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Technical considerations for implementation of x-ray CT polymer gel dosimetry
This article has been downloaded from IOPscience. Please scroll down to see the full text article. 2005 Phys. Med. Biol. 50 1727 (http://iopscience.iop.org/0031-9155/50/8/008) View the table of contents for this issue, or go to the journal homepage for more
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INSTITUTE OF PHYSICS PUBLISHING Phys. Med. Biol. 50 (2005) 1727–1745
PHYSICS IN MEDICINE AND BIOLOGY
doi:10.1088/0031-9155/50/8/008
Technical considerations for implementation of x-ray CT polymer gel dosimetry M Hilts1,2, A Jirasek3 and C Duzenli2,4 1 Medical Physics, BC Cancer Agency—Vancouver Island Centre, Victoria, BC V8R 6V5, Canada 2 Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z1, Canada 3 Department of Physics and Astronomy, University of Victoria, Victoria, BC V8W 3P6, Canada 4 Medical Physics, BC Cancer Agency—Vancouver Centre, Vancouver, BC V6R 2B6, Canada
E-mail:
[email protected]
Received 30 September 2004, in final form 3 March 2005 Published 6 April 2005 Online at stacks.iop.org/PMB/50/1727 Abstract Gel dosimetry is the most promising 3D dosimetry technique in current radiation therapy practice. X-ray CT has been shown to be a feasible method of reading out polymer gel dosimeters and, with the high accessibility of CT scanners to cancer hospitals, presents an exciting possibility for clinical implementation of gel dosimetry. In this study we report on technical considerations for implementation of x-ray CT polymer gel dosimetry. Specifically phantom design, CT imaging methods, imaging time requirements and gel dose response are investigated. Where possible, recommendations are made for optimizing parameters to enhance system performance. The dose resolution achievable with an optimized system is calculated given voxel size and imaging time constraints. Results are compared with MRI and optical CT polymer gel dosimetry results available in the literature. (Some figures in this article are in colour only in the electronic version)
1. Introduction Gel dosimetry is one of the most promising tools to fill the 3D dose verification void in modern radiation therapy practice and is currently the focus of international research efforts. One main focus of gel dosimetry research is the development of the gels themselves. Work in this area is currently ongoing for two main types of gels: Fricke and polymer gels. Fricke gels are based on the well-known Fricke chemical dosimeter but suffer from spatial instability of the dose distribution. Polymer gels are less well understood but more spatially stable systems based on the radiation-induced polymerization of monomers infused in a gel matrix. Detail on development of these gels may be found in the proceedings of the recent international 0031-9155/05/081727+19$30.00 © 2005 IOP Publishing Ltd Printed in the UK
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conferences on gel dosimetry (DOSGEL) (Schreiner and Audet 1999, Baldock and De Deene 2001, De Deene and Baldock 2004). This paper focuses on another active area of gel dosimetry research, the development of techniques to read out the dose information from gels. Magnetic resonance imaging (MRI) was the first method of imaging gel dosimeters (Gore et al 1984) and is still the most commonly used modality for imaging polymer gel dosimeters. Some excellent results have been achieved using MRI, however, MRI is expensive, relatively inaccessible, prone to imaging artefacts (De Deene et al 2000a, 2000b, Berg et al 2001, Lepage et al 2001a, Watanabe et al 2002, Hurley et al 2003) and highly sensitive to imaging temperature (Maryanski et al 1994, 1997, De Deene and De Wagter 2001). As a result, alternate read-out methods are being investigated, including optical computed tomography (OCT) (Gore et al 1996, Maryanski et al 1996), Raman spectroscopy (Baldock et al 1998), x-ray computed tomography (CT) (Hilts et al 2000) and ultrasound (Mather et al 2002). Each gel formulation and read-out technique has advantages and disadvantages over others, and current efforts are focused on developing gel dosimetry systems (gel combined with imaging modality) that will be capable of providing practical, clinical 3D dosimetry solutions. Due largely to the accessibility and relatively low cost of CT imaging for cancer hospitals, interest in CT as a read-out tool for clinical gel dosimetry is increasing (Audet et al 2002). The focus of the current paper is to outline considerations for implementing CT polymer gel dosimetry. Specifically phantom design, CT imaging protocol, image uniformity, imaging time and gel response are investigated and, where possible, optimization strategies are provided. Achievable dose resolution is calculated for optimized CT gel dosimetry given voxel size and imaging time constraints. These results are compared with those presented in recent MRI and OCT gel dosimetry literature. Results indicate that an optimized CT polymer gel dosimetry technique has many attractive features for a clinical 3D dosimetry tool and highlights that the remaining limitation for routine clinical implementation is low dose sensitivity. In summary, this work demonstrates many important considerations for implementation of CT polymer gel dosimetry and provides useful recommendations for system optimization. 