|Year : 2021 | Volume
| Issue : 2 | Page : 450-454
Evaluating the effects of metal artifacts on dose distribution of the pelvic region
Nooshin Banaee1, Mehdi Salehi Barough1, Sepideh Asgari1, Elham Hosseinzadeh1, Ehsan Salimi2
1 Department of Medical Radiation, Engineering Faculty, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 Institute for Research in Fundamental Sciences, Iranian Light Source Facility, Tehran, Iran
|Date of Submission||23-Sep-2019|
|Date of Decision||22-Nov-2019|
|Date of Acceptance||07-Jan-2020|
|Date of Web Publication||19-Oct-2020|
Department of Medical Radiation, Engineering Faculty, Central Tehran Branch, Islamic Azad University, Tehran
Source of Support: None, Conflict of Interest: None
Aim of the Study: Some cancerous patients have hip prosthesis of metal elements when they undergo radiation therapy. Metal implants are a cause of metal artifacts in computed tomography (CT) images due to their higher density compared to normal tissues. The aim of this study is to evaluate the quantitative effects of metal artifacts on dose distribution of the pelvic region.
Materials and Methods: Seven patients with metal implants in the pelvic region were scanned and CT images were exported to the Monaco treatment planning system. Based on the diagnosis of each patient, three-dimensional plans were implemented on CT images and dose distributions were extracted. At the next step, metal artifacts were contoured and electron densities of these new structures were modified to the extent of soft tissue. Finally, dose distributions and the differences were investigated by VeriSoft software.
Results: The results of this study showed that if the electron density to metal artifacts is not assigned properly, it will increase the calculated monitor units (MUs) by almost 3.78 MUs/fraction which will significantly affect total dose distribution of treatment.
Conclusion: For the precise implementation of the treatment and in order to minimize the systematic errors related to the calculated MUs, necessary corrections on the electron density of metal artifacts should be considered before the treatment planning. The issue will be more critical in advanced treatment modalities where dose escalation is needed.
Keywords: Dose distribution, electron density, metal artifact, metal implant, radiotherapy
|How to cite this article:|
Banaee N, Barough MS, Asgari S, Hosseinzadeh E, Salimi E. Evaluating the effects of metal artifacts on dose distribution of the pelvic region. J Can Res Ther 2021;17:450-4
|How to cite this URL:|
Banaee N, Barough MS, Asgari S, Hosseinzadeh E, Salimi E. Evaluating the effects of metal artifacts on dose distribution of the pelvic region. J Can Res Ther [serial online] 2021 [cited 2021 Aug 5];17:450-4. Available from: https://www.cancerjournal.net/text.asp?2021/17/2/450/298623
| > Introduction|| |
In radiotherapy, treatment delivery methods are getting more complicated due to treatment precision. Therefore, the initial step of the treatment chain is to confirm that the results of treatment planning system (TPS) are the same as delivery.
Nowadays, pelvic cancers including prostate, bladder, cervix, and rectum consist of a high rate of incidence. As the population get older, the number of patients with hip prosthesis presenting for radiotherapy is expected to increase. According to the National Joint Registry, 86,488 hip joint replacements were done in 2012, which is a 7% increase from 2011.
The hip prostheses are usually made of high atomic number elements causing intense metal artifacts that bring about blurring and streaking on computed tomography (CT) images. In megavoltage photon beams, these substances have the potential to severely influence the dose delivered to the prescription point and to tissue shielded by the prosthesis. Furthermore, the metal artifacts appear like bones with the same or higher electron densities (EDs) which are expected to affect dose calculation accuracy of TPS.,,
Many different techniques have been offered to decrease metal artifacts. Some techniques suggest replacing the metal implants with less attenuating materials or applying higher energies of X-ray beams for preventing metal artifacts. However, in the reality, based on the patient's situation, replacing implants and using high-energy X-rays are not feasible. Accordingly, a quantitative estimation of the effects of metal artifacts on dose calculation before treatment is crucial.
The purpose of this study is to evaluate the quantitative effects of metal artifacts on dose distribution of the pelvic region using the Monaco TPS (Elekta, Stockholm, Sweden) and VeriSoft software (PTW, Freiburg, Germany).
| > Materials and Methods|| |
At the onset of the study, the CIRS Electron Density Phantom (CIRS, Norfolk, VA, USA) with several interchangeable rods made of various tissue-equivalent materials consisting of three rods of lung, liver, muscle, water, and bone was applied to plot the CT number-ED calibration curve. The electron density of the materials ranged from 0.18 (lung) to 1.8 (bone).
Then, the acquired CT-to-ED file was imported to the Monaco TPS. These data have a fundamental role in the dose calculation of TPS, because Monaco assigns interaction probabilities and stopping powers to each voxel based on its mass density. It converts CT numbers to EDs using the defined CT-to-ED file.
Then, pelvic CT images of seven patients with metal prosthesis were imported to the Monaco TPS. [Figure 1] shows a sample CT image of the patient with a metal prosthesis where the metal artifacts in the treating area and surrounded tissues exist.
|Figure 1: The axial view of pelvic computed tomography scan of a patient. (a) Target region, (b) metal artifact region, (c) metal implant|
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Then, based on the target volume and vicinity of organs at risk (e.g., rectum and bladder), a three-dimensional plan (Plan A) with four fields (box) and dose prescription of 46 Gy/23fr was applied. Finally, dose distributions and dose–volume histograms (DVHs) were obtained.
