Journal of Cancer Research and Therapeutics

: 2018  |  Volume : 14  |  Issue : 2  |  Page : 278--286

Dosimetric verification of small fields in the lung using lung-equivalent polymer gel and Monte Carlo simulation

Nahideh Gharehaghaji1, Habib Alah Dadgar2,  
1 Department of Paramedical, Tabriz Medical School, Tabriz, Iran
2 Department of Medical Physics, RAZAVI Hospital, Mashhad, Iran

Correspondence Address:
Habib Alah Dadgar
Department of Medical Physics, RAZAVI Hospital, Mashhad


Purpose: The main purpose of this study was evaluate a polymer-gel-dosimeter (PGD) for three-dimensional verification of dose distributions in the lung that is called lung-equivalent gel (LEG) and then to compare its result with Monte Carlo (MC) method. Materials and Methods: In the present study, to achieve a lung density for PGD, gel is beaten until foam is obtained, and then sodium dodecyl sulfate is added as a surfactant to increase the surface tension of the gel. The foam gel was irradiated with 1 cm × 1 cm field size in the 6 MV photon beams of ONCOR SIEMENS LINAC, along the central axis of the gel. The LEG was then scanned on a 1.5 Tesla magnetic resonance imaging scanner after irradiation using a multiple-spin echo sequence. Least-square fitting the pixel values from 32 consecutive images using a single exponential decay function derived the R2 relaxation rates. Moreover, 6 and 18 MV photon beams of ONCOR SIEMENS LINAC are simulated using MCNPX MC Code. The MC model is used to calculate the depth dose water and low-density water resembling the soft tissue and lung, respectively. Results: Percentages of dose reduction in the lung region relative to homogeneous phantom for 6 MV photon beam were 44.6%, 39%, 13%, and 7% for 0.5 cm × 0.5 cm, 1 cm × 1 cm, 2 cm × 2 cm, and 3 cm × 3 cm fields, respectively. For 18 MV photon beam, the results were found to be 82%, 69%, 46%, and 25.8% for the same field sizes, respectively. Preliminary results show good agreement between depth dose measured with the LEG and the depth dose calculated using MCNP code. Conclusion: Our study showed that the dose reduction with small fields in the lung was very high. Thus, inaccurate prediction of absorbed dose inside the lung and also lung/soft-tissue interfaces with small photon beams may lead to critical consequences for treatment outcome.

How to cite this article:
Gharehaghaji N, Dadgar HA. Dosimetric verification of small fields in the lung using lung-equivalent polymer gel and Monte Carlo simulation.J Can Res Ther 2018;14:278-286

How to cite this URL:
Gharehaghaji N, Dadgar HA. Dosimetric verification of small fields in the lung using lung-equivalent polymer gel and Monte Carlo simulation. J Can Res Ther [serial online] 2018 [cited 2020 Oct 1 ];14:278-286
Available from:

Full Text


Radiotherapy is the use of ionizing radiation in the treatment of cancer. Reduction of received dose to healthy organs is the significant process in radiotherapy treatment. It has led to the expansion of modern radiation therapy techniques such as intensity-modulated radiotherapy (IMRT) and stereotactic radiosurgery.[1],[2],[3],[4],[5] Dosimetry in the small field irradiation in low-density regions can be complex and sophisticated due to the existence of electronic disequilibrium. On the other hand, the range of electrons is inversely proportional to the density of the medium, especially for fields smaller than about 4 cm × 4 cm that introduced as small fields in radiotherapy. Lateral electronic equilibrium that exists along the central axis of the beam can be affected to calculate absorbed dose in the low-density materials.

Gel dosimeters are tissue-equivalent materials, energy independent, and also as three-dimensional (3D) dosimeters.[6],[7] Therefore, gel dosimeters is the best choice for the evaluation of dose absorbed in homogeneous media where there is electronic equilibrium. Although several lung-equivalent gel (LEG) dosimeters have been developed, there are only few reports available for evaluating the 3D-dose distributions in the heterogeneous regions such as lung tissue.[8],[9],[10] Olberg et al. used lung-equivalent dosimeter based Fricke gel but that gel was denser than the lung tissue.[11] Hence, investigations are extended to commence for receiving lower-density materials. LEGs according to the lung density are used; however, the density of the dosimeter should be reduced.[12],[13],[14],[15],[16] Authors have proposed two techniques for reducing the density of polymer gels: (1) Adding Styrofoam beads (2) using high-speed magnetic stirrer or home mixtures in the gel until the foam is observed. In the present study, we added sodium dodecyl sulfate (SDS) as a surfactant to increase the surface tension of the gel. During fabrication, the LEG, temperature, and the time of mixing are significant on the resulting density and homogeneity of the gel. In short, the foam gel will have a density between 0.25 and 0.35 kg/dm3.

