An integrated tomotherapy system allows for improved radiotherapy verification by ... it is possible to use data collected during tomotherapeutic treatments for ...
Phys. Med. Biol. 45 (2000) 3545–3562. Printed in the UK
PII: S0031-9155(00)14099-0
Megavoltage CT image reconstruction during tomotherapy treatments K J Ruchala†, G H Olivera†‡, J M Kapatoes†, E A Schloesser†, P J Reckwerdt† and T R Mackie†‡§ † TomoTherapy Inc., 2228 Evergreen Road, Middleton, WI 53562, USA ‡ Department of Medical Physics, University of Wisconsin, Madison, WI 53706, USA § Department of Human Oncology, University of Wisconsin, Madison, WI 53706, USA Received 19 May 2000, in final form 14 August 2000 Abstract. An integrated tomotherapy system allows for improved radiotherapy verification by enabling the collection of megavoltage computed tomography (MVCT) images before or after treatment delivery. In this investigation, the possibility of collecting MV tomographic data and reconstructing images during a tomotherapy treatment is examined. By overcoming difficulties with the normalization of modulated treatment data and with the incompleteness of treatment data, it is possible to use data collected during tomotherapeutic treatments for MVCT reconstruction. The benefits of these techniques include potential increases in patient throughput, reductions in imaging dose, visualization of the patient in the treatment position and improvements in image contrast.
1. Introduction The development of tomotherapy has the potential to improve the process of radiotherapy. Specifically, treatments will be delivered in helical slices and temporally modulated with a multileaf collimator (MLC). This will allow for a highly conformal treatment that will deliver dose to the tumour while sparing sensitive structures (Mackie et al 1993, 1997, 1999, Mackie 1997, Olivera et al 1999). However, as treatments become more conformal, and the margins around the tumour are reduced, it becomes that much more important to be able to verify the success of the treatment. Thus, new methodologies are being developed to ensure that the ability to evaluate delivered treatments is congruous with the ability to deliver them. One means of verification is CT image reconstruction. This is important, because if movements or changes in the patient’s anatomy are not detected, the treatment could be compromised (Boellaard et al 1998, Jaffray et al 1999b, Yu et al 1998). Ideally, a tomotherapy machine would contain both on-board kilovoltage and megavoltage CT (MVCT) scanning capabilities. However, in phantom studies, MVCT has demonstrated the ability to detect internal objects with sizes and contrasts akin to many internal organs (Ruchala et al 1999, 2000a). Moreover, the reconstructed values in the MVCT images better represent the actual attenuation coefficients for the treatment photons, making those images useful for dose calculations (Ruchala et al 2000b). In addition to properly positioning the patient’s body and interior organs, it is also vital to know that the treatment was delivered as intended. This is checked using feedback signals from the MLC, and is corroborated through the process of delivery verification (Kapatoes et al 1999). Specifically, transmission data collected during the treatment is combined with tomographic 0031-9155/00/123545+18$30.00
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images of the patient to provide an independent confirmation that the leaves of the MLC are modulated as desired. Finally, through the process of dose reconstruction, the delivered treatment fluences can be projected through a CT image of the patient, determining the dose delivered to each region of the patient (Kapatoes et al 1999, McNutt et al 1996a, b, Olivera et al 1999). However, dose reconstruction still ideally uses a tomographic description of the patient at the time of the treatment (Kapatoes et al 2000). Performing the dose reconstruction based upon an old image set, such as the planning CT, introduces the possibility of incorrect or misleading dose calculations to the extent that the planning CT does not best represent the patient’s position, shape and anatomy at the time of the treatment. Finally, the delivered dose can be compared with the desired dose, and if any discrepancies are detected they can be remedied in subsequent fractions through the process of adaptive radiotherapy. It is with this process that images collected, or improved, after a fraction can benefit the remaining fractions through knowledge of the delivered dose. One way to generate the images of the patient necessary for dose reconstruction is to perform pretreatment CT, as can be done with the University of Wisconsin (UW) tomotherapy machine, as well as with other MVCT systems (Aoki et al 1990, Brahme et al 1987, Groh et al 2000, Guan and Zhu 1998, Hesse et al 1998, Jaffray et al 1999a, Lewis et al 1992, Midgley et al 1998, Morton et al 1991, Mosleh-Shirazi et al 1998, Nakagawa et al 1994, Partridge et al 1998, Simpson et al 1982, Spies 2000, Spies et al 2000, Swindell 1983, Swindell et al 1983, 1991). However, if it is believed that a patient may have moved during treatment, posttreatment MVCT images would optimally be collected immediately following delivery to determine the patient’s final position, and consequently estimate the dose that was delivered. So in the context of multifraction adaptive radiotherapy, there is benefit in gaining new or improved tomographic data, even if it occurs during or after the treatment. Likewise, being able to gather such data during the treatment may relax the necessary dose and scan time for pretreatment scanning. For this reason, the topic of collecting or improving images during the actual treatment is being investigated. One benefit is that this method could potentially save time. Instead of doing all scanning and reconstruction prior to delivering the treatment, tomographic data could be collected and viewed during the treatment. Additionally, while a pretreatment scan should be low dose, the treatment, by its nature, is high dose. This translates to many more photons being collected, such that noise is reduced and low-contrast detectability might be enhanced. Conversely, it may be possible to use treatment data to maintain image quality while reducing imaging dose, and this may allow MVCT scans to be taken more often. Finally, with treatment tomography, the images reflect the position of the patient at the time of delivery, making them potentially the most useful for delivery verification and dose reconstruction. For treatments that may incur patient motion, these techniques may provide an accurate representation of the patient’s anatomy, and help determine the dose delivered so that any discrepancies can be remedied. In this investigation, the goal is to enhance MVCT capabilities by collecting and augmenting images during tomotherapeutic treatments, although the results are also applicable to other types of intensity-modulated radiation therapy (IMRT) that utilize tomographic image reconstruction. 2. Materials and methods 2.1. Inherent difficulties with reconstruction of treatment data In order to achieve these benefits, there are two fundamental impediments to MVCT treatment reconstruction. One problem is that in order to reconstruct CT data, the measured transmission
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signals need to be normalized by the incident fluences, and then logarithmically transformed. For pretreatment MVCT or kVCT scans, the normalization is accomplished by collecting an airscan vector, 0 (x), which is a measurement of the signal in each detector with the beam on, but no attenuating material in place. The transmitted fluences are converted to ray-integrals through the well-known formula: i (x) λ(x) = − ln = µ(r) dr (1) 0 (x)