2. Materials and methods 2.1. Gel preparation and irradiation All polymer gels used in this work were polyacrylamide gels (PAGs) manufactured in an in-house designed and built glovebox (Jirasek et al 2001). Details of the procedures used to manufacture and irradiate vials of gel are provided elsewhere (Hilts et al 2004). In brief, PAGs were composed of electrophoresis grade acrylamide monomer and N, N methylene bisacrylamide (bis) crosslinker (Sigma-Aldrich, St Louis MO, USA), gelatin (300 Bloom, Sigma-Aldrich) and deionized water. Gel vials were irradiated to uniform doses using 6 MV photons from Varian 21EX and 2100 medical linear accelerators (Varian Medical Systems Inc., Palo Alto, CA, USA). Vials were exposed to oxygen about 15 h post irradiation as polymerization is complete by this time (De Deene et al 2000c, Lepage et al 2001b). CT imaging was performed approximately 24 h later to ensure full oxygenation of the gels (Hepworth et al 1999). 2.2. CT imaging CT imaging for all experiments was performed using a GE HiSpeedCT/i CT scanner (GE Medical Systems, Milwaukee, WI, USA) equipped with a high performance PerformixTM x-ray tube. This CT scanner is a single slice, 3rd generation, rotate–rotate machine and
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Table 1. Scan parameters used to test the effect of scan protocol on image noise. Parameters in italic comprise the reference protocol. Reconstruction algorithm names are standards for GE CT scanners. Scan parameter (units)
Values used
Tube voltage (kV) Tube current (mA) Slice scan time (s) Slice thickness (mm) Field of view (cm2) Pixel dimension (mm) Reconstruction algorithm
80, 100, 120, 140 100, 150, 200, 250, 300, 380 0.8, 1, 2, 3, 4 1, 3, 5, 7, 10 25 × 25 0.5, 1, 1.5, 2, 2.5, 3, 5 Standard, Soft, Lung, Detail, Bone, Edge
is similar to the types of CT scanners currently available in most cancer hospitals. Some measurements contained in this work (such as the effects of CT imaging protocol on image noise and imaging time) will depend on the particular make and model of CT scanner. However, the overall trends presented here will be representative of other single slice machines, and the optimization strategies proposed will remain relevant. The details of CT imaging specific to each investigation are provided below. 2.2.1. Phantom design experiments. The effect of phantom diameter on image noise was investigated by imaging five water filled glass (2 mm Pyrex) phantoms with different diameters, selected as typical sizes for 3D verification of radiation therapy treatments. The importance of phantom material for optimized image quality was investigated by imaging three phantoms of the same dimension (29 × 29 cm2 ) but constructed of different materials: acrylic, solid water and styrofoam. All phantoms, both for the phantom size and the phantom material experiments, were CT imaged using the same protocol, as given (italicized) in table 1. 2.2.2. Imaging protocol experiments. The effects of CT imaging protocol on image noise were studied using a cylindrical water filled phantom. The phantom was ∼12.5 cm in diameter to mimic a typical size for a gel dosimetry phantom. The dependence of image noise on each available CT imaging parameter was measured individually. This was achieved by obtaining images of the water phantom using scan protocols that independently varied each scan parameter listed in table 1 from a reference protocol (italicized in table 1). All imaging used the smallest available field of view (25 × 25 cm2 ) and pixel size was varied post-imaging using MatLab (The Math Works Inc., Natick MA, USA) as described in section 2.3. Note that for each set of scan parameters two images were obtained in order to remove artefacts by background subtraction prior to making noise measurements. 2.2.3. Imaging time experiments. Imaging time was measured by recording the average time required to acquire a final image of a single slice, including image averaging. For multi-slice imaging, images of several slices were obtained sequentially and the average time required to obtain a single slice determined. This was done in order to include the effect of x-ray tube heating on imaging time. The effect of imaging protocol on this imaging time was determined by varying the tube current and number of averages in order to achieve different loads on the x-ray tube. Determining imaging time per slice in this manner allows for easy calculation of the total time required to scan a volume of polymer gel.
