British Journal of Radiology (2006) 79, S87-S98
© 2006 British Institute of Radiology
doi: 10.1259/bjr/60612178
Dose-guided radiation therapy with megavoltage cone-beam CT
J Chen, PhD
O Morin, BSc
M Aubin, Eng-MSc
M K Bucci, MD
C F Chuang, PhD
and
J Pouliot, PhD
UCSF Comprehensive Cancer Center, Department of Radiation Oncology, University of California San Francisco, 1600 Divisadero Street, Suite H1031, San Francisco, CA 94143, USA
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Abstract
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Recent advances in fractionated external beam radiation therapy have increased our ability to deliver radiation doses that conform more tightly to the tumour volume. The steeper dose gradients delivered in these treatments make it increasingly important to set precisely the positions of the patient and the internal organs. For this reason, considerable research now focuses on methods using three-dimensional images of the patient on the treatment table to adapt either the patient position or the treatment plan, to account for variable organ locations. In this article, we briefly review the different adaptive methods being explored and discuss a proposed dose-guided radiation therapy strategy that adapts the treatment for future fractions to compensate for dosimetric errors from past fractions. The main component of this strategy is a procedure to reconstruct the dose delivered to the patient based on treatment-time portal images and pre-treatment megavoltage cone-beam computed tomography (MV CBCT) images of the patient. We describe the work to date performed to develop our dose reconstruction procedure, including the implementation of a MV CBCT system for clinical use, experiments performed to calibrate MV CBCT for electron density and to use the calibrated MV CBCT for dose calculations, and the dosimetric calibration of the portal imager. We also present an example of a reconstructed patient dose using a preliminary reconstruction program and discuss the technical challenges that remain to full implementation of dose reconstruction and dose-guided therapy.
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The rationale for adaptive radiation therapy and dose-guided radiation therapy
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Recent advances in fractionated external beam radiation therapy, such as three-dimensional conformal and intensity-modulated radiation therapy (IMRT), have increased our ability to deliver radiation doses that conform more tightly to the tumour volume. Clinical studies and simulations indicate that these more conformal, higher dose treatments can decrease both the spread of disease and normal tissue complications [1–5]. Increasing use of functional imaging will also motivate further complexity in radiation treatment plans to include concurrent boosts in regions of high cancerous growth [6, 7]. As these dose distributions conform more tightly to the patient anatomy, dose gradients necessarily become steeper inside the irradiated volume. Using IMRT, a dose gradient of 10% mm–1 can be achieved easily. Thus, it is increasingly important to set precisely the positions of the patient and the internal organs. Currently, external markers and patient immobilizing masks and casts are used to reproduce the skeletal position of the patient with about 3 mm accuracy over several weeks of treatment [8]. However, the effectiveness of these alignment and immobilization techniques are limited by changes in the internal organ locations relative to bony and external markers. For example, the prostate can shift up to 1 cm relative to the pelvic bones due to variations in rectal/bladder filling. During the course of head and neck cancer treatment, the tumour can shrink and the patient can lose significant weight, resulting in dosimetric errors as large as 40% [9, 10]. For this reason, imaging tools in the treatment room and methods of adapting treatments to match the patient anatomy on the treatment table are the keys to realising the full benefit of conformal therapy.