 Materials and Methods

Gel fabrication

The foam gel was fabricated using described prior procedure with following components.[9] Gelatin (300 Bloom), methacrylic acid (MAC), SDS, and Bis[tetrakis (hydroxymethyl) phosphonium] sulfate (THPS) were purchased from Sigma-Aldrich Corporation was an American multinational chemical, life science and biotechnology company [Figure 1].{Figure 1}

Gelatin (12 g) was used to compensate the loss of gel strength due to the addition of MAC at higher temperature. SDS was used as a surfactant for obtaining the lower density near the density of the lung tissue. To remove effects of oxygen during the polymerization, 10 mM of THPS was added. In constructing this foam gel, first, the gelatin (12 g) was dissolved in half of the total amount of water at room temperature (approximately 23°C). After 15 min, the gelatin will be swelled, and then gelatin solution was heated to 45°C. Temperature is increased so as to jellify the gel. MAC was dissolved by more than half of the remain water. Meanwhile, SDS solution was made with the lower remaining part of the water. The gelatin solution is then cooled down to 35°C and then it becomes very viscous. In the continuing part, the MAC solution is added. The solution was beaten using a household mixer to make it homogeneous. The SDS solution is then added. The gel was beaten for 3 min till creamy foam liquid was obtained. Then, the THPS solution was added. Finally, gel foam was poured into the recipient and then taken in the lung phantom for irradiation [Figure 2].{Figure 2}


All gel foam samples were irradiated with photon beams from a linear accelerator (ONCOR SIEMENS) equipped with a multi-leaf collimator. The foam gel vials were irradiated with 1 cm × 1 cm field size, 6 MV photon beams and 3, 6, 9, and 15 Gy with a source-to-surface distance (SSD) of 100 cm. The calibration vial was irradiated with a 10 cm × 10 cm 6 MV photon beam.

In this work, we assumed that the dose absorbed in the calibration vial is close to the dose that would be absorbed in water [Figure 3].{Figure 3}


For measurement of the polymerization and dose distribution in gels, imaging techniques have emerged as noninvasive methods including magnetic resonance imaging (MRI), optical and X-ray computed tomography, Raman spectroscopy, and ultrasound.[15],[16],[17],[18],[19],[20],[21],[22],[23],[24],[25],[26],[27] Additionally, regarding the application of gel dosimeters, various formulations of the gel can be used. As polymer gels are sensitive to oxygen, so this parameter is caused drawback for construction with decreasing the sensitivity of the gel through the polymerization. MRI is a popular technique commonly used in the measurement of gel dosimeters.[5],[28],[29],[30],[31],[32],[33] In general, 1.5 Tesla strength magnetic fields are used in the clinical systems with a half a millimeter resolution. Therefore, an increased image resolution was required for polymer gels irradiated using very small fields. In addition, with increasing the magnetic fields, R2 measurements are decreased.[5] The values of the spin–spin relaxation time (T2) and R2 (1/T2) are dependent on dose, and can be obtained using echo images in the MRI scanning. The polymerization of a gel dosimeter causes a reduction in the spin-lattice relaxation time (T1) and T2.[16],[34] T2 provide better images with higher signal-to-noise ratios than T1.[15]

Multiple spin echo sequence was investigated in constructing images that can be converted to absorbed dose maps. For this scanner and imaging sequence type, maximum number of echoes (32) was employed. After irradiation, the containers were scanned inside an RF head coil using a 1.5T GE MRI machine. To obtain the transverse relaxation rate values, the pixels values of a set of MRI all obtained at same position were fitted to a single exponential function given by:

Y (t) = A × exp (t0– R2 × t) + B

where A, B, and t0 are constants, t is the time, and R2 the transverse relaxation rate. The exponential functions of each set are linked by A, B, and R2 values during the fitting; the four functions should have the same values. The other constant t0 was not linked during the process of improving the fitting quality, as the images belong to different sets. For analyzing the images of MRI with DICOM format to obtain signal intensity of images, we applied PolyGeVero software (GeVero Co., Lodz, Poland,[35] Dose profiles and R2 plots were obtained using PolyGeVero software that has been developed for fast and easy calculations of 3D radiotherapy dosimetry data. For obtaining data processing of this software, 32 DICOM images of MRI scanning enter the PolyGeVero and then processing has commenced for those images. The region of interest (ROI) was chosen for any vial and the same ROI is applied for whole images. On the other hand, signal intensity is achieved for each ROI for different echo times. Moreover, signal intensity plots unit age of different echo times mapped for any vials. With depicting of T2 curves-related signal intensity with echo times for irradiated samples with different doses, R2 scales for any sample were obtained.

Monte Carlo simulations

A reliable technique to the small field dosimetry is the use of Monte Carlo (MC) calculation. These models overcome many of the limitations of treatment planning algorithms. MC simulates the interactions of particles that happened in a linear accelerator and are not widely adopted in clinical applications because of the high consuming time. Each particle interacts with the geometry of the system (the linear accelerator head and the phantom). Therefore, factors such as the total absorbed energy can be calculated. MC methods provide potential for more accurate calculation of inhomogeneity media. The MCNP × 2.4.0 MC code was applied for dose calculations. We used a validated MC model of Varian 2100EX model for our dose calculations in the lung and water phantoms [Figure 4].{Figure 4}

The components of a linear accelerator for 6 and 18 MV photon beams are shown in [Figure 4].

The multi-leaf collimator with 80 leafs was molded with thickness of 6.1 cm. The projected width of every leaf was 1 cm in the isocentric plane. A phase-space (PS) file was generated above the secondary collimator with running 108 primary particles for both energies and then this PS was used for dose calculations with different field sizes for 6 and 18 MV photon beams. The multi-leaf collimator with 80 leafs was molded to create small fields used as beamlets in IMRT. The round front end of leafs was modeled as inclined plane surfaces according to beam divergence. In running PS file, the secondary collimator, MLC, and phantom were simulated and the absorbed dose was scored inside scoring cells filled with water. Water and lung phantoms with dimension of 21 cm × 30 cm × 30 cm were simulated under treatment head with SSD of 100 cm. The percentage depth doses (PDDs) for 0.5 cm × 0.5 cm, 1 cm × 1 cm, 2 cm × 2 cm and 3 cm × 3 cm field sizes were calculated using MC method for homogeneous water and inhomogeneous phantom containing the lung. The photon and electron low-energy cutoffs were 10 and 500 keV, respectively. For definition of soft tissue water with 1.08 g/cm3 density was used. For lung material, we used water with 0.25 g/cm3 density. The *F8 tally was used for dose calculations in water and lung phantom.


In [Figure 5] and [Figure 6], the effect of water–lung interface is apparent, while its dosimetric magnitude and spatial spread differ considerably for different photon energies and field sizes. In other words, the dose begins to drop off at the water–lung interface and decreases steadily with depth inside lung, then at the second water–lung interface the dose builds up and reaches its second maximum in water. However, this pattern of dose variation with depth becomes very pronounced with decreasing the field size. Otherwise, the dose beyond the lung increases significantly compared with homogeneous water phantom. [Figure 5] illustrates the depth dose variation with depth for homogeneous water and inhomogeneous lung phantom for 6 MV photon beam. For this geometry, the maximum dose reduction percentage was calculated at the depth of 7 cm, just after the build-down region. The dose reduction percentages in the lung compared with water were obtained as 44%, 39%, 13%, and 7% for 0.5 cm × 0.5 cm, 1 cm × 1 cm, 2 cm × 2 cm, and 3 cm × 3 cm field sizes, respectively. Meanwhile, it was noted that the dose inside the lung is getting similar to water dose in field size of 3 cm × 3 cm. [Figure 6] illustrates the comparison of depth dose calculations for homogeneous water and lung phantoms for 18 MV beam. In all field sizes, there is a sharp decline in the depth dose which starts from water–lung interface and reaches a relatively steady state inside the lung similar to 6 MV beam. The dose reduction percentage was calculated at 10 cm depth for 18 MV beam and there were higher dose reductions in the lung relative to water with 82%, 70%, 46%, and 26% for 0.5 cm × 0.5 cm, 1 cm × 1 cm, 2 cm × 2 cm, and 3 cm × 3 cm field sizes, respectively. For the 18 MV beam, due to the higher range of secondary electrons, the forward electronic disequilibrium becomes more pronounced relative to 6 MV beam and consequently the depth dose reduces considerably for smaller fields sizes. The PDD curves for both energies are shown in [Figure 5] and [Figure 6].{Figure 5}{Figure 6}