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2.2.4. Dose response experiments. The same CT imaging protocol: 120 kV, 200 mAs, 25 × 25 cm2 field of view, 1 cm slice thickness and the Standard reconstruction algorithm was used for all dose response experiments. A detailed description of the technique used for CT imaging gel vials is provided elsewhere (Hilts et al 2004). In brief, all vials for each batch of gel were imaged simultaneously in a styrofoam phantom designed to provide optimized signal-to-noise ratio (SNR). Background subtraction was used to remove artefacts and 16 images were averaged to further increase image SNR. 2.3. Image processing and analysis Image averaging, background subtraction and all other image analyses were performed using MatLab. Background subtraction was particularly critical to ensure removal of artefacts that might obscure noise measurements. For studying the effect of phantom size, CT imaging technique and voxel size on image noise, the standard deviation in CT number σNCT was extracted from identical 50 × 50 pixel regions of interest (ROIs) at the centre of the images. For investigating the effect of phantom material on σNCT , noise measurements were made for ten 21 × 21 pixel ROIs, identically positioned in the images of all three (acrylic, solid water and styrofoam) phantoms. This was done in order to simulate the relative σNCT extracted from gel vials when these materials are used to house the vials during imaging. Uniformity of CT imaging was tested by extracting mean CT number (NCT ) and σNCT from 36, 25 × 25 pixel ROIs in the same phantom used for the CT imaging protocol experiments. For the gel dose response studies, the values of NCT were extracted from the centre of each vial (21 × 21 pixel ROIs) in the final averaged and background subtracted images. As stated above (section 2.2) all CT imaging was performed at the smallest available field of view, 25 × 25 cm2 . This provides a pixel dimension of ∼0.5 mm. The effect of pixel dimension on image noise was examined by increasing pixel dimension up to 5 mm, as given in table 1, post-imaging using MatLab. Noise measurements were obtained from each image, as above, by extracting σNCT from identical 2.5 × 2.5 cm2 ROIs at the centre of the images. 3. Results and discussion 3.1. Phantom design considerations 3.1.1. Phantom material. Phantom material affects both image noise and artefacts. The effect of phantom material on image noise is particularly dramatic when imaging gel vials, as may be done to obtain a dose response or calibration curve. This is demonstrated in figure 1 which shows a >11 times reduction in image noise achieved using a styrofoam instead of water equivalent plastic or acrylic phantomfor housing gel vials during CT imaging. This noise results in uncertainty in the vial reading σNCT , a factor that contributes to reduced dose resolution in clinical gel dosimetry (Trapp et al 2004). Consequently, noise reduction in gel vial measurements is a concern in clinical implementation of CT gel dosimetry. The effect of phantom material on image noise for larger phantoms as would be used for measuring clinical dose distributions will not be as significant. However, in these cases use of high density phantom materials can be a concern due to pronounced beam hardening artefacts which are difficult to remove by background subtraction and may obscure dose information. As shown in figure 2(a), this is particularly important for phantoms that are not symmetric within the scanner bore. This includes rubber stoppers which should be removed during imaging: the image in figure 2(b) has dose information obscured by an artefact that is clearly removed by imaging the gel without the rubber stopper, figure 2(c).