For many decades, imaging inside the treatment room has played a role in verifying radiation therapy treatment. Portal images, projection images of the patient using the treatment aperture, are used to confirm the patient position and verify coverage of the tumour. The use of radiographic film for portal imaging has limited the frequency of this verification due to the required time and dose to the patient. However, recent implementation of electronic portal imaging devices (EPIDs) allows a digital image to be acquired in a few seconds with low doses. This has allowed the use of daily portal imaging to visualize and adjust the patient position before each treatment. For example, using implanted gold markers to locate the prostate, daily portal imaging has been used to position the prostate with 1–2 mm accuracy [11–13]. The use of portal imaging to adjust patient position before treatment is limited, however, because soft tissue cannot be visualized without implanted markers and the full three-dimensional (3D) geometry is obscured by the projection onto a two-dimensional (2D) plane. Therefore, considerable research now focuses on developing three-dimensional imaging of the patient on the treatment table. Several systems have been developed including (1) a "CT on rails" system, requiring an additional diagnostic CT machine in the treatment room [14]; (2) a kilovoltage cone-beam CT (kV CBCT) system, consisting of an additional kV X-ray source and detector attached to the treatment gantry [15, 16] (these systems are described more fully in this issue in papers by Thieke et al and Moore et al, respectively); (3) a megavoltage cone-beam CT (MV CBCT) system using the pre-existing treatment machine and EPID for imaging [17–19]; (4) a MV CT system, using the pre-existing treatment machine with an attached arc of detectors [20]; and (5) a tomotherapy system, replacing the traditional treatment machine (beam) with a CT ring and a MV beam source [21–23]. These imaging systems continue to improve and recent results indicate that 1–2% soft-tissue contrast resolution is possible [15, 17, 18, 21] as well as accurate localization of various tumours [14, 16, 19, 20, 22, 23].
In the above examples of image-guided radiation therapy (IGRT), treatment room imaging modalities are used to translate and rotate the patient to better match the patient position used for treatment planning. Another potentially more powerful use of these images is to modify the delivered treatment fields to account for the variable patient position. This type of adaptive radiation therapy could adjust for the changing relative positions of the internal organs and the changing shape of the organs. This is particularly important for organs that move significantly during the course of treatment. For these sites, techniques under current development include gated treatments (halting irradiation when the target is out of a certain acceptable region) [24–27] or target tracking during irradiation using specially designed mobile linear accelerators [28, 29]. For some sites, however, the most important anatomical changes occur between treatment fractions. In this case, a pre-treatment image may be used to adjust the treatment fields immediately before irradiation [30, 31]. Another possibility is to determine patient-specific anatomical variation using images from the first week of treatment and to tailor the treatment plan for future fractions to account for the individual's variation [32–34]. Finally, if the dose that was delivered in previous fractions can be estimated, the treatment plan for future fractions may be re-optimized to compensate for dosimetric errors [35]. This dose-guided therapy could correct for both errors due to patient anatomical changes as well as machine delivery errors, thus providing the most accurate dose delivery. The various adaptive radiation therapy schemes are depicted in Figure 1
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Figure 1. A general view of adaptive radiation therapy. The large grey arrow represents the conventional flow of treatment, and the small arrows indicate the possible points of feedback into the process.
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The development of dosimetric verification and reconstruction
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Currently, few methods are used to track the dose delivered during treatment. Standard techniques involve measuring doses on the patient surface using diodes or thermoluminescent dosemeters. However, these techniques provide only point dose measurements, and the time and effort to place the dosemeters on the patient and process the data limit their clinical use. Consequently, few institutions use these methods regularly for treatment verification. A new implantable MOSFET dosemeter has also been developed [36]. This dosemeter directly measures the dose in critical internal structures, but again provides only a point measurement and is an invasive technique with limited application. What is needed to verify conformal therapies is an automated method to reconstruct the full 3D dose distribution.
Several researchers have suggested methods to reconstruct the delivered patient dose during treatment. Most methods propose using on-board EPIDs to quickly and easily acquire a two-dimensional array of digitized X-ray measurements in a precisely positioned plane in the treatment exit beam. A few formulae have been derived to estimate the dose to the exit surface, midplane, or centre point of the patient based solely on EPID measurements [37–40]. To find a 3D patient dose distribution, however, requires additional information about the patient position and attenuation of the beam. For breast treatments, a simple patient contour may give sufficient information [41]. However, in general, information on tissue inhomogeneity is also necessary. Several years ago, it was suggested that the planning CT could be used for this purpose [42, 43], but this method would fail to detect dosimetric errors produced by the variable patient and organ positions and shapes. The 3D imaging modalities that are being developed for IGRT provide an obvious opportunity to simultaneously obtain the patient geometry for reconstructing dose. Currently, there is active development of dose reconstruction procedures for tomotherapy systems, and 3% accuracy in low-gradient regions has been demonstrated [44]. A pilot study using MV CBCT on a traditional treatment machine also found good relative agreement with measurements, but a systematic absolute deviation [45].