where i (x) = 0 (x) e− µ(r) dr is the measured transmission data in each detector. However, during a temporally modulated treatment, such as tomotherapy, the leaves of the MLC are rapidly opening and closing. This means that the effective incident fluences upon the patient may change from pulse-to-pulse of the linac, depending upon the motion of the MLC leaves. One possibility would be to calculate the incident fluences based on the leaf positions at the time of each pulse, as provided by delivery verification. However, this computation is complicated by factors such as scatter, penumbra and tongue-and-groove-like effects (Kapatoes et al 1999). The other possibility would be to experimentally measure the incident fluences by running the modulated plan once in air and once with the patient. However, for the NOMOS MIMiC MLC used in this study, the leaves do not move in an exactly reproducible fashion, but instead have complex dependences on the number of open leaves, the number of moving leaves and the time elapsed since a leaf moved last. Thus, this type of normalization was also problematic. The other fundamental problem with MVCT reconstruction is that the data collected are inherently incomplete. This is a necessary consequence of the goal of tomotherapy, which is to selectively irradiate the tumour while avoiding sensitive structures. The result is that instead of collecting ray-integrals at every detector position for every angle, one ideally only collects data corresponding to rays that pass through the tumour and sufficiently avoid critical organs. This means that while the attenuation data collected during a treatment have a very high dose, and therefore excellent noise characteristics, the extent of these data is extremely limited, resulting in an incomplete sinogram. Such sinogram data sets are essentially unreconstructible without further modification. In conventional CT, randomly occurring bad or missing data can sometimes be replaced by exploiting redundancies in the data collection process (Margosian 1982). However, this is generally not possible for tomotherapy treatments since the sinogram data set is systemically incomplete due to large regions of the patient being selectively avoided. 2.2. University of Wisconsin tomotherapy benchtop In order to understand the solution to the leaf-modulation problem it may be helpful to begin with a description of the University of Wisconsin (UW) tomotherapy benchtop (Balog 1998, Kapatoes et al 1999, Mackie et al 1993, 1999, Olivera et al 1999). The set-up features a stationary General Electric Orion accelerator (Buc, France) with a nominal energy of 4 MV and an average energy of 1.36 MeV. The linac is directed at a 738-channel General Electric Medical Systems xenon detector (Milwaukee, WI) at 5 atm pressure. A NOMOS MIMiC MLC is affixed to the linac to conformally modulate the beam. This MLC has a field of view (FOV) of about 18.0 cm measured at the axis-of-rotation distance of 93 cm. Similarly, this FOV constraint only allows useful signals to be collected in approximately 200 channels of the detector. The radius of curvature of the detector is 110 cm, although spatial constraints prevented the detector from being focused at the source. Instead, the front face of the detector is situated 127 cm from the linac source, as measured along the central axis. In practice, however, this detector position improved the effective efficiency of the xenon detector at megavoltage
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energies from approximately 1% to 7% (Keller et al 2000). The phantoms were placed on a computer-controlled phantom rotating and positioning device (Paker-Hannifin Company, Rohnert Park, CA), referred to as the phantom actuator. Thus, this system is analogous to a conventional CT scanner in which the patient remains still, but the linac and detector rotate about the patient. Most of the treatment deliveries discussed here were to a 17.6 cm diameter cylindrical phantom, which narrowly fitted within the MLC FOV with all of the leaves open. Axial rotations were used with a period of rotation of 69 s and the slice thickness was 0.76 mm. The data acquisition system (DAS) synchronized the linac output at 200 Hz with the detector read-out, and this linac output corresponds to 200 MU min−1 . An additional treatment to the head of a German shepherd dog cadaver was performed. This treatment was performed helically, using a 25.5 s period of rotation, a slice thickness of 0.76 cm, and a pitch of 0.5. For all treatments, the collected data were preprocessed using the procedure discussed in Ruchala et al (1999) and included the steps of dark-current subtraction, reference channel normalization, logarithmic conversion and beam-hardening correction. For the gantry-based UW tomotherapy prototype machine, treatments can be expedited by using a faster gantry period in conjunction with a higher linac output rate. Similarly, the linac output can be sufficiently reduced to allow for MVCT scanning with patient doses of the order of several cGy. On the first tomotherapy machine, the fastest rotation time will be on the order of 10 s, and the net scanning time will depend on how many rotations are needed to scan a desired region, given other scan parameters such as pitch and slice thickness. 2.3. Normalizing transmission data for modulated treatments 2.3.1. Normalization using a non-modulated plan. As discussed earlier, one complication with attempting to reconstruct data from modulated treatments is that the transmission data are difficult to normalize. One possibility would be to collect the incident fluences experimentally, by running the plan in air, but this approach is limited by the fact that the MLC leaves do not move in a perfectly reproducible manner. However, this problem can temporarily be sidestepped by using an unmodulated plan, which might provide an insight into a more general solution. The idea is that instead of modulating the leaves to deliver a dose that conforms to the tumour, a treatment plan is selected such that several leaves remain open for the entire treatment and the remaining leaves remain closed. Thus, the term unmodulated refers to the fact that the leaves are not fluctuating during the treatment, even though the leaves may maintain any single pattern for the entire delivery. In this case, the central 12 MLC leaves were opened for the duration of the treatment, while the other eight leaves were permanently closed. Such a delivery, to the cylindrical phantom shown later, produces a cylindrically symmetric dose distribution. The relevance to normalization for one view can be seen in figure 1. A conventional normalization is shown in figure 1(a). For measurements taken with all of the leaves are open, the airscan (bold curve) can be used to normalize the phantom scan (dashed curve), in accordance with equation (1). The same principle can be applied to a leaf pattern provided the view is ‘stable’, in that none of the leaves are moving during the linac pulse. This is shown in figure 1(b). In particular, the full curve represents the stable-position airscan, or spairscan, collected for this arbitrary pattern of non-moving leaves. This is the equivalent of an airscan, in that no attenuating body is in place, yet the name has been altered to represent the fact that some leaves may be open and others may be closed, though none of the leaves are moving. By adding a phantom,
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Figure 1. (a) For conventional CT, the phantom transmission measurements (dashed curve) can be normalized to the airscan measurements (full curve) in accordance with equation (1). (b) Normalization for leaves in an arbitrary, but stable, position. (c) The two full curves represent different measurements of a given spairscan during two separate runs of a modulated plan. This makes the normalization of the transmission data (dashed curve) for that view unreliable.
