Biol. Phys. 51 25 Nill S, Unkelbach J, Dietrich L and Oelfke U 2005 Online correction for respiratory motion: evaluation of two different imaging geometries Phys. Med. Biol. 50 4087–96 Renault G, Tranquart F, Perlbarg V, Bleuzen A, Herment A and Frouin F 2005 A posteriori respiratory gating in contrast ultrasound for assessment of hepatic perfusion Phys. Med. Biol. 50 4465–80 Schicho K, Figl M, Donat M, Birkfellner W, Seemann R, Wagner A, Bergmann H and Ewers R 2005 Stability of miniature electromagnetic tracking systems Phys. Med. Biol. 50 2089–98 Schweikard A, Shiomi H and Adler J 2004 Respiration tracking in radiosurgery Med. Phys. 31 2738–41 Seppenwoolde Y, Shirato H, Kitamura K, Shimizu S, van Herk M, Lebesque J V and Miyasaka K 2002 Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy Int. J. Radiat. Oncol. Biol. Phys. 53 822–34 Tewatia D, Zhang T, Tome W, Paliwal B and Metha M 2006 Clinical implementation of target tracking by breathing synchronized delivery Med. Phys. 33 4330–6 Vedam S S, Keall P J, Docef A, Todor D A, Kini V R and Mohan R 2004 Predicting respiratory motion for four-dimensional radiotherapy Med. Phys. 31 2274–83 Vedam S S, Kini V R, Keall P J, Ramakrishnan V, Mostafavi H and Mohan R 2003 Quantifying the predictability of diaphragm motion during respiration with a noninvasive external marker Med. Phys. 30 505–13


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Webb S 2006 Motion effects in intensity modulated radiation therapy: a review Phys. Med. Biol. 51 R403–25 Willowson K, Baldock C and Fulton R 2006 Implementation and optimisation of optical motion tracking for motion correction in PET/CT Australas. Phys. Eng. Sci. Med. 29 158–9 Wolthaus J W H, van Herk M, Muller S H, Belderbos J S A, Lebesque J V, de Bois J A, Rossi M M G and Damen E M F 2005 Fusion of respiration-correlated PET and CT scans: correlated lung tumour motion in anatomical and functional scans Phys. Med. Biol. 50 1569–83 Yan H, Yin F-F, Zhu G-P, Ajlouni M and Kim J H 2006 The correlation evaluation of a tumor tracking system using multiple external markers Med. Phys. 33 4073–84