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Dose-guided radiation therapy using MV CBCT and treatment-time portal images
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In 2003 [46], we began developing a procedure to reconstruct the dose delivered to the patient based on treatment-time portal images and pre-treatment MV CBCT. Our procedure follows the steps described below and depicted in Figure 2.
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Figure 2. Overview of proposed dose reconstruction procedure using MV CBCT imaging and treatment-time portal imaging.
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Step 1A: Prior to treatment, with the patient in the treatment setup position, acquire a MV CBCT image. This image can be used to align the patient as closely as possible to the planned position and also provides the photon attenuation information necessary to reconstruct the delivered dose.
Step 1B: Convert the MV CBCT image to effective photon attenuation coefficient. Generally, this can be accomplished by calibrating the MV CBCT system using a calibration phantom composed of materials with known electron densities. However, imaging artefacts in the MV CBCT image may need to be corrected to improve the calibration accuracy.
Step 2A: During the treatment, acquire portal images of the treatment beam as it exits the patient. This portal image is acquired using the same EPID used for the CBCT imaging.
Step 2B: Convert the portal images to a 2D map of treatment beam energy fluence. The acquired portal image signal is a convolution of the energy fluence incident on the detector with the detector response to radiation. Moreover, the energy fluence consists of both the primary beam and radiation scattered from the patient. To use the portal image for dose calculations, the primary energy fluence must be derived from the portal image.
Step 3: Back-project the energy fluence measured at the detector plane through the CBCT of the patient, accounting for the 1/r2 falloff of radiation from a point source and attenuation through the patient. This calculation is easily accomplished if the position of the detector plane relative to the patient and source is accurately known.
Step 4: Calculate the 3D dose distribution delivered to the patient using a dose calculation engine. This type of dose calculation is the same as that performed for treatment planning purposes, and all the techniques that have been developed for treatment planning may be used.
The reconstruction procedure described above provides an estimate of the 3D dose distribution deposited in the patient as represented by the MV CBCT. Several uses of the reconstructed dose distribution to guide future treatments can be envisaged. Scenario 1: The most basic use of the reconstructed dose is to provide a dosimetric verification that the treatment delivery generally provides the desired dose distribution and that no gross errors exist. This verification could be performed during the first treatment and repeated weekly throughout treatment. This simple approach would effectively reduce gross dosimetric errors, but would not otherwise increase the precision of the delivered dose. Scenario 2: If the patient dose is reconstructed for the first week of treatment, the variation in the delivered dose may also be evaluated. If the MV CBCT for each treatment is contoured to delineate the various important structures, the variation in dosimetric indices, such as the maximum dose to sensitive normal structures or the dose to 95% of the tumour volume, can be calculated. General systematic trends such as the under or over dosing of particular extremities of a structure may also be detected by examining the dose distributions over the first week. Based on this information, the treatment plan can be modified, for example, to increase or decrease margins of the tumour in particular directions. In this manner, the treatment plan can be tailored to each individual patient. Scenario 3: Finally, a complete dose-guided therapy system would be able to integrate the dose over previous fractions. This would require the ability to deform the daily MV CBCT images to map identical points in the patient before the integral dose is calculated [47]. The cumulative dose distribution can be used to adjust the treatment plan to compensate for deviations from the desired distribution, thus improving the accuracy and conformality of the overall treatment.
The dose reconstruction procedure and the dose-guided therapy described above continue to be developed and researched. This article summarizes the work to date and comments on the remaining challenges. First, we present a description of a MV CBCT system that has been implemented on a linear accelerator for clinical use. We then describe experiments performed to calibrate the MV CBCT for electron density and to use the calibrated MV CBCT for dose calculations. We also briefly describe the dosimetric calibration of an EPID for dose reconstruction. Finally, we present an example of a reconstructed patient dose using a preliminary reconstruction program and discuss the technical challenges that remain to full implementation of dose reconstruction and dose-guided therapy.