the spairscan signal is attenuated such that the dashed curve in figure 1(b) is collected. The spairscan data in the channels corresponding to the open leaves can then be used to normalize the respective channels of data collected with the phantom in place. Hence, for the unmodulated plan described above, one can normalize all of the transmission data by simply collecting a single spairscan for the desired leaf pattern of the central 12 leaves open and the other leaves closed. The complication is that moving leaves add tremendous uncertainty to the process. To demonstrate this, a modulated treatment was run twice in air and once in a phantom. The results for one view are shown in figure 1(c). Again, the dashed curve represents the measurement collected with a phantom in place. However, the two (thin and bold) full curves represent attempted spairscan measurements for the same view. Note that the instability of the leaf motion causes these two measurements to differ significantly. As such, there is not a single consistent spairscan because the leaves are not stable in this view and the emitted fluences vary each time the plan is delivered. Consequently, such unstable views cannot be reliably normalized. Even so, it should be noted that these difficulties with normalization are not inherent to having different leaf patterns, but instead result from the finite, yet irreproducible, motion times of the leaves during a modulated delivery. 2.3.2. Normalization of modulated data. The unmodulated treatment is not a realistic or clinically relevant case, but instead a simplified case in which all of the data can be normalized with a single spairscan. On the contrary, the goal of having an MLC is to be able to temporally modulate treatments. As such, a method was identified by which some, albeit not all, of the modulated treatment data could be successfully normalized. It begins by delivering the modulated treatment in air, and then to the patient. Since the leaves are, in general, moving, it is not feasible to simply divide one set of measurements by the other, for the reasons demonstrated in figure 1(c). However, it is not the case that leaves are moving during each and every view, or pulse of the linac. Instead, there are many linac pulses during which all of the leaves are stable,
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K J Ruchala et al Table 1. The materials used in the phantom, and their arrangement in the phantom, in accordance with the labels in figure 5(a). The electron density contrasts are provided as well as the Hounsfield numbers one would expect for those materials.
Material
Phantom position
Water 99.9% deuterium oxide Sucrose + water Sucrose + water Sucrose + water Sucrose + water Ethyl + water Ethyl + water Ethyl + water MgSO4 + water MgSO4 + water CaCl2 + water Spectral artefact
a (base) b c d e f g h i j k l m
% Contrast, electron density 0 −0.44 1.03 2.59 4.78 17.9 −1.40 −3.27 −9.99 3.95 12.0 3.10
Ideal Hounsfield Number 0 −4 10 26 48 180 −14 −33 −100 40 120 31
remaining in the same positions as for the previous pulse. Thus, one can safely normalize some of the treatment data by: (a) determining which views of the treatment delivery in air are stable; (b) determining which views of the treatment delivery to the patient are stable; (c) finding the intersection of those two sets of views, and verifying that the leaves have the same pattern in each case; (d) for the views determined in (c), the treatment data can be normalized with the corresponding spairscan views from the air delivery. One caveat of this method is that one is not able to normalize all of the treatment data, but instead only the stable fraction for which spairscans are available. The extent to which this limits reconstruction will be discussed in a later section. The means for identifying the stable views requires only small modifications from the delivery verification techniques developed by Kapatoes and co-workers (Kapatoes et al 1999, Olivera et al 1999). The idea is that transfer functions can be measured or calculated which relate the signal measured in each detector to the energy fluence emitted from each leaf, for each delivery angle. By combining these transfer functions with the measured transmission signals, one can determine the linac pulses during which all of the leaves were stable and in the desired pattern. Ideally, it would be possible to normalize all of the treatment data. In order to achieve this, a calculation of the incident fluences might be necessary, which could require greater precision for the delivery verification values as well as precise estimates of scatter, tongue-and-groovelike effects, output factors and other practical complications. Another option might be to place a segmented chamber between the MLC and the patient, allowing for a direct measurement of the energy fluences that are incident upon the patient. 2.4. Treatment plans The example of a non-modulated scan was presented to provide an intuitive basis for spairscan normalization. Since all of the data in the non-modulated scan can be normalized, this
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Figure 2. (a) The mask used to indicate the treatment region (white) and a sensitive structure (black) in the phantom. The tomotherapy optimization indicates that the dose shown in (b) can be delivered by modulating the leaves in accordance with the energy fluence sinogram shown in (c).
delivery pattern demonstrates a particularly favourable case for the contrast improvements. Nonetheless, by addressing the normalization problem, it is possible to deliver arbitrary treatment patterns and use the treatment data for image reconstruction. In this case, the phantom being treated is a 17.6 cm diameter water-filled acrylic cylinder containing 12 vials of low-contrast solutions (table 1). The regions of interest (ROI) for the treatment optimization are shown in figure 2(a). This is an overlay to the phantom image that denotes tumours in white and the sensitive structure in black. The tomotherapy optimization software uses this input and the phantom image to calculate a planned energy fluence sinogram. Specifically, the dose distribution shown in figure 2(b) can be delivered by modulating the leaves in accordance with the planned energy fluence sinogram shown in figure 2(c). The grey-scale intensities indicate how much energy fluence should be passed by each leaf during each discretized angular range, or projection. In this case, the treatment region is centrally located, so many of the peripheral leaves never open. The inner leaves modulate so as to treat the tumour while protecting the sensitive structure. This treatment was delivered on the benchtop system described above. The phantom actuator had a period of rotation of 69 s, such that 1 s was spent delivering radiation to each projection. For a delivery in which all of the leaves remained open, these scan parameters would produce a dose of 175 cGy to the centre of the phantom. However, leaf modulations reduce the delivered dose, such that in this case the ‘tumour’ receives 120 cGy (Balog 1998). Note that the contrast cylinders are not detectable in the dose image because the optimized
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Figure 3. (a) The binary leaf-position sinogram that is converted from the energy fluence sinogram shown in figure 2(c). This binary sinogram indicates the gross availability of high-dose treatment data. (b) The binary sinogram showing the available high-dose treatment data that are also stable.