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Figure 1. Effect of material housing gel vials on the uncertainty in measurement of vial NCT (σNCT ). This uncertainty affects the precision of dose response curves extracted from these vial readings.
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Figure 2. Examples of artefacts caused by high density materials used in phantom construction. A glass flask (a) containing a proton irradiated beam (centre) shows dramatic artefacts (white) due to photon attenuation in the flask neck. An artefact due to a rubber stopper is denoted by the white arrow in (b). The artefact reduction achieved by removing the rubber stopper in (b) prior to imaging is shown (white arrow) in (c).
3.1.2. Phantom size. Phantom size also has a dramatic effect on image noise. Increasing phantom diameter is found to increase σNCT exponentially, as is demonstrated in figure 3. This is the result of increased photon attenuation by larger phantoms and the ensuing reduction in the number of photons counted (N) and the increase in quantum noise (Hsieh 2003). Since image noise directly impacts dose resolution, CT gel dosimetry phantoms should be designed so that the imaged phantom is as small as practical for a given application. Due to the exponential response, even small reductions in phantom size provide a measurable improvement in image
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Figure 3. The effect of phantom size on CT image noise. The fit is exponential.
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Figure 4. Spatial uniformity in CT images as measured using 36 spatially distinct 25 × 25 pixel ROIs in a single image (a) and 50 averaged images (b). The locations of the ROIs within the phantom are shown schematically in (b). Background subtraction was applied in both cases. Error bars represent the standard deviation on the mean for each ROI.
noise. In light of this fact, an optimum gel system could be designed so that the gel is irradiated in a larger phantom but is CT imaged alone, removed from this larger phantom, in order to reduce CT image noise. 3.2. CT imaging considerations 3.2.1. Image uniformity. The ability of a gel dosimetry read-out technique to produce spatially uniform images is critical for accurate gel dosimetry as non-uniformity in an image could be falsely interpreted as inhomogeneity in the recorded dose distribution. Figure 4(a) shows the mean and standard deviation for 36 spatially distinct 25× 25 pixel ROIs in a single, background subtracted CT image of a water filled phantom. The reference CT scanning protocol (table 1) was used. It is clear that the variation between ROIs (standard deviation of 0.14 H) is far less than the uncertainty within each ROI (average standard deviation of
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Figure 5. Effects of scan technique (x-ray tube voltage, current and slice scan time) on CT image noise. Note that different x-axis are provided for the three parameters.
3.65 H). In order to ensure that this uniformity holds after application of image averaging to reduce image noise, the test was repeated after employing 50 image averages. The results, shown in figure 4(b), indicate that even with the low noise present in a large number of averaged images, the uniformity remains better than the intra-ROI variation as the standard deviation between the ROI means is only 0.03 H. This excellent uniformity makes x-ray CT an attractive alternative to MRI and OCT gel imaging techniques which exhibit problems with image non-uniformity (De Deene et al 2000a, 2000b, Oldham and Kim 2004). 3.2.2. Technique parameters. CT imaging technique affects image noise levels. Increasing x-ray tube voltage x-ray tube current (mA) and slice scan time (s) all result in a decrease (kV), in image noise σNCT as is shown in figure 5. Best fits to the data, provided in figure 5, indicate that increasing tube current and slice scan time reduce image noise by (mA)−0.57 and (s)−0.47 , respectively. Since these parameters linearly increase the number of√photons incident on the phantom, these results are quite consistent with the theoretical 1/ N reduction in image noise based on photon counting statistics (Hsieh 2003). Note that this relationship to image noise is identical to that of image averaging, consistently used in CT gel dosimetry as an additional noise √ reduction tool (Hilts et al 2000, Trapp et al 2001). Image averaging reduces noise by 1/ NAX, where NAX is the number of averages (until an electronic noise limit is reached with a very high number of averages (Trapp et al 2001)). The relationship between tube voltage and image noise is however more complex. The results shown in figure 5 indicate that increasing tube voltage decreases image noise by ∼(kV)−1.4 . This means that a change in tube voltage has a greater effect on image noise than does an equal change in tube current or slice scan time. For example, doubling tube voltage would produce an expected ∼60% reduction in image noise whereas doubling tube current only an ∼30% reduction.
