The effects of metal on size specific dose estimation (SSDE) in CT : a phantom study

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1 The University of Toledo The University of Toledo Digital Repository Theses and Dissertations 2016 The effects of metal on size specific dose estimation (SSDE) in CT : a phantom study Maram M. Alsanea University of Toledo Follow this and additional works at: Recommended Citation Alsanea, Maram M., "The effects of metal on size specific dose estimation (SSDE) in CT : a phantom study" (2016). Theses and Dissertations This Thesis is brought to you for free and open access by The University of Toledo Digital Repository. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of The University of Toledo Digital Repository. For more information, please see the repository's About page.

2 A Thesis entitled The Effects of Metal on Size Specific Dose Estimation (SSDE) in CT: A Phantom Study. by Maram M Alsanea Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Biomedical Sciences Dr. E. Parsai, Committee Chair Dr. Kerry Krugh, Committee Member Dr. Diana Shvydka, Committee Member Dr. Patricia R. Komuniecki, Dean College of Graduate Studies The University of Toledo May 2016

3 Copyright 2016, Maram Mohammed Alsanea This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author.

4 An Abstract of The Effects of Metal on Size Specific Dose Estimation (SSDE) in CT: A Phantom Study by Maram M. Alsanea Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Biomedical Science Degree in Medical Physics The University of Toledo May 2016 Over the past number of years there has been a significant increase in the awareness of radiation dose from use of computed tomography (CT). Efforts have been made to reduce radiation dose from CT and to better quantify dose being delivered. However, unfortunately, these dose metrics such as CTDIvol are not a specific patient dose. In 2011, the size-specific dose estimation (SSDE) was introduced by AAPM TG- 204 which accounts for the physical size of the patient. However, the approach presented in TG-204 ignores the importance of the attenuation differences in the body. In 2014, a newer methodology that accounted for tissue attenuation was introduced by the AAPM TG-220 based on the concept of water equivalent diameter, Dw. One of the limitation of TG-220 is that there is no estimation of the dose while highly attenuating objects such as metal is present in the body. The purpose of this research is to evaluate the accuracy of size-specific dose estimates in CT in the presence of simulated metal prostheses using a conventional PMMA CTDI phantom at different phantom diameter (body and head) and beam energy. Titanium, Cobalt- chromium and stainless steel alloys rods were used in the study. Two iii

5 approaches were used as introduced by AAPM TG-204 and 220 utilizing the effective diameter and the Dw calculations. From these calculations, conversion factors have been derived that could be applied to the measured CTDIvol to convert it to specific patient dose, or size specific dose estimate, (SSDE). Radiation dose in tissue (f-factor = 0.94) was measured at various chamber positions with the presence of metal. Following, an average weighted tissue dose (AWTD) was calculated in a manner similar to the weighted CTDI (CTDIw). In general, for the 32 cm body phantom SSDE220 provided more accurate estimates of AWTD than did SSDE204. For smaller patient size, represented by the 16 cm head phantom, the SSDE204 was a more accurate estimate of AWTD that that of SSDE220. However, as the quantity of metal increased it was shown that SSDE220 became more accurate where the percentage error was within ±4% of the AWTD. In addition, the acquired axial CT images were reconstructed both with and without a single energy metal artifact reduction algorithm (SEMAR), to study the effect on Dw. The Dw calculations used to determine SSDE220 varied by less than 0.2% between the images reconstructed with and without the metal artifact reduction algorithm. For the majority of the scans percentage error observed with 100 kvp is less than that with 120 kvp for SSDE204. Finally, a comparison of the manually calculated SSDE220 and that calculated by the Radimetrics software, showed an overestimation of SSDE values reported by the software compared to the manually calculated measurements which is due to an underestimation of Dw values calculated by the software. This underestimation resulted from including the slices effected by the cone beam artifact in SSDE calculations. iv

6 Acknowledgements I cannot express enough appreciation for Dr. Krugh for his patient guidance, encouragement, and priceless advice for this research as well as with everything he taught me related to clinical work. Also, I wish to acknowledge the help provided by the CT technologists at the University of Toledo Medical Center in arranging time for working on the CT scanner. Finally, I am particularly grateful for my husband for all the sacrifices, support and encouragement for pursing my Master s degree. v

7 Table of Contents Abstract... iii Acknowledgements...v Table of Contents... vi List of Tables... viii List of Figures... ix 1 Introduction The Evolution of CT Technology CT image Data and Reconstruction Hounsfield Unit Type of Reconstructions in CT Concern with Doses from CT CT Dosimetry Patient Dosimetry Dose Monitoring Software (Radimetrics) Metal artifact Objectives of the study Methods and Materials Results and Discussion CTDIvol Measurements SSDE Determination and Comparison with the Measured Dose...34 vi