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MV cone-beam CT imaging
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MV cone-beam CT imaging is a 3D reconstruction procedure similar to conventional CT. A series of projection measurements, in this case 2D portal images, are acquired at many angles around the patient. The image reconstructed is a 3D image without slice artefacts. In the radiation oncology context, the imaging beam is produced by the conventional linear accelerator used for treatment, and the projection images are detected using on-board EPIDs. The imaging photons, therefore, are primarily in the mega-electron volt energy range. In this configuration, the patient can be positioned once on the treatment table and need not be repositioned between imaging and treatment.
As the linear accelerator gantry and the EPID rotate about the patient, the EPID and beam source positions will shift from their ideal isocentric locations due to sagging of the mechanical supports. To correct for this effect, we perform a geometric calibration of the system, illustrated in Figure 3 [48, 49]. This calibration provides a unique relationship between the position of a voxel in the reconstruction volume and a pixel on the detector plane for each angle. Because the EPID used for imaging is also used to detect the exit beam fluence, the same calibration information can be employed during the dose reconstruction procedure to back-project the energy fluence through the MV CBCT volume. This prevents any possibility of misregistration between the EPID measurements and the MV CBCT volume.
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Figure 3. Depiction of the geometric calibration of the linear accelerator/electronic portal imaging device (EPID) system for cone beam CT (CBCT) imaging and for dose reconstruction. The result of the calibration is a set of projection matrices (P) that map a point in space (RXYZ) to the projected point on the detector plane (Ruv).
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The MV CBCT system installed in our clinic has been previously described [19]. Briefly, it consists of an amorphous-silicon flat panel EPID integrated with a clinical linear accelerator. The total exposure of the CBCT acquisition can be varied from 1 to 60 monitor units. Upon patient selection, a reference CT is automatically loaded into the software. The linear accelerator gantry then rotates in a continuous 200° arc acquiring images at 1° increments. This acquisition procedure lasts about 45 s. The image reconstruction starts immediately after the acquisition of the first portal image, and a 256x256x256 reconstruction volume is completed in 110 s. The software automatically registers the MV CBCT with the reference CT and calculates table shifts for patient alignment.
To date, 38 patient MV CBCT images have been acquired in our clinic. All patients have given informed consent, and the patient image acquisitions are performed in accordance with the institutional review board's ethical standards. Depending on the frequency of the acquisitions, the dose used for MV CBCT ranges from approximately 1.5 cGy to 12 cGy delivered at the point of rotation (the isocentre). The dose at the entrance surface of the arc reaches about 160% of the isocentre dose for an imaged pelvis and 133% for the head and neck region. The dose at the exit surface falls to about 66% of the isocentre dose for a pelvis and 55% for the head and neck region. Figure 4 presents four MV CBCT images acquired weekly on the same patient to study tumour evolution. At each new acquisition, the dose was lowered. The last CBCT of the series was acquired with approximately 2.9 cGy delivered at the isocentre, still presenting enough soft-tissue information to assess the tumour size and perform patient alignment.
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Figure 4. Examples of megavoltage cone beam CT(MV CBCT) images at different exposure levels, from 2.9 cGy to 10 cGy.
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Three-dimensional imaging of the patient in the treatment position exposes the difficulties created by distortion of patient anatomy. Figure 5 displays the fusion of a MV CBCT image (grey) with the planning CT (colour). In this case, a physician has manually registered the two sets of images by aligning the base of the skull. A considerable shift, up to 6 mm, can be observed in the positions of the spinal cord between the two image sets. This misplacement of the spinal cord could not be corrected by translating or rotating the MV CBCT image relative to the CT as it was caused by an increase in the arching of the patient's neck. Although several fractions would be needed to assess if this misplacement occurs regularly, the new anatomy, as depicted by the MV CBCT image, could be used to study the dosimetric impact of the patient's anatomical distortion.
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Figure 5. Registration of a patient megavoltage cone beam CT(MV CBCT) (grey) with the kV CT (colour) used for treatment planning. A large difference in the arching of the neck causes a considerable deviation in the spinal cord position.