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Figure 6. Image noise measured at a range of x-ray tube loads achieved by changing each of tube voltage, current and slice scan time individually from the standard set of reference parameters (given in table 1). The curve obtained by changing tube voltage is clearly much steeper than that obtained by changing current or slice scan time. This indicates that increasing tube voltage is the most efficient means of lowering image noise through choice of scan protocol.
The load on an x-ray tube (kJ) is related to imaging technique and the number of images obtained: kJ = kV × mA × s × NAX.
(1)
Given the results presented here, this means that increasing tube voltage is a more efficient means of reducing image noise than increasing tube current or slice scan time. This is emphasized in figure 6 which shows image noise measured for a range of tube loads obtained by independently varying each of tube voltage, current and slice scan time from the reference values given in table 1. In addition, from equation (1), the efficiency of noise reduction by image averaging is on par with tube current and slice scan time but less than tube voltage. The implication is that maximizing tube voltage should be the priority when optimizing CT gel dosimetry for low noise. For further noise reduction post-imaging, digital image filtering should be considered (Hilts and Duzenli 2004). 3.2.3. Reconstruction algorithm. Several reconstruction algorithms are available on CT scanners in order to allow the user to highlight features of interest in the images. These algorithms frequently employ filters to, for example, enhance contrast or detail in the images. As a result, as shown in figure 7, an enormous variation in image noise is obtained by simply changing reconstruction algorithm. The algorithms tested here are those available on the GE HiSpeed CT/i scanner (see table 1). Algorithm details are proprietary and specific algorithms will differ with scanner manufacturer. However, a similar relative variation can be expected for other scanners and the general conclusions drawn here will remain relevant. These results demonstrate that reconstruction algorithm has the greatest impact on image noise of any scan technique parameter and, as such, choice of an appropriate reconstruction algorithm is critical for optimized CT gel dosimetry. The low contrast resolution algorithm (e.g. SOFT by GE) provides the lowest noise and this algorithm is recommended for most gel dosimetry applications. However, since these algorithms typically employ a light smoothing operation, if recording very high spatial detail is more critical than high dose resolution, a standard
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Figure 7. The effect of choice of reconstruction algorithm on noise in the resulting CT image. The reconstruction algorithms tested are standards for GE CT scanners.
algorithm (e.g. STANDARD by GE) is recommended. Algorithms specifically designed to highlight edges and detail should be avoided. 3.2.4. Slice thickness and pixel size. CT imaging can provide images with very small voxel dimensions. Scanning with a small (25 × 25 cm2 ) field of view, the 512 × 512 matrix size of CT images provides images with a pixel dimension of less than 0.5 mm. In the out of image plane dimension (slice separation), traditional single slice CT scanners can typically provide 1 mm resolution, and some new multi-slice machines can do even better. Small voxel size in CT imaging does however present an inherent compromise with image noise. This is due to decreased photon counting statistics with decreased voxel size and applies to both pixel size within the image plane (x, y) and, when slice thickness equals slice separation (as is typical in CT imaging), the distance between image planes (z). The measured effects of both pixel dimension and slice thickness on image noise are shown in figure 8. The relationship between image noise and slice thickness (h) is found to expected to increase linearly with slice thickness, be σNCT ∝ h−0.5 . Given all else equal, N is √ and this measured result agrees with the 1/ N reduction in image noise predicted by theory. The effect of pixel dimension on noise is measured to be greater than that of slice thickness and, as is shown in figure 8, is described well by a mono-exponential (R 2 = 0.9932). There is a disagreement in the literature as to the predicted behaviour of image noise with pixel √ dimension (w) in CT imaging. Most authors suggest a 1/ w 3 relationship with image noise (Brooks and Di Chiro 1976, Barrett et al 1976, Goodsitt and Johnson √ 1992). However, others ∝ 1/ w (Goodenough 2000). suggest that a smaller effect is to be expected, such as σ NCT √ When fit to this functional type σNCT ∝ 1 w x , this study indicates a relationship midway √ between these predictions: σNCT ∝ w 1.3 . However, the fit to this function (R 2 = 0.9361) is not nearly as good as the exponential fit that is shown in figure 8. Consequently, the effect of pixel dimension on image noise should be considered exponential for gel dosimetry, similar to the effect of phantom size. These results have valuable implications for optimum clinical implementation of CT gel dosimetry since in many situations a 0.5 mm voxel dimension may prove unnecessary. This would occur, for example, when using gel dosimetry to validate treatment planning
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Figure 8. The effects of CT image voxel dimensions on image noise. The in-plane dimension (pixel size) has a greater effect on image noise than does the dimension between image planes (slice thickness and separation). Table 2. A summary of the measured quantitative effects of phantom size, CT scanning technique factors and voxel size on CT image noise. Factor affecting image noise (symbol)
Relationship to image noise (σNCT )
Phantom diameter (d) Tube voltage (kV) Tube current (mA) Slice scan time (s) Number of averages (NAX) Pixel dimension (w) Slice thickness (h)
σNCT σNCT σNCT σNCT σNCT σNCT σNCT
∝ ed ∝ (kV)−1.4 ∝ (mA)−0.5 ∝ s−0.5 ∝ (NAX)−0.5 ∝ ew (or w −0.65 ) ∝ h−0.5
calculations performed on a calculation grid that has a 1.25 or 2.5 mm resolution. As these results have shown, image noise can be improved by ∼60 % by increasing the voxel dimension to 2 mm. This demonstrates, quantitatively, the compromise between voxel size and image noise in CT gel dosimetry and highlights the importance of optimizing voxel size based on the requirements of a given application. 3.2.5. Prediction of image noise. For ease of reference, the quantitative effects on image noise of all CT imaging factors investigated here, in addition to phantom size (section 3.2) are summarized in table 2. The effect of image averaging on image noise is also included for comparison purposes. An exciting result for implementation of CT gel dosimetry is that all the parameters listed in table 1 affect image noise independently of one another. This was demonstrated by varying all parameters in pairs, measuring the resulting image noise and comparing these results to calculated predictions based on the relationships summarized in table 2. Note that all measurements are made after performing a background subtraction. Table 3 provides several examples using low, medium and high noise imaging techniques. The
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Table 3. From the results in table 2, the percentage noise relative to the reference protocol (Rel. σNCT ) can be derived for any imaging protocol. Given a noise measurement (Meas. σNCT ) for the reference protocol, image noise can then be calculated for any CT imaging protocol (Calc. σNCT ). Examples for low, average and high noise protocols are provided. Imaging protocol
Non-ref. parameters
Reference – Low noise 300 mA, 10 mm, 4 s, SOFT Average noise 120 kV, 250 mA, 5 mm, 2 s High noise 100 mA, 1 mm, 0.8 s, DETAIL
Meas. σNCT (H)
Rel. σNCT (%)
Calc. σNCT (H)
Diff. (H)
3.64 0.87 2.53 32.24
100 19 68 878
3.64 0.69 2.49 32.00
– 0.18 0.04 0.24
results show that measured and predicted image noise agree very well and demonstrate that, after measuring image noise for a reference imaging protocol, image noise can be predicted for any other imaging protocol. The practical implication is that, given a known gel CT dose response, this feature allows for selection of a particular CT imaging protocol in order to meet specific dose resolution requirements. 3.3. Time considerations Time requirements for performing polymer gel dosimetry include times for gel irradiation, imaging and analysis as well as time for gel manufacture if gels are made in-house. With the exception of imaging time, these requirements will be the same regardless of imaging modality and consequently this work studies the time required to CT image polymer gel. Note that imaging time as discussed here is the time required to do the actual scanning and the total time to image a gel would also include set-up time. For CT imaging, the required scanner warm-up typically takes ∼5 min and positioning of the gel phantom (using lasers) takes