Depth dose curves were normalized to dMax and were scaled for inclusion on the same graph. The dose reduction percentage was calculated using the following formula (1) at a given depth for all cases.


Moreover, comparison PDD between LEG and MC simulation for 1 cm × 1 cm small field is shown in [Figure 7].{Figure 7}

Dose maps for gel dosimetry

Dose-R2 plot, which has approximately 0.9953 standard deviation, showed linear correlation between R2 and absorbed dose irradiated by 6 MV photon beams and 3, 6, 9 and 15 Gy [Figure 8].{Figure 8}

[Figure 9] shows the obtained quantitative R2 image from MRI scanning.{Figure 9}

Moreover, dose distribution form LEG irradiations and isolines without R2-map in the LEG are shown in [Figure 10].{Figure 10}

R2 profiles along the longitudinal axis of the beam at around red lines from surface to inside, in the middle (X = −0.3 mm, Y = 50.6 mm, Z = 5.2 mm) and 3D views of R2 for foam gel have the following MRI parameters:

Magnetic field: 1.50, protocol name: t2_se_tra_16-echoes, number of averages: 1.00, image width (pt): 512, image height (pt): 512, pixel size X: 0.20, pixel size Y: 0.20, image orientation: 1.00 0.03 0.00–0.03 1.00 0.01: Range minimum: 0, Range maximum: 6000, value unit: 1/s are shown in [Figure 11], [Figure 12], [Figure 13], respectively.{Figure 11}{Figure 12}{Figure 13}

Definition of profile points from the beginning to the end for obtaining the transverse profiles is shown in [Table 1].{Table 1}

Dose profile approximately in the middle of foam gel (X = 0.9 mm, Y = 51.0 mm, and Z = 5.2 mm) in the PolyGeVero software is shown in [Figure 14].{Figure 14}

The dose profile is found to flatten off at a depth between −33 and 33 mm in the YZ axis and 30–68 mm in the XZ axis. In short, it can be seen that the dose sensitivities for R2 are lower in a gel foam dosimeter than in a corresponding water-density-equivalent gel dosimeter. In addition, the saturation dose is higher in the low-density gel foam dosimeters than in the water-density-equivalent gel dosimeter.