8 3.2.1 Effect of Metal type on Dose Estimation Effect of Different Reconstruction Algorithms on Dose Estimation Effect of Quantity of Metal rods on Dose estimation Effect of Beam energy on Dose estimation Comparison of Dw, effective diameter and SSDE calculation with Radiometrics values Conclusion...46 References...48 vii

9 List of Tables 2.1 Technique factors used for the scans using both phantoms (body and head) without and with the metal rods in place AWTD and SSDE values with the % error for the body phantom at 120kVp using one rod of different metal types AWTD and SSDE values with the % error for the head phantom at 120kVp using one rod of different metal types The average CT number, Dw and their standard deviation for each reconstruction algorithm at different metal type AWTD and SSDE values with the % error for the body phantom at 120kVp using Co-Cr metal at varies quantity AWTD and SSDE values with the % error for the body phantom using 4 rods of Co-Cr metal at different beam energy Radimetrics Comparison of SSDE and DW values...44 viii

10 List of Figures 1-1 First-generation CT scanner geometry A diagram of the Third- generation CT scanner and basic component of a modern third generation CT scanner (Toshiba system) operated at UTMC Overbeaming effect become less with 64 slice scanners compared with 4 slice CT because of the geometrically wider detector A non SEMAR image and a SEMAR image for an oral cavity tumor A conventional nested CTDI Phantom utilized for the study Illustration of the nested CTDI phantom used to acquire the images for the study using both parts of the phantom Alignment and centering of the CTDI Body phantom using the laser light before starting the measurements The Raysafe screen shows the dose profile of the acquisition where one peak appears as an indication of one 360 degree rotation of the X-ray tube One of the measurements performed on the study A circular ROI has been drawn manually in one of the images Using the ROI manager in the Image J software an average of the CT numbers and areas in the ROIs have been calculated Cone beam effect on one of the images...31 ix

11 2-9 Example calculations by the Radimetrics software Variation of SSDE with different reconstruction algorithms with and without application of SEMAR relative to AIDR 3D using the body phantom Variation of SSDE with different reconstruction algorithms with and without application of SEMAR relative to AIDR 3D using the head phantom The effect of the SST rod on image quality of an AIDR reconstructed image with and without SEMAR The effect of the SST rod on image quality of a FBP reconstructed image with and without SEMAR % Error of the SSDE at different quantity of TI rods using the head phantom The % Error at two different beam energy x

12 Chapter 1 Introduction X-ray computed tomography (CT) is a medical imaging modality that can acquire and reconstruct images of thin cross-sections of an object using X-ray. Since its introduction to clinical application in the early 1970s (Bushberg, 2011), CT technology has made rapid improvements that contribute to the field of medical imaging. When first introduced, it was thought that CT would be efficient for only head scans because of the duration of the necessary scan. However, the accuracy (high image quality) and fast scan capabilities enable current CT system to be used in many clinical applications. Some applications include cardiac imaging, perfusion studies, and guided procedures which include biopsy and abscess drainage. Computed tomography enables visualization of different anatomies in great spatial resolution and with high contrast. Moreover, CT contributes to the field of nuclear medicine by the technology of hybrid imaging devices such as single-photon emission computed tomography, SPECT/CT, and positron emission tomography, PET/CT which enable anatomic visualization in addition to the physiologic images provided from SPECT and PET scans, constantly proving a better visualization of the body. In addition, CT has an important role in the field of oncology in terms of using a CT simulator which allows the physician to have a good estimation of 1

13 the cancer position, staging, and its extent in the body before starting the radiation treatment. As a result, physicians are able to provide a more efficient plan of treatment by minimizing the risk to healthy tissue. Even though there has been controversy about the risk versus benefits in CT, there is no doubt that this technology discovers many diseases and helps in an early diagnosis and treatment that contributes to the health of the human beings The evolution of CT technology CT systems have been through different changes in data acquisition geometries which were represented by generations of CT. Each generation differs from others in terms of acquisition and/or components. Starting from 1971, when the first generation of CT was introduced until present time, CT has encountered an enormous amount of technological improvements. First of all, in the early days of CT when the first CT generation occurred, the scanner was limited to measuring a single slice at a time using one single pencil beam. The measurements were made in a linear fashion and when the first projection was done, the x-ray tube and detector rotated another one degree around the patient in order to collect another projection. This process is continued until collecting 180-degrees of projection data. This method of data acquisition is called translation-rotate (Figure 1-1). One of the first generation scanners, a head scanner, was built by Hounsfield at the Central Research Laboratories of the Company EMI, Ltd. in England. The scan was able to acquire 12 slices, each with a 13-mm slice thickness (Ulzheimer et al, 2009). However, one of the obvious issues with this type of scanner was the long acquisition time which was not suitable for imaging other body parts where motion 2