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MV CBCT calibration for dose calculation
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To use the MV CBCT image in a dose reconstruction program, the signal from each voxel must be converted to effective photon attenuation coefficient for the beam spectrum (Step 1B of our dose reconstruction procedure). To perform this conversion, the MV CBCT system can be calibrated using a CT calibration phantom (CIRS Model 062, Norfolk, VA) with tissue-equivalent inserts, as is currently done with kV CT. A table is formed mapping CT signal intensity to electron or physical density which can then be converted to photon attenuation coefficient for a known beam spectrum. Figure 6 shows the results of performing this simple calibration on our MV CBCT system using the following inserts of relative electron density with respect to water: lung inhale (0.190), lung exhale (0.489), adipose (0.952), breast (0.976), water (1), muscle (1.043), liver (1.052), trabecular bone (1.117) and dense bone (1.512). The relationship between MV CBCT signal and electron density is linear. These results are similar to previous work with MV fan-beam CT performed on a tomotherapy unit at 6 MV [50].
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Figure 6. Megavoltage cone beam CT(MV CBCT) intensity as a function of electron density for tissue-equivalent inserts in a CT calibration phantom (pictured in above left).
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Although the above calibration works well for the narrow CT calibration phantom, the MV CBCT images of extended objects exhibit cupping artefacts due to the influence of scattered radiation reaching the EPID. Figure 7 illustrates this cupping effect on the MV CBCT of a large cylinder of water. If uncorrected, this cupping artefact will also appear in the image converted to photon attenuation coefficient, leading to errors in the calculated dose. However, a simulation study using the large cylinder of water pictured in Figure 7 indicates that the dosimetric errors in a homogeneous medium produced by such severe cupping artefacts remain relatively small, approximately 4% for a single open field [51]. This suggests that a crude correction of the cupping artefact in MV CBCT images may be sufficient to obtain acceptable dosimetric accuracy. To test this hypothesis, the MV CBCT of a water cylinder was used to model the spatial dependence of the cupping artefact. A spatially dependent correction function was derived from this cupping model. This correction function was then applied to the MV CBCT of an anthropomorphic head phantom as a rough correction for the cupping artefact in the image. After conversion to density using the MV CBCT calibration curve, this image was imported into a commercial treatment planning system (Philips Pinnacle, Bothell, WA). The dose calculated using the MV CBCT compared well with the dose calculated using a kV CT of the same phantom. Using a gamma index comparison with a 3% dose and 3 mm distance-to-agreement criterion, 98% of calculated dose points fell within the acceptance criteria.
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Figure 7. Radial(top row) and axial (bottom row) profiles through the megavoltage cone beam CT (MV CBCT) images of a large cylinder filled with water. The unmodified CBCT (left) exhibits a large cupping artefact as a result of scattered radiation reaching the electronic portal imaging device (EPID). Using a simple 3D cupping model effectively reduces the artefact (right). The radial and axial slices of the MV CBCT images (insets) are displayed using the same windowing level.
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The above example demonstrates the potential of using MV CBCT images for dose calculations. Besides using these images for dose reconstruction, using patient MV CBCT images in the treatment planning system, as performed on the head phantom described above, would also provide a useful verification. The MV CBCT provides a more accurate representation of the patient on the treatment table. Applying the treatment plan to the MV CBCT would provide a first estimate of the dose delivered to the patient during treatment. The effects of modified patient position or anatomy could be evaluated. However, the beam delivery itself could not be verified without a full dose reconstruction based on measurements of the treatment beam.
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Calibration of EPIDs for exit-plane dose
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Besides the patient photon attenuation data, the other necessary piece of information for dose reconstruction is the treatment beam energy fluence derived from the treatment-time portal images (Step 2 of our dose reconstruction procedure). An intermediate step to determining the energy fluence is to convert the EPID image to a measurable form of dose, in our case the dose in water measured in the detector plane and at a depth of 1.5 cm [52]. The advantage of first calibrating the EPID against dose in water is that it can be accomplished by experiments since the dose in a water phantom is easily measured. The calibration can then be validated by measurements as well. Moreover, the dose in water can be more easily converted to energy fluence due to the great number of water dose deposition models and algorithms that have already been developed.
To translate the EPID signal to dose in water, we employ convolution models of dose deposition. The lateral spread of the dose in the EPID and in the water is described by empirically derived kernels. Because the EPID consists of millions of individual pixels, the dose deposited in each pixel is also multiplied by a spatially dependent sensitivity factor that accounts for inhomogeneity in the detector response. Finally, comparisons of EPID and ion chamber measurements are used to form conversion tables that translate between the EPID signal and dose in water.