rotational treatment compensates for the perturbations to the fluence and scatter caused by the contrast materials that might otherwise distinguish those objects. Since the MLC is a binary system, the planned energy fluences cannot be delivered directly, but need to be converted to a series of open and closed leaf states. At present this is accomplished in the tomotherapy control software in essentially the same way as has been done in the NOMOS MIMiC control software. Specifically, all of the leaves are closed at the start of each projection arc, and from the beginning of the arc to halfway through the different leaves open. The leaves needing to pass large energy fluences will open earlier in the arc, and others will open later or not at all. During the second half of the arc, all of the leaves will progressively close, symmetric with their opening times. Ultimately, all of the leaves will be closed at the beginning and end of each projection, and all of the leaves that will pass any energy fluence in a given projection will be open at the centre of that arc. The optimized leaf-fluence sinogram has been converted to a binary leaf-state sinogram (figure 3(a)). This tells whether each leaf should be open or closed for each pulse of the linac, delivered at 200 Hz for 69 s. Thus, this figure represents the total availability of transmission data to be collected during the treatment. In practice, the leaves do not instantaneously open or close in between the 5 ms between leaf states; typically, opening or closing motion for the MIMiC requires between 30 and 70 ms. By using the aforementioned delivery verification techniques, one can identify the stable views or the linac pulses during which no leaves are moving. The resulting sinogram (figure 3(b)), identifies the availability of normalizable transmission data from the treatment. For this plan, 44.5% of the 13 800 views are normalizable, although in general, this fraction depends on the plan, the MLC, the rotation speed and the linac output rate. These normalizable data are the information that may presently be useful for reconstruction. 2.5. Reconstruction of incomplete data sets For this planned treatment, it can be readily seen that the normalizable transmission data shown in figure 3(b) are not a complete data set, as large quantities of data are systemically avoided
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Figure 4. (a) A low-dose pretreatment sinogram is used to add a small number of complete views to the high-dose treatment data. The added views seen in this enlarged section of the 13 800 state sinogram are in states 2000, 2200, 2400 and 2600. Another option for adding data is to collect a number of ‘flashes’, as shown in (b). In this case 2 of the 23 flashes can be seen as the blocks of data around states 2120 and 2720.
and this severely impairs reconstruction. Attempts were made to reconstruct these data using iterative techniques and methods for estimating the missing data (Ruchala 1999), but these methods were unsuccessful because of the number of data missing. However, with tomotherapy treatment data, as with region-of-interest tomography (ROIT) (Nalcioglu et al 1979a, b), the data sets are intentionally incomplete to reduce dose. Thus, regularization can derive from a modest amount of additional data collection. The salient question is how many additional data are needed for useful reconstruction of treatment data, how to collect these data, and to what extent does this additional data collection increase the time and dose of the treatment. Two different methodologies have been identified though which enough data can be collected for reconstruction. These techniques are called pretreatment augmentation and treatment modification. A third possibility is to use the leakage and transmission through the MLC, although the details of this topic have been addressed separately (Ruchala et al 2000a). Pretreatment augmentation begins by collecting a complete, but low-dose, sinogram encompassing the patient’s entire cross section. Such a scan, characteristic of pretreatment scans, is performed with all of the leaves open. This allows for data collection in each detector at each angle. In practice, the angular sampling was relaxed to reduce dose, and 69 views were used delivering 0.9 cGy to the centre of the phantom. These complete, but low-dose, pretreatment data can be used in conjunction with the high-dose but limited-view treatment data, a region of which is shown in figure 4(a). The blocks of interior data are from the normalizable treatment data shown in figure 3(b). The four thin horizontal lines are superimposed to show the availability of low-dose pretreatment views that can help to interpolate the missing data.
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Figure 5. (a), (b), (c) The cylindrical phantom used is shown for conventional MVCT at doses of 175 cGy, 7.0 cGy and 0.9 cGy respectively. By augmenting the 0.9 cGy data set with the high-dose data from the unmodulated treatment, the reconstruction shown in (d) is generated. The contrast in the treatment region is much improved over the unaugmented 0.9 cGy reconstruction, although the improvements elsewhere are more modest. By augmenting the 0.9 cGy sinogram with the normalizable treatment data from the modulated plan that delivered 120 cGy to the tumour, the image shown in (e) can be reconstructed. The contrast in the treatment region is improved relative to the original 0.9 cGy reconstruction, although not quite as good as in the 7.0 cGy image.
It is also possible to collect all of the data necessary for reconstruction entirely during the treatment through treatment modification. Specifically, it is feasible that the MLC modulation pattern of the delivery can be slightly adjusted such that a ‘more reconstructible’ data set is collected during the treatment delivery. The most promising version of this is to use a number of flashes, or instances in which all of the leaves briefly flicker open. The natural time to do this is at the centre of each optimization angle, as most of the leaves are already open at those times. For this plan 23 such flashes were used, meaning that a flash view was taken in every third projection. Two of these flashes can be seen in figure 4(b), as the two thicker bands of data available across the entire FOV. Their thickness is due to the finite leaf opening and closing times. The additional dose delivered during the flashes ultimately limits the number that can be used, although this will be discussed later. 3. Results 3.1. Phantom results Reconstructions of the water-filled acrylic phantom with low-contrast inserts are shown imaged using 175 cGy, 7 cGy and 0.9 cGy MVCT in figures 5(a), 5(b) and 5(c). The contrast solutions are labelled in figure 5(a) and are described in table 1. These images were acquired as conven-
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Figure 6. The treatment was modified such that full FOV data were collected during each of 23 flashes in the treatment. (a) An image is reconstructed entirely from the data in those 23 flashes. The flash data can also be augmented with the treatment data, allowing for improved contrast, especially in the treatment region, as seen in (b).