The results of the current study were compatible with results of the previous investigation on the subject of small fields in radiation therapy of lung cancers both experimental and MC studies. Nevertheless, there were small discrepancies pertaining to the amount of dose reduction percentages in the lung as well as the solid tumor inside the lung. We assume these slight differences can be attributed to the differences in MC code used comparing the MCNP with BEAM and EGSnrc. Furthermore, there were small differences in the geometry simulated in the current study with others. In water–lung interface, the absorbed dose fluctuates because of the density variations and secondary electron generation rate in different media. This phenomenon is exacerbated in conditions of small fields and high-energy photon beams currently being used in radiation therapy. In low-density material such as the lung, the absorbed dose reduces abruptly due to the reduction of secondary electrons coming from upstream in the lung and the dose build-down region is created after water–lung interface. On the other hand, in water–lung interface, the dose build-up region is generated due to more secondary electrons production in unit density medium; and at a short distance, the electronic equilibrium was created. Overall, the interface effect was very pronounced for the 18 MV photon beams, since the range of secondary electrons was higher. Jones and Das studied the effect of lung heterogeneity on small beamlets for 6, 15, and 24 MV photon beams by the EGSnrc MC code.[36] Their simulations showed a dose decrease for small fields in the presence of low-density media due to the lack of lateral electronic equilibrium. As the density and field size increased, the dose reduction was less pronounced and for the 10 cm field, there was an increased dose as expected due to lack of attenuation. Their data suggested that current TPS may dramatically over or underestimate the dose in inhomogeneous media for small field sizes that are used for IMRT. In a study on lung in the small field sizes, a large dose reduction was reported in the lung for field size of 2 cm × 2 cm due to the LED and it reached up to 16.2% and 33.3% for 6 and 18 MV beams, respectively.[36] Carrasco et al. assessed the absorbed dose by measurements, MC simulations, and TPS calculations for 10 cm × 10 cm, 5 cm × 5 cm, 2 cm × 2 cm and 1 cm × 1 cm field sizes and X-ray spectrum of 6 and 10 MV, where the collapsed cone overestimated the dose inside the lung heterogeneity about 3–5%.[37] In a similar study by da Rosa et al., they compared the accuracy of different inhomogeneity correction methods in a lung phantom.[38] Analyzing their pertinent data on the MC and the curves without correction revealed that the dose reduction percentages inside the lung at the depth 10 cm (the comparable depth with our results, 5 cm distance from interface) were 50% and 90%, respectively, for 2 cm × 2 cm and 1 cm × 1 cm field sizes for 15 MV photon beam. Comparing our results of 46% and 70% for the same field sizes, the differences were acceptable with consideration of differences in used MC code, geometries, and photon beams energies. We compared our 6 MV results with the study of Stathakis et al. on a similar geometry with lung thickness of 5 cm.[39] The BEAMnrc/DOSXYZnrc code calculations were used in their study and two other analytical algorithms were assessed against MC results. The dose reduction percentages inside the lung were obtained for 1 cm × 1 cm, 2 cm × 2 cm, and 3 cm × 3 cm field sizes. Their maximum values were approximately 50%, 16%, and 11%, respectively, while our results were 39%, 13%, and 7% for the similar geometry. As we can deduce from our results, the obtained dose reductions were consistent with their results and like other cases the observed discrepancies can be attributed to the small differences in MC code and geometries. In a recent study by Disher et al., effects of lateral disequilibrium were studied for the lung with different densities ranging from 0.001 to 5 g/cm3. Their study showed that several LED small fields in ultralow density of the lung can lead to significant dose reduction inside the lung and cause large under-dosage of solid tumor inside the lung.[40] Our results were consistent with the recent studies on stereotactic body radiation therapy (SBRT) fields used for lung cancer. Consequently, in accordance with previous studies, application of more accurate dose calculation engines seems an essential prerequisite for lung cancer treatment to lower the uncertainty associated with delivered dose to the small regions of the lung. The dose response for R2 of gel foam dosimeter is density dependent. The strongest dependence was found for the R2-dose response. It was found that the variations in R2 are related to variations in bubble size distribution rather than density variations. De Deenet et al. suggested that the strong R2 relaxation dispersion can be the main reason for the variations in the R2 profiles. A stability study of the dose-R2 response reveals a similar course of the dose-R2 sensitivity as in a water-density-equivalent normoxic MAC-based gel dosimeter.[31] Finally, after comparison between MC simulations in the virtual lung phantom and lung-equivalent gel in the same volumes, it is clear for us that the small discrepancy as shown in [Figure 9] is due to the heterogeneity of the foam gel and also may be due to little density differences in the obtained lung foam gel and simulated lung in the MCNP code. Shortly, there is a good agreement between our results and other authors as we had discussed in the literature.


Dose calculations inside the lung were performed for small fields used in IMRT or SBRT using MC method. The results were in accordance with the previous studies and large dose reduction was found for lung tissue. Thus, accurate dose calculation for these techniques has crucial importance for radiation therapy outcome and its shortcomings may compromise the expected survival for treated patients. Application of either MC method or other algorithms capable of considering the complex dosimetric situation in the lung is recommended. Gel foam is introduced as a potential 3D integrating dosimeter for verification of absorbed dose distributions in the lung during radiation treatments. To the best of our knowledge, the polymer gel foam dosimeter may become a valuable verification tool for the implementation of MC-based treatment plans in the clinic.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.


1Maryanski MJ, Schulz RJ, Ibbott GS, Gatenby JC, Xie J, Horton D, et al. Magnetic resonance imaging of radiation dose distributions using a polymer-gel dosimeter. Phys Med Biol 1994;39:1437-55.
2McJury M, Oldham M, Cosgrove VP, Murphy PS, Doran S, Leach MO, et al. Radiation dosimetry using polymer gels: Methods and applications. Br J Radiol 2000;73:919-29.
3Haraldsson P, Bäck SA, Magnusson P, Olsson LE. Dose response characteristics and basic dose distribution data for a polymerization-based dosemeter gel evaluated using MR. Br J Radiol 2000;73:58-65.
4Heufelder J, Stiefel S, Pfaender M, Lüdemann L, Grebe G, Heese J. Use of BANG polymer gel for dose measurements in a 68 MeV proton beam. Med Phys 2003;30:1235-40.
5Wong CJ, Ackerly T, He C, Patterson W, Powell CE, Ho A, et al. High-resolution measurements of small field beams using polymer gels. Appl Radiat Isot 2007;65:1160-4.
6MacDougall ND, Miquel ME, Keevil SF. Effects of phantom volume and shape on the accuracy of MRI BANG gel dosimetry using BANG3. Br J Radiol 2008;81:46-50.
7Maryanski MJ, Gore JC, Kennan RP, Schulz RJ. NMR relaxation enhancement in gels polymerized and cross-linked by ionizing radiation: A new approach to 3D dosimetry by MRI. Magn Reson Imaging 1993;11:253-8.
8De Deene Y. Essential characteristics of polymer gel dosimeters. J Phys Conf Ser 2004;3:34-57.
9Lepage M, McMahon K, Galloway GJ, De Deene Y, Bäck SA, Baldock C. Magnetization transfer imaging for polymer gel dosimetry. Phys Med Biol 2002;47:1881-90.
10De Deene Y, Vergote K, Claeys C, De Wagter C. Three dimensional radiation dosimetry in lung-equivalent regions by use of a radiation sensitive gel foam: Proof of principle. Med Phys 2006;33:2586-97.
11Olberg S, Skretting A, Bruland O, Olsen DR. Dose distribution measurements by MRI of a phantom containing lung tissue equivalent compartments made of ferrous sulphate gel. Phys Med Biol 2000;45:2761-70.
12Oldham M. Optical-CT scanning of polymer gels. J Phys Conf Ser 2004;3:122-35.
13Gum F, Scherer J, Bogner L, Solleder M, Rhein B, Bock M. Preliminary study on the use of an inhomogeneous anthropomorphic Fricke gel phantom and 3D magnetic resonance dosimetry for verification of IMRT treatment plans. Phys Med Biol 2002;47:N67-77.
14Haraldsson P, Karlsson A, Wieslander E, Gustavsson H, Bäck SA. Dose response evaluation of a low-density normoxic polymer gel dosimeter using MRI. Phys Med Biol 2006;51:919-28.
15Saitoh H, Fujisaki T, Sakai R, Kunieda E. Dose distribution of narrow beam irradiation for small lung tumor. Int J Radiat Oncol Biol Phys 2002;53:1380-7.
16Maryanski MJ, Schulz RJ, Ibbott GS, Gatenby JC, Xie J, Horton D, et al. Magnetic resonance imaging of radiation dose distributions using a polymer-gel dosimeter. Phys Med Biol 1994;39:1437-55.
17Ibbott GS, Maryanski MJ, Eastman P, Holcomb SD, Zhang Y, Avison RG, et al. Three-dimensional visualization and measurement of conformal dose distributions using magnetic resonance imaging of BANG polymer gel dosimeters. Int J Radiat Oncol Biol Phys 1997;38:1097-103.
18Berg A, Ertl A, Moser E. High-resolution polymer gel dosimetry by parameter selective MR-microimaging on a whole body scanner at 3T. Med Phys 2001;28:833-43.
19De Deene Y, Baldock C. Optimization of multiple spin-echo sequences for 3D polymer gel dosimetry. Phys Med Biol 2002;47:3117-41.
20Gore JC, Ranade M, Maryañski MJ, Schulz RJ. Radiation dose distributions in three dimensions from tomographic optical density scanning of polymer gels: I. Development of an optical scanner. Phys Med Biol 1996;41:2695-704.