At the next step, metal artifacts on whole CT images were contoured precisely [Figure 1], and because these metal artifacts overlap with soft tissues which have typically electron density equivalent to that of water, EDs of metal artifacts were modified to 1. Using this method, it was expected that the TPS considered metal artifacts as a soft tissue which is approximately close to reality. Then, the treatment plan was calculated based on this conversion and regardless of any metal implant (Plan B).
To evaluate the quantitative comparison of Plan A and Plan B, Digital Imaging and Communications in Medicine dose files of each plan pairs were exported to VeriSoft software with the accepted gamma index criteria offset of 3%/3 mm.
| > Results|| |
[Figure 2] shows the CT-to-ED curve with seven materials used for this study.
|Figure 2: Computed tomography-to-electron density curve with seven materials used for this study|
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The quantitative results of the study regarding the number of monitor units (MUs)/fraction, range of EDs, and maximum dose of the plan sets for seven patients are tabulated in [Table 1].
|Table 1: The quantitative results of seven patients with considering metal artifacts and forcing the electron densities of metal artifacts to 1|
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[Figure 3] shows the DVH curves of Plans A and B for patient 1.
|Figure 3: Dose–volume histogram of patient 1. Dash line: plan A, Dotted lines: plan B|
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The differences between Plans A and B based on the gamma index of 3%/3 mm which are extracted from VeriSoft software are shown in [Figure 4].
|Figure 4: Differences between plans A and B extracted from VeriSoft software for seven patients|
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The evaluation of differences between Plan A and Plan B was also performed quantitatively by choosing the number of similar points. [Table 2] shows the number of similar points, the average of total gamma, and similarity percentage of the two plan sets.
| > Discussion|| |
CT images are always the base images which are used in radiotherapy treatment planning. This modality of imaging has many important roles in radiotherapy, for localization of tumors, representing the cancer region and the area around it, size of the treating region, and density information of all scanned tissues which help predict the attenuation of the traversed photons through tissues and consequently result in dose.,
All TPS s require an input file of CT-to-ED conversion. The principle computation of TPS is to convert CT numbers of tissues to EDs and based on the attenuation of the beam, absorbed dose results. Therefore, the accuracy of this input and the type of materials for this conversion are essential. On the other hand, the accuracy of dose calculation in a TPS, around metal implants without considering similar material in CT-to-ED curve, is of concern. For the current study, the maximum input ED was 1.80 which represents metal; therefore, in the dose calculation of TPS, the accurate ED of metal implant is taken into consideration [Figure 2].
The metal artifacts are one of the sources of systematic errors in calculating required MUs to deliver certain amount of radiation dose. Therefore, it seems that evaluating the effects of metal artifacts on dose distribution is requisite.
In CT scan images of the studied patients, soft tissue, bone, target region, air cavities, metal implants, and metal artifacts are visible. Metal artifacts are overlapped with some air voxels and mostly soft tissues with higher EDs compared to the real structure of overlapping tissues [Figure 1].
According to [Table 1], comparing the effects of metal artifacts on studied patients showed that based on patients abnormality, treatment plan, and adjacency of metal artifacts with soft tissues, a maximum difference of 3.78 MUs/fraction could be found by not assigning correct ED for metal artifacts. Because the metal artifacts are represented by higher EDs in CT images, the required MU to deliver certain amount of dose without considering correct ED of metal artifacts could exceed by almost 1.2% per fraction. In reliance on the number of fractions for a whole treatment, this variation on the number of delivered MUs will lead to significant estimation of treatment outcome and patient quality assurance.
This issue will be more crucial in advanced treatment modalities e.g., Intensity Modulated Radiation Therapy (IMRT) and Volumetric modulated Arc Therapy (VMAT) where patient-specific verification and dose escalation are needed.
In the study of Ziemann et al., different methods were applied to improve the dose calculation in radiation therapy due to metal artifacts. The results of that study showed that metal artifacts lead to a dose error in the isocenter up to 8.4% and the corrections with the Augmented Likelihood Image Reconstruction reduce this dose error to 2.7%, corrections with linear interpolation to 3.2%, and manual artifact correction to 4.1%. The discrepancy of the results might be functions of accurate contouring, distance of treating region from metal implant, density and type of metal, intensity of metal artifacts, energy of photon beams, and size of the target volume.
DVH curves of all patients were also evaluated in the TPS. The first patient information is shown in [Figure 3]. According to the calculation results of the TPS, the TPS has calculated a higher maximum dose for higher EDs (Plan A), and when the artifact is corrected by modifying EDs (Plan B), the maximum dose of the plan is reduced.
The results of VeriSoft software showed that at regions far from the metal implant, modifying ED information does not have significant effects on dose distribution [Figure 4].
The results of this study indicated that for an accurate treatment, CT images of the patients should be edited and necessary conversions regarding artifact corrections should be applied before treatment.
| > Conclusion|| |
The results of this study showed that in each fraction of radiotherapy, the maximum difference of 3.78 could be retrieved in calculated monitor units of corrected and notcorrected CT images within regions containing metal implants. Based on the number of fractions for a whole treatment, this variation on the number of delivered MUs will lead to significant errors of treatment delivery.
Therefore, to minimize the number of systematic errors related to the calculated MUs and also for the accurate and effective treatment, necessary corrections should be considered before treatment planning.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2]