tional, or pretreatment, scans, meaning that all of the leaves were open for the duration of the scan. As expected, lower-dose images are much noisier, and this noise impairs the visibility of low-contrast objects. However, even in the 0.9 cGy image, contrasts below 3% are visible, and contrasts of 1% are visible with 7 cGy. Figure 5(d) shows the result of augmenting the 0.9 cGy sinogram with the treatment data collected during the delivery of the unmodulated plan. The contrast in this image is greatly improved over the non-augmented 0.9 cGy image, especially in the treatment region. The results in this case benefit from being able to normalize all 175 cGy of the treatment data and from having a moderately large treatment region. The result of augmenting the 0.9 cGy image with the normalizable treatment data from the modulated plan (figure 2(c)) is shown in figure 5(e). Again, the contrast is improved in the treatment region relative to the 0.9 cGy image without treatment data augmentation. The improvements in this case are not as drastic as the unmodulated case, though, because the tumour dose was lower (120 cGy) and only a fraction of those data was normalizable. For both of these treatmentaugmented reconstructions, the additional dose was relatively evenly distributed, contributing 0.9 cGy to the centre of the phantom and less than 1.0 cGy everywhere in the phantom. Results using the treatment modification, or ‘flashes’ technique, are shown in figure 6. Figure 6(a) depicts an image reconstructed only from the 23 flashes collected during the treatment. This small number of views renders it particularly susceptible to streak artefacts, and also degrades the smoothness of the round borders. Figure 6(b) is a reconstruction in which the 23 views of flash data were augmented with the normalizable treatment data. As with the pretreatment augmentation reconstructions, the addition of treatment data also improves the contrast here, most noticeably in the region of interest. The additional dose that results from these flashes is more complex than for the pretreatment method. This is because the flashes occur when many leaves are already open for treatment delivery; yet a full FOV is achieved by opening all of the leaves that remain closed. Thus, flashing does not add dose evenly, but instead adds more dose to the regions that were being shielded by the leaves, while contributing less additional dose to the regions for which most leaves were already open. A dose volume histogram (DVH) illustrating this point can be seen in figure 7(a). The full curves represent the tumour dose, the dashed curves represent the normal tissue dose and the dotted curves represent the sensitive structure dose. The curves of each type marked with crosses are for the unmodified treatment while the circles denote the
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Figure 7. (a) The DVH curves for the original treatment, the modified ‘flashes’ treatment, and a ‘flashes’ treatment using anticipated dose reduction techniques. (b) Curves showing how the additional dose from the ‘flashes’ is distributed—the treatment modification technique tends to deliver more additional dose to the regions of the patient originally receiving less dose, although the magnitude of this curve can be reduced.
curves for the modified treatment. Clearly, the tumour’s DVH curve is only slightly shifted by the flashes, whereas the curves for the healthy tissue and sensitive structure are shifted more drastically, and this could undermine their conformal avoidance. However, there are methods of reducing dose from the flashes that will be discussed in a later section. By implementing such methods, the same flash images could be collected with a lower dose, as represented by the dotted, dashed and full curves marked with triangles. This reduced dose level would not be significantly detrimental to the conformal avoidance of sensitive structures. Another way of viewing these data is through the average additional dose against original dose curve shown in figure 7(b). That is, the additional dose received in a voxel for the modified treatment is related to the dose received in that voxel for the unmodified treatment. There is a strong correlation indicating that the lower dose points suffer a greater dose increase from the treatment modification, while the higher dose points are affected far less. Specifically, for this treatment plan and delivery, the additional dose from the treatment modification ranged from 2 cGy to 12 cGy, as represented by the full curve. However, this dose can be reduced to the level indicated by the lower dashed curve, through several hardware modifications. Even with dose reductions, one needs to maintain concern for sensitive structures. An additional imaging dose may be detrimental to such structures, although these structures could also be seriously harmed if any anatomical changes or internal motions were undetected. Thus, the benefits of verification need to be weighed against the costs in dose, which is precisely why developments in verification techniques are important. Pretreatment scanning allows for better patient positioning, and imaging during or after the treatment further gives the benefit of anatomical verification and dose reconstruction. Through dose reconstruction and adaptive radiotherapy, hot or cold spots in one fraction can be remedied in subsequent fractions. Though, regardless of whether the additional imaging dose is high or low, or whether it is for MVCT or
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Table 2. The noise variance ratios for three different regions of the different reconstructed images. These ratios indicate how much noisier a region is than the corresponding image in the 175 cGy conventional MVCT. Thus low values signify that a reconstruction has contrasts comparable with the high-dose image, whereas very high numbers represent much noisier images. Noise variance in each region of an image relative to the noise variance for the 175 cGy conventional MVCT Image being compared with the 175 cGy conventional MVCT
Treatment region
Sensitive structure
Normal tissue
175 cGy conventional MVCT 7.0 cGy conventional MVCT 0.9 cGy conventional MVCT 0.9 cGy conventional MVCT, augmented with high dose data from unmodulated treatment 0.9 cGy conventional MVCT, augmented with normalizable data from modulated delivery Reconstruction from the 23 flashes collected during the modified treatment delivery Reconstruction of data from the 23 flashes, augmented with normalizable data from the modified treatment
1.0 22 160 1.9
1.0 19 170 1.2
1.0 20 150 66
40
140
93
39
38
55
13
31
26
kVCT, it is appropriate to incorporate all the expected dose into the optimization process. This allows the imaging dose to be mitigated by adjustments in the treatment plan, such that the net delivery of dose to the patient will best match the intended delivery. Additionally, knowledge of the effective imaging dose helps to determine the frequency with which CT verification is appropriate, and how to balance the benefits of imaging against the costs in dose. One method of analysing the improvement in image contrast from augmenting low-dose images with high-dose treatment data is to compare the noise characteristics of the images. Thus, the noise variance was computed for three regions in each image: the treatment region, the sensitive structure and a more peripheral portion of the normal tissue. It is known that the use of filtering can affect the noise measurements, as well as the appearance of the images. For this reason, the same apodizing filters were used for all of these reconstructions. Similarly, the augmentation processing routines were carefully constructed to avoid additional filtering or averaging, which could bias the results. These noise variances, for each region of each image, are listed in table 2 as ratios versus the corresponding noise variances in the 175 cGy conventional, complete FOV, MVCT image 175 cGy (figure 5(a)). That is, the values are calculated using the ratio ROI σ 2 /ROI σ 2 . Values of this ratio near 1.0 indicate that an image is comparable in quality to the 175 cGy image. Higher values indicate how much noisier than the 175 cGy reconstruction a given image is. For the three conventional scans, these ratios closely mimic the dose ratios, as expected. For example, a dose of 175 cGy is 25 times greater than 7 cGy, so the noise variance in the 7 cGy images should be greater by approximately that same factor. For the low-dose, treatment-augmented reconstructions, it can be seen that the noise variance ratios are much smaller than for the low-dose reconstructions alone. The most extreme case is for the 0.9 cGy image that is augmented with data from the unmodulated treatment. In the ‘treatment region’ and the ‘sensitive structure’, both of which receive constant high-dose delivery for the unmodulated non-conformal plan, the noise variance ratios fall to values below two. In other words, the effective imaging dose for those regions is increased from 0.9 cGy to
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Figure 8. An image-set rendering and two tomographic slices of a German shepherd cadaver, acquired using different methodologies: (a) 6 cGy kVCT on a Siemens Hi-Q scanner, (b) 10 cGy pretreatment MVCT, (c) MVCT images of the dog using only the 25.5 flashes collected during each rotation of the treatment.