To test the calibration procedure, EPID images of the exit beam were acquired through a Rando anthropomorphic head phantom (The Phantom Laboratory, Salem, NY). The calibrated EPID images were compared with the dose measured using an ion chamber (Scanditronix-Wellhöfer CC13, Bartlett, TN) scanned in a water tank (Scanditronix-Wellhöfer blue phantom, Bartlett, TN). Figure 8 shows a comparison between the measured dose at a depth of 1.5 cm of water and the calibrated EPID signal for a 10 cm square open field. The EPID signal matches the measured dose to within 2% (2 standard deviations) for the in-field regions (excluding the penumbra).
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Figure 8. Comparisons of measured dose profiles(line) in water and calibrated electronic portal imaging device (EPID) profiles (circle with dot) for a 10 cm square field through a Rando head phantom.
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A dose reconstruction program
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Utilizing some of the work described above, we performed a preliminary version of the dose reconstruction procedure on the treatment of a head and neck patient in our clinic. A MV CBCT image was acquired of the patient set up on the table as for treatment (Step 1A). The same day, portal images were acquired (Step 2A) during the patient's normal course of treatment (6 MV beam, 2 opposed lateral wedged fields and an anterior–inferior oblique open field). To utilize the MV CBCT image in the dose reconstruction program, it must first be converted to effective photon attenuation coefficient (Step 1B). For this test case, the MV CBCT was converted to attenuation coefficient using a spatially dependent calibration that utilizes the kV CT patient image as a reference. This allowed us to reduce the effects of the MV CBCT calibration on the reconstructed dose, thus highlighting the dosimetric impact of the remaining steps of the procedure.
To convert the portal images to energy fluence (Step 2B), the portal images were first converted to equivalent dose in water using the calibration procedure described above. To infer the energy fluence at the detector plane from the equivalent dose in water, we used an in-house dose calculation program that predicts the dose at a depth of 1.5 cm of water given the energy fluence at the water surface. This energy fluence is then iteratively corrected until the predicted dose matches the measured dose. To calculate the dose in water, we used convolution kernels published in the literature [53], derived using Monte Carlo calculations and assuming a 6 MV spectrum. The energy fluence that is derived using this method is composed of both primary beam as well as radiation scattered from the patient. For this study, the contribution of the scattered radiation was neglected.
The two remaining steps to the dose reconstruction process are (Step 3) the back-projection of the energy fluence measured at the detector plane through the CBCT of the patient and (Step 4) the calculation of the 3D dose distribution delivered to the patient using a dose calculation engine. To perform the back-projection, we utilized the geometric information obtained during calibration of the MV CBCT imaging system (depicted in Figure 3
). The geometric calibration of the system yields a set of projection matrices that map a point in space to a pixel in the detector plane. The projection matrix for each angle accurately accounts for all geometric factors such as sag in the detector or gantry, detector rotation, or variation in the detector to source distance. These projection matrices were used to back-project the energy fluence from the detector plane through the CBCT volume while correcting for 1/r2 fall-off and the attenuation of each intersected voxel.
The final step of the reconstruction procedure is to calculate the dose deposited in the patient from the energy fluence and the attenuation coefficient for each voxel. The total energy released in each voxel that interacts with the beam is proportional to the energy fluence multiplied by the attenuation coefficient. The spatial distribution of the deposited energy can then be described using a kernel. The kernels we used for this purpose were the same kernels used to determine the energy fluence at the detector plane from the equivalent dose in water. The application of the kernels to calculate the dose was performed using in-house software utilizing the collapsed-cone superposition method [53]. In this method, the energy deposition calculation is only performed along a set of rays emanating from each interaction voxel.