21Hilts M, Audet C, Duzenli C, Jirasek A. Polymer gel dosimetry using x-ray computed tomography: A feasibility study. Phys Med Biol 2000;45:2559-71.
22Trapp JV, Bäck SA, Lepage M, Michael G, Baldock C. An experimental study of the dose response of polymer gel dosimeters imaged with x-ray computed tomography. Phys Med Biol 2001;46:2939-51.
23Oldham M, Kim L. Optical-CT gel-dosimetry. II: Optical artifacts and geometrical distortion. Med Phys 2004;31:1093-104.
24Jirasek A, Hilts M, Shaw C, Baxter P. Investigation of tetrakis hydroxymethyl phosphonium chloride as an antioxidant for use in X-ray computed tomography polyacrylamide gel dosimetry. Phys Med Biol 2006;51:1891-906.
25Baldock C, Rintoul L, Keevil SF, Pope JM, George GA. Fourier transform Raman spectroscopy of polyacrylamide gels (PAGs) for radiation dosimetry. Phys Med Biol 1998;43:3617-27.
26Rintoul L, Lepage M, Baldock C. Radiation dose distribution in polymer gels by Raman spectroscopy. Appl Spectrosc 2003;57:51-7.
27Mather ML, Whittaker AK, Baldock C. Ultrasound evaluation of polymer gel dosimeters. Phys Med Biol 2002;47:1449-58.
28Mather ML, De Deene Y, Whittaker AK, Simon GP, Rutgers R, Baldock C. Investigation of ultrasonic properties of PAG and MAGIC polymer gel dosimeters. Phys Med Biol 2002;47:4397-409.
29De Deene Y, De Wagter C. Artefacts in multi-echo T2 imaging for high-precision gel dosimetry: III. Effects of temperature drift during scanning. Phys Med Biol 2001;46:2697-711.
30De Deene Y, De Wagter C, De Neve W, Achten E. Artefacts in multi-echo T2 imaging for high-precision gel dosimetry: I. Analysis and compensation of eddy currents. Phys Med Biol 2000;45:1807-23.
31De Deene Y, De Wagter C, De Neve W, Achten E. Artefacts in multi-echo T2 imaging for high-precision gel dosimetry: II. Analysis of B1-field inhomogeneity. Phys Med Biol 2000;45:1825-39.
32MacDougall ND, Miquel ME, Keevil SF. Effects of phantom volume and shape on the accuracy of MRI BANG gel dosimetry using BANG3. Br J Radiol 2008;81:46-50.
33Ertl A, Berg A, Zehetmayer M, Frigo P. High-resolution dose profile studies based on MR imaging with polymer BANG (TM) gels in stereotactic radiation techniques. Magn Reson Imaging 2000;18:343-9.
34Baldock C, Burford RP, Billingham N, Wagner GS, Patval S, Badawi RD, et al. Experimental procedure for the manufacture and calibration of polyacrylamide gel (PAG) for magnetic resonance imaging (MRI) radiation dosimetry. Phys Med Biol 1998;43:695-702.
35Marek K, Piotr M. The polyGeVero software for fast and easy computation of 3D radiotherapy dosimetry data. J Phys 2014: Conference Series, Volume 573, conference 1.
36Jones AO, Das IJ. Comparison of inhomogeneity correction algorithms in small photon fields. Med Phys 2005;32:766-76.
37Carrasco P, Jornet N, Duch MA, Weber L, Ginjaume M, Eudaldo T, et al. Comparison of dose calculation algorithms in phantoms with lung equivalent heterogeneities under conditions of lateral electronic disequilibrium. Med Phys 2004;31:2899-911.
38da Rosa LA, Cardoso SC, Campos LT, Alves VG, Batista DV, Facure A. Percentage depth dose evaluation in heterogeneous media using thermoluminescent dosimetry. J Appl Clin Med Phys 2010;11:2947.
39Stathakis S, Esquivel C, Quino LS, Myers P, Calvo O, Mavroidis P, et al. Accuracy of the small field dosimetry using the Acuros XB dose calculation algorithm within and beyond heterogeneous media for 6 MV photon beams. Int J Med Phys Clin Eng Radiat Oncol 2012;1:78-87.
40Disher B, Hajdok G, Gaede S, Battista JJ. An in-depth Monte Carlo study of lateral electron disequilibrium for small fields in ultra-low density lung: Implications for modern radiation therapy. Phys Med Biol 2012;57:1543-59.