14 would be presence. As a result, there was the demand for more improvements which then led to the second generation of CT. Figure 1-1. First-generation CT scanner geometry. (Goldman, 2007). In the second generation, the number of pencil beams increased. One of the second CT generation scanners was the one manufactured by EMI in late 1975, which consisted of 30 detectors; constantly reducing the scan time to even less than 20 second per slice and acquiring more measurements through the body, which, contributes to better image quality. Following came the third generation scanner consisting of a large number of detectors arrays arranged in an arc which covered the whole object being scanned. This generation eliminated the need for translation of the source and the detector as was necessary with the first and second generation. In this design, the X-ray tube and detector rotates simultaneously around the patient to complete a 360-degree acquisition. Most of the scanners used today utilize this ability (Figure 1-2). Even though this generation solved problems that occurred with the first and second generations such as time and 3

15 image quality, there were issues with detector stability and artifacts in the image (ring artifact). The reason is, since in this design each detector measures rays passing only at a specific distance from the center of rotation, any error or drift in the calibration of a detector relative to other detectors is back -projected along these ray paths causing a ring in the image. Figure 1-2. In the left a diagram of The Third- generation CT scanner. (Goldman, 2007). In the right, basic component of a modern third generation CT scanner s (Toshiba system) operated at the University of Toledo Medical Center, Toledo, Ohio. The components appear while the gantry covers are removed. The fourth generation of CT consists of a 360-degree ring around the patient which contains stationary detectors while the x-ray tube rotates around the patient. In this system, since the entire projection is measured by a single detector, the detector cell size is what determines the spacing between the samples in the formed projection. Unlike the third generation where the data is acquired by the detector array concurrently, each fourth-generation detector can measure rays at any distance from the center of rotation and can be dynamically calibrated before it passes into the patient s shadow, so that ring artifacts are not a problem as introduced with the third generation (Goldman, 2007). However, there were some disadvantages for the fourth generation which included the 4

16 need for a large ring diameter since the tube rotates inside the detector ring; as a result, the source to skin distance should be maintained. In addition, for a more efficient system, up to 4800 detectors should be used; otherwise, there will be gaps between detectors which causes low geometric dose efficiency. Finally, the presence of scatter was a major drawback which was never solved in this generation. Because each detector cell can receive photons over a large angle, there will be no rejection of scatter and the use of scatter-absorbing septa (used in the third generation) is not possible because the septa should be aimed at the center of the ring, which was the patient s location. As a result, a large amount of the attenuated primary radiation would be shielded by the septa. In early third and fourth generation scanners, the detector and x-ray tube would have to stop after the acquisition of each slice; however, with the introduction of the slip-ring concept in the early 1990s this was solved. In this technology, the x-ray tube and the detector is not connected to the stationary electronic scanner; as a result, continuous rotation of the tube and detector is possible. The slip ring allows the transmission of both the tube power input and the detector signal output. Also, the acquisition of the data can be achieved while the patient table is moving which is called helical (spiral) scan mode. This type of scan acquisition is different from the axial scan acquisition which is done while the patient table remains stationary during the scan. However, at the beginning of this technology, the acquisition was done in a single slice, (SDCT), which caused small volume coverage in a single breath hold and poor spatial resolution in the z-axis due to wide collimation. Larger volume coverage and improved z-axis resolution became possible after the introduction of four-slice CT systems in Multi-detector CT (MDCT) scanner 5

17 differs from the single-slice CT scanner mainly in terms of design of detector assembly as the MDCT systems have a detector array that is segmented in the z-axis. An advantage associated with this system is the ability to perform cardiac imaging with the addition of the electrocardiogram (ECG) gating capability enabled by gantry rotation times down to 0.5 s. In 2000, the introduction of an eight-slice CT system enabled even shorter scan times, but did not yet provide improved z-axis resolution which then was achieved by the 16 slice system. In 2004, all manufacturers introduced the 32, 40 and even 64 slice system. With 64 slice CT scanners, while maintaining the image quality, there is reduction in the radiation exposure compared with the previous MDCT systems. The reason for this is found in the overbeaming effect (Rogalla et al, 2009).The overbeaming effect describes the amount of x-ray beam that is in excess to the width of the active detector elements (Figure 1-3). As the collimation width increases (as was the case with most 64 slice scanner) the amount of overbeaming decreases on a percentage basis. The 64 slice detector assemblies vary in width from 2.8 to 4 cm and were segmented to enable the acquisition of slices with mm in thickness thus providing high z-axis resolution. The race for more advance CT systems with more slices has continued. Currently, 128, 256,320, and 640 slice CT systems are available. In conclusion, the availability of MDCT enables CT to be used in different applications to scan different parts of the body and gives detailed picture of the organs scanned in a non-invasive way. 6