over 90 cGy. The ‘normal tissue’ region, which is located outside of the unmodulated treatment delivery region, has a much more modest noise improvement, as the effective imaging dose is increased by a factor of 150/66 = 2.3. The pattern of the results is similar for the other reconstructions, although the magnitudes are reduced. The augmentation of the 0.9 cGy pretreatment scan with the data from the optimized treatment delivery reduces the noise variance ratio from 160 to 40 in the treatment region. This corresponds to a four-fold increase in the effective imaging dose. For this same image, the improvements in noise variance are much smaller for the sensitive structure and the normal tissue, which corroborates the visual inspection (figure 5(e)). This result is indicative of the fact that the optimized plan was designed to minimize dose to the sensitive structure and the healthy tissue, and without this additional dose the improvement in contrast is more marginal. Finally, the same pattern of improvement in contrast presents itself by comparing the noise variance ratios for the reconstructions using the treatment modification flashes. Specifically, the addition of treatment data to the flashes-only reconstruction increases the effective imaging dose in the treatment region, allowing for a three-fold reduction of noise. The improvements are smaller for the sensitive structure and normal tissue, as these regions receive less treatment dose, and therefore have less additional information that can be used to augment the low-dose data. 3.2. Dog cadaver results In addition to demonstrating these techniques on the low-contrast solution phantom, some of these methods are also illustrated as part of a tomotherapeutic treatment made to the head
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of a German shepherd cadaver on the tomotherapy benchtop. In this case, a kVCT of the head was used to plan a treatment to a sinus tumour, and the delivery was made on the benchtop immediately following an MVCT. Figure 8(a) shows a rendering of the kVCT images performed on Pinnacle (ADAC Laboratories, Milpitas, CA), as well as two of the tomographic slices. This kVCT delivered a dose of 6 cGy to the centre of the nose, as measured with TLDs. The set of images in figure 8(b) shows the MVCT of the dog acquired immediately prior to the treatment. This MVCT delivered a dose of 10 cGy to the nose. This treatment delivery was modified to include the collection of a leaf-flash during every other projection. There were 51 projection angles per rotation, 34 rotations, and the helical pitch was one-half. Thus, for every two rotations of the cadaver, the table moved one slice thickness of 0.76 cm and a total of 51 flashes were collected. Sets of 51 flashes were reconstructed, and can be seen in figure 8(c), which shows two tomographic slices collected through the use of flashes as well as a rendering based on the entire set of 33 flash-images spanning the treatment region. The useful imaging dose of these flash pulses was 2.5 cGy, although the dose delivered during the motion of the leaves increased the total additional flash dose to a maximum of 10 cGy. However, there are methods for reducing the delivered dose and increasing the efficiency of dose utilization, as remain to be discussed below. Likewise, the small number of view angles causes streak artefacts that degrade image quality relative to the pretreatment scan, and these will also be discussed. The comparison of these images with others acquired during the treatment using only MLC leakage and transmission can be found at http://www.madrad.radiology.wisc.edu/tomo/megavoltage/ mvct.html. One difference that should be noted between this set of images collected during the treatment (figure 8(c)) and the low-contrast phantom images is that these treatment images of the dog were not augmented with treatment data. That is, while the flash views do allow for treatment imaging, the augmentation with other treatment data did not benefit reconstruction in this case. The reason is because this treatment was to a very small tumour; typically only one or two leaves were open at a time, corresponding to about 0.9 to 1.8 cm at isocentre. As a result, there were far fewer high-dose treatment data available in this case than for the low-contrast phantom treatment that had a larger treatment region and more leaves open. However, even without the additional benefits of incorporating the incomplete views of the treatment data, the technique of treatment modification is still useful as a means of extracting a subset of the treatment data to generate images of the patient at the exact time of the treatment delivery. 4. Discussion In the previous section, it was seen that data could be collected entirely during the treatment to provide an image of the patient in the treatment position. Moreover, in some cases the use of treatment data in conjunction with MVCT image reconstruction can qualitatively and quantitatively improve the contrast in images. The biggest contrast improvements are in the treatment region, as that region receives the most additional dose that can be used to improve image noise statistics. For the unmodulated plan, the effective imaging dose in the treatment region improved from 0.9 cGy in the pretreatment image to over 90 cGy in the augmented image. The normalizable fraction of the modulated treatment data, when used to augment the 0.9 cGy pretreatment image, increased the effective imaging dose in the tumour by a factor of four, to 3.6 cGy. The point is that not only can augmentation with treatment data improve contrast, but another option is to maintain contrast while reducing imaging dose. Instead of delivering a 7 cGy pretreatment scan, one can obtain similar results with a lower-dose scan and treatment augmentation. This latter method may allow for more frequent MVCT, such that daily tomographic imaging might be possible.
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However, it is also important to examine the issue of artefacts. That is, contrast improvements are helpful, but the benefit is tempered if new artefacts otherwise degrade the image quality. This is the reason why leakage images were not found, at present, to be useful in the context of augmentation with treatment data. Similarly, both the pretreatment augmentation and treatment modification methods of image reconstruction do suffer from artefacts due to the relatively small number of views (69 and 23, respectively, for the lowcontrast phantom and 51 for the dog treatment). In each case, streak reduction could be achieved by collecting more pretreatment views or a larger number of flashes. For the present benchtop system, collecting these additional data would require additional dose. Nonetheless, improvements are expected on the UW tomotherapy prototype that would allow for significant dose reduction, as the linac will have a tunable output and a gridded gun, instead of a fixed pulse rate and output per pulse. This will allow a given dose to the patient to be spread over a larger number of pulses. For example, instead of delivering a 1 cGy pretreatment scan with 69 pulses, one could divide that 1 cGy amongst several hundred views, which would reduce streaking. Similarly, the linac output rate could be reduced during the flashes, and optimally shut-off altogether during the leaf-opening and closing portion of the flashes. This would mean that the additional dose contributed by each flash would be diminished and there would no longer be the inefficiency of non-normalizable dose delivered while the leaves moved. Again, this reduction is illustrated by the three curves marked with triangles in figure 7(a), and the dashed curve in figure 7(b). Briefly turning the linac off in this manner could also reduce tumour dose, so the point remains that any flashes are ideally accounted for during the optimization process. Ultimately, instead of being limited to around 23 flashes that contribute up to 12 cGy, these expected improvements will allow for a larger number of flashes, yet still significantly reduce the total additional dose. One other anticipated improvement is faster leaf motion. This will be helpful to the treatment modification technique because each flash will require less time, thereby reducing the additional dose and the disruption to the treatment plan. Likewise, faster leaves will result in fewer unstable views. This will allow a larger number of treatment data to be normalized, allowing for greater contrast improvements. Somewhat larger contrast improvements are also expected for actual treatments in that typical treatments, will deliver around 200 cGy whereas this modulated treatment, with the given scan parameters, only delivered 120 cGy to the tumour. Again, this increased dose directly translates to an increased number of normalizable data, which should improve contrast. Likewise, improvements in image quality are also expected as the implementations of these treatment augmentation techniques are further developed. At present, probably the most effective way to reconstruct data collected during treatment delivery is to use the pretreatment augmentation technique. The benefit stems from the fact that it can reduce the dose of daily, pretreatment MVCT images from around 7 cGy to perhaps 1 or 2 cGy, without significantly compromising image contrast in the treatment region, and potentially saving time. However, one may wish to save even more time by removing the pretreatment scanning stage altogether. If this happens, one can still generate MVCT images using data collected entirely during the treatment by utilizing the treatment modification technique. At present this method of increasing patient throughput without loss of imaging comes at the cost of increased dose and a reduced number of imaging views. This is because the flashes needed for this technique currently deliver a relatively large dose due to the high linac output rates and the slow leaf speeds. Yet in upcoming tomotherapy systems these problems should be remedied, such that better images could be acquired entirely during the treatment with only marginal additional doses, through the use of a gridded-gun linac and faster leaves. This would also be useful for determining the patient’s position during the treatment.
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5. Conclusion The potential for reconstructing MVCT images during tomotherapy treatments has been investigated. In addition, the possibilities for incorporating the incomplete data collected during modulated tomotherapeutic delivery have been studied. The main impediments to image reconstruction using treatment data are the incompleteness of the data sets and the uncertainties in the normalization of data for modulating leaves. Methods were developed to partially address both of these problems. Thus, images were reconstructed using the treatment data acquired during a delivery on the UW tomotherapy benchtop. By using the pretreatment augmentation technique, one can reduce patient imaging dose to a few cGy, yet maintain contrast comparable to a higher-dose image. It is also possible to reduce patient time on the treatment table and determine the patient’s precise position at the time of the delivery by eliminating pretreatment MVCT and collecting all of the data necessary for reconstruction during the treatment itself. This latter technique will become more clinically viable as expected hardware enhancements allow for a significant reduction in the additional dose required. Acknowledgments The authors would like to thank Dr John Balog, Dr David Pearson, Dr Guang Fang and Ralf Hinderer for their assistance with data collection on the tomotherapy benchtop. We are also grateful to Jennifer Smilowitz and Lisa Forrest DVM for their coordination of the treatment to the German shepherd cadaver. References Aoki Y, Akanuma A, Evans P M, Lewis D G, Morton E J and Swindell W 1990 A dose distribution evaluation utilizing megavoltage CT imaging system Radiat. Med. 8 107–10 Balog J 1998 Tomotherapy dosimetry and the tomotherapy benchtop PhD Thesis Department of Medical Physics, University of Wisconsin-Madison Boellaard R, van Herk M, Uiterwaal H and Mijnheer B 1998 First clinical tests using a liquid-filled electronic portal imaging device and a convolution model for verification of the midplane dose Radiother. Oncol. 47 303–12 Brahme A, Lind B and Nafstadius P 1987 Radiotherapeutic computed tomography with scanned photon beams Int. J. Radiat. Oncol. Biol. Phys. 13 95–101 Groh B A, Siewerdsen J H, Drake D G, Wong J W and Jaffray D A 2000 MV and kV cone-beam CT on a medical linear accelerator ICCR The Use of Computers in Radiation Therapy ed W Schlegel and T Bortfeld (Heidelberg: Springer) pp 561–3 Guan H and Zhu Y 1998 Feasibility of megavoltage portal CT using an electronic portal imaging device (EPID) and a multi-level scheme algebraic reconstruction technique (MLS-ART) Phys. Med. Biol. 43 2925–37 Hesse B, Spies L and Groh B 1998 Tomotherapeutic portal imaging for radiation treatment verification Phys. Med. Biol. 43 3607–16 Jaffray D, Drake D, Moreau M, Martinez A and Wong J 1999a A radiographic and tomographic imaging system integrated into a medical linear accelerator for localization of bone and soft-tissue targets Int. J. Radiat. Oncol. Biol. Phys. 43 773–89 Jaffray D, Yan D and Wong J 1999b Managing geometric uncertainty in conformal intensity-modulated radiation therapy Semin. Radiat. Oncol. 9 4–19 Kapatoes J M, Olivera G H, Reckwerdt P J, Fitchard E E and Mackie T R 1999 Delivery verification in sequential and helical tomotherapy Phys. Med. Biol. 44 1815–41 Kapatoes J M, Olivera G H, Ruchala K J, Reckwerdt P J, Smilowitz J B, Balog J P, Pearson D W and Mackie T R 2000 Database energy fluence verification and the importance of on-board CT imaging in dose reconstruction ICCR The Use of Computers in Radiation Therapy ed W Schlegel and T Bortfeld (Heidelberg: Springer) pp 294–6 Keller H, Glass M, Olivera G H and Mackie T R 2000 Monte Carlo evaluation of a highly efficient photon detector for tomotherapy ICCR The Use of Computers in Radiation Therapy ed W Schlegel and T Bortfeld (Heidelberg: Springer) pp 150–2
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Lewis D G, Swindell W, Morton E J, Evans P M and Xiao Z R 1992 A megavoltage CT scanner for radiotherapy verification Phys. Med. Biol. 37 1985–99 Mackie T R 1997 Tomotherapy Proc. 12th Int. Conf. on the Use of Computers in Radiation Therapy (Salt Lake City, Utah) ed D D Leavitt and G Starkshall (Madison, WI: Medical Physics Publishing) pp 9–11 Mackie T, Balog J, Ruchala K, Shepard D, Aldridge J, Fitchard E, Reckwerdt P, Olivera G, McNutt T and Mehta M 1999 Tomotherapy Semin. Radiat. Oncol. 9 108–17 Mackie T R, Holmes T, Swerdloff S, Reckwerdt P, Deasy J O, Yang J, Paliwal B and Kinsella T 1993 Tomotherapy: a new concept for the delivery of dynamic conformal radiotherapy Med. Phys. 20 1709–19 Mackie T R et al 1997 Tomotherapy, rethinking the process of radiotherapy Proc. 12th Int. Conf. on the Use of Computers in Radiation Therapy (Salt Lake City, Utah) ed D D Leavitt and G Starkshall (Madison, WI: Medical Physics Publishing) pp 9–11 Margosian P M 1982 A redundant ray projection completion method for an inverse fan beam computed tomography system J. Comput. Assist. Tomogr. 6 608–13 McNutt T R, Mackie T R, Reckwerdt P J and Paliwal B R 1996a modelling dose distributions from portal dose images using the convolution/superposition method Med. Phys. 23 1381–92 McNutt T R, Mackie T R, Reckwerdt P J, Papanikolaou N and Paliwal B R 1996b Calculation of portal dose using the convolution/superposition method Med. Phys. 23 527–35 Midgley S, Millar R and Dudson J 1998 A feasibility study for megavoltage cone beam CT using a commercial EPID Phys. Med. Biol. 43 155–69 Morton E J, Swindell W, Lewis D G and Evans P M 1991 A linear array, scintillation crystal-photodiode detector for megavoltage imaging Med. Phys. 18 681–91 Mosleh-Shirazi M, Evans P, Swindell W, Webb S and Partridge M 1998 A cone-beam megavoltage CT scanner for treatment verification in conformal radiotherapy Radiother. Oncol. 48 319–28 Nakagawa K, Aoki Y, Akanuma A, Onogi Y, Terahara A, Sakata K, Muta N, Sasaki Y, Kawakami H and Hanakawa K 1994 Real-time beam monitoring in dynamic conformation therapy Int. J. Radiat. Oncol. Biol. Phys. 30 1233–8 Nalcioglu O, Cho Z H and Lou R Y 1979a Limited field of view reconstruction in computerized tomography IEEE Trans. Nucl. Sci. 26 546–51 Nalcioglu O, Sankar P and Sklansky J 1979b Region-of-interest x-ray tomography (ROIT) Proc. SPIE 206 98–102 Olivera G et al 1999 Tomotherapy Modern Technology of Radiation Oncology ed J Van Dyk (Madison, WI: Medical Physics Publishing) pp 521–87 Partridge M, Evans P M and Mosleh-Shirazi M A 1998 Linear accelerator output variations and their consequences for megavoltage imaging Med. Phys. 25 1443–52 Ruchala K 1999 Megavoltage computed tomography for tomotherapeutic verification PhD Thesis Department of Medical Physics, University of Wisconsin-Madison Ruchala K, Olivera G, Kapatoes J, Schloesser E, Reckwerdt P and Mackie T 2000a Megavoltage CT Imaging as a by-product of multi-leaf collimator leakage Phys. Med. Biol. 45 N61–N70 Ruchala K, Olivera G, Schloesser E, Hinderer R and Mackie T 2000b Calibration of a tomotherapeutic megavoltage CT system Phys. Med. Biol. 45 N27–N36 Ruchala K, Olivera G, Schloesser E and Mackie T 1999 Megavoltage CT on a tomotherapy system Phys. Med. Biol. 44 2597–621 Simpson R G, Chen C T, Grubbs E A and Swindell W 1982 A 4-MV CT scanner for radiation therapy: the prototype system Med. Phys. 9 574–9 Spies L 2000 On scatter in megavoltage x-ray transmission imaging Dipl.-Phys. Department of Medical Physics, Deutsches Krebsforschungszentrum-Heidelberg Spies L, Evans P M, Patridge M, Hansen V N and Bortfelt T 2000 Direct measurement and analytical modelling of scatter in portal imaging Med. Phys. 27 462–71 Swindell W 1983 A 4-MV CT scanner for radiation therapy: spectral properties of the therapy beam Med. Phys. 10 347–51 Swindell W, Morton E J, Evans P M and Lewis D G 1991 The design of megavoltage projection imaging systems: some theoretical aspects Med. Phys. 18 855–66 Swindell W, Simpson R G and Oleson J R 1983 Computed tomography with a linear accelerator with radiotherapy applications Med. Phys. 10 416–20 Yu C, Jaffray D and Wong J 1998 The effects of infra-fraction organ motion on the delivery of dynamic intensity modulation Phys. Med. Biol. 43 91–104