Figure 9 shows the comparison between the planned dose distribution found using the patient kV CT image and a commercial treatment planning system (Philips Pinnacle, Bothell, WA) and the reconstructed dose distributions found using the MV CBCT, the treatment-time portal images, and the in-house dose reconstruction program. There are some qualitative similarities, but also some marked differences. The reconstructed dose distribution appears to be approximately 10% higher than the dose predicted by the planning system. It is likely that this is in part due to an increase in the portal image signal from the scattered radiation that was not corrected in this preliminary version of the dose reconstruction. There also appears to be a slight difference in the alignment of the beams detected by the portal images. The doses from the treatment planning system suggest a slight gap between the opposed lateral fields and the anterior field. In contrast, the reconstructed dose distribution has a high dose band at the intersection of the fields. Without further verification, it is not clear whether this slight difference in field alignment was a real event detected using the treatment-time portal images. Other possible causes for the differences in the two dose distributions include differences in the dose calculation engines, differences in patient position or anatomy in the two images, as well as persistent cupping artefacts in the MV CBCT.
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Figure 9. Comparisons between planned isodose contours calculated using the patient kV CT image and a commercial treatment planning system(left) and reconstructed isodose contours calculated using the megavoltage cone beam CT (MV CBCT), the treatment-time portal images, and an in-house dose reconstruction program (right).
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As the above example demonstrates, much research remains to be done to increase the dosimetric accuracy of our dose reconstruction program. Currently, we continue to work toward simple but effective techniques to reduce cupping artefacts in the MV CBCT images and to calibrate the MV CBCT for photon attenuation coefficient. We also continue to refine our EPID dosimetric calibration models described above and to improve the conversion of the EPID signal to primary energy fluence. One of the remaining challenges is to implement a correction for the scatter contribution in the portal images. Portal image scatter correction has been investigated by other researchers, and some good results have been reported using a scatter-to-primary ratio model and Monte Carlo-based scatter kernels [54–56]. Finally, once the individual steps of the dose reconstruction procedure have been optimized, the dosimetric accuracy of the full procedure will need to be determined using dose measurements in phantoms. As discussed below, the dosimetric accuracy achieved will affect the clinical application of the dose reconstruction procedure.
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Future directions in dose-guided therapy research
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This article has summarized the work performed as well as the challenges remaining to develop a dose reconstruction procedure based on MV CBCT images of the patient on the treatment table and treatment-time portal images. As described earlier, the ability to reconstruct the delivered patient dose opens up the possibility of adapting the patient treatment plan to improve dose delivery. The accuracy of the dose reconstruction procedure and the availability of image processing tools will affect how treatment may be guided using this new dose information. Our initial goal is to achieve 5% accuracy for the reconstructed patient dose. With this level of accuracy, gross dosimetric errors, which have been demonstrated to be as high as 40% in cases of considerable patient weight loss [10], could be detected and corrected. Implementation of more complex dose-guidance strategies, such as scenarios 2 and 3 discussed earlier, will require increased dosimetric accuracy as well as the ability to precisely locate the dose distribution in terms of critical structures. It is here that the rapidly advancing field of 3D image processing will play a key role. Tools such as automated segmentation and 3D deformable registration increase our ability to determine under or over dosed regions as well as track the cumulative dose to various organs in the patient.
By focusing on the key parameter determining radiation treatment outcomes, dose verification and dose-guided therapy have the potential to considerably improve the treatment of cancer. Moreover, they offer the opportunity to increase our understanding of treatment effectiveness, improving our knowledge of the radiation doses and distributions that lead to the control of cancer or the injury of normal structures. Although this level of precision has long been a goal in radiation oncology, the continuing advances in imaging technology and in imaging processing may soon make this goal attainable.
This research was supported by Siemens Oncology Care Systems.
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Acknowledgments
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The authors would like to acknowledge the following persons for their valuable contributions, enlightening discussions and active participation on the acquisition of the clinical cone-beam images. At UCSF, Albert Chan, Chris Malfatti, Amy Gillis, Ping Xia, Lynn Verhey. And at Siemens OCS, Ali Bani-Hashemi. This research was supported by Siemens Oncology Care Systems (OCS). One of the authors (OM) wishes to acknowledge a doctoral scholarship from NSERC-Canada.
Received for publication June 30, 2005.
Revision received August 8, 2005.
Accepted for publication September 7, 2005.
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Abstract
The rationale for adaptive...