18 Figure 1-3. Overbeaming effect become less with 64 slice scanners compared with 4 slice CT because of the geometrically wider detector. (Rogalla et al, 2009) CT scanners have advanced throughout time for the sake of higher image quality, faster scanning capabilities and more volume coverage in one rotation of the scanner CT image data and reconstruction Hounsfield Unit The intensity scale or the gray scale in CT images is called the CT number or Hounsfield Unit (HU) in honor of Sir Godfrey Hounsfield. The CT number is derived from the voxel attenuation coefficient that is calculated from the acquired projection data during image reconstruction. It is defined by the following equation: HU = [µx,y,z - µ(water) / µ(water)]* 1000, (1.1) where µ(water) the linear attenuation coefficient of water, and µx,y,z is the average linear attenuation coefficient of tissue in a voxel at location (x,y,z). As a result, from this definition the CT number of water is 0 and for air is since its linear attenuation 7

19 coefficient is miniscule relative to that of water. Also, since µ is energy- dependent the HU might change slightly at different tube voltage. However, since it is normalized to water the HU is fixed at 0 for water Type of Reconstructions in CT Image reconstruction in CT is a mathematical process that generates images from X-ray projection data acquired at many different angles around the patient (Yu et al, 2010).The topic of CT image reconstruction is wide and complex, in this section a brief discussion about some important reconstructions and their applications will be introduced. This topic has been divided into two main categories: analytical reconstruction and iterative reconstruction. First, from a historical point of view, filtered back projection (FBP) was introduced first to clinical application and is one of the analytical type of reconstructions. FBP has been improved over time to accommodate to the advancements in CT technology. In this type of reconstruction, the projection data is first filtered and then back-projected to produce a 2-dimensional array of voxels that are assigned a shade of gray depending on the calculated linear attenuation coefficient in each voxel (Nelson et al, 2011). The filtering process is needed to remove the blurring (star-like artifacts) that occur in different parts of the reconstructed image. Fast reconstruction timing is an advantage of this type. However, the filtering process can elevate the noise level especially for exams that are performed with low radiation technique. As a result, when using few number of photons image quality is limited and not acceptable in some clinical situations. 8

20 At low doses, the demand for better image quality with less noise encourage scientists to create different algorithms to solve these issues. One type of reconstructions that overcomes some of FBP limitations is iterative reconstruction (IR) which as the name indicates is a type of iterative reconstructions. This type of reconstruction works by an initial estimation of the projected image (Forward-projection). Then, the estimated projections are compared with the actual measured projections acquired by the CT detector. An update of the estimated projection is made based on the comparison made. The same steps are repeated until the difference between the estimated projection and the calculated one reaches an acceptable minimum level. One can clearly understand from the introduced function of IR that it is computationally intensive compared to FBP. In contrast, it has many advantages that overcome this limitation. IR provides better noise performance, higher image quality in term of spatial resolution, correction for artifacts (i.e., metal artifact and streak artifact) and better low- contrast detectability. In addition, IR algorithms are known for their dose reduction ability. Several different IR methods have been introduced by CT manufacturers, adaptive iterative dose reduction (AIDR) is an algorithm that is introduce by Toshiba which they claim its ability of dose reduction. A study by Chen et al investigates the effect of 50% dose reduction using AIDR 3D in some images and compare them to the FBP images. The study found that the dose reduction did not affect the quality of the iterative reconstructed coronary CT angiography (CTA) images and in fact, these images are comparable to images acquired at standard radiation exposure and reconstructed with FPB. 9

21 Moreover, a phantom study performed using ADIR shows a reduction in images noise by 40% compare with FBP without altering spatial resolution (Gervaise et al, 2011). To conclude, we should not forget that the main purpose of using a CT scan is to acquire clear images of the human body to reach the best diagnosis. However, we should make every effort to improve image quality without harming the patient with unnecessary dose that could cause side effects Concern with doses from CT Computed tomography is the greatest contributor to the increase in ionizing radiation exposure in medical imaging accounting for 49% of the collective doses received to the US population though it accounts for only 12% of imaging procedures that use ionizing radiation (NCRP, 2006). By 2011, approximately 80 million CT scans per year were used in the United States, 7 million of which were in children. In addition, with all the improvement on CT systems, especially with the introduction of MDCT, it made it easier and faster to perform a CT scan at critical moments such as in trauma. A study shows that just 3.2 percent of emergency patients received CT scans in 1996, while 13.9 percent of emergency patients seen in 2007 received scans with an expected use of CT that is growing 10 percent annually (Kocher et al, 2011). The concern about radiation dose from CT arises from the fact that relatively high doses are associated with a CT scans compared to other conventional radiological procedures. For example, an effective dose from a two view chest x-ray is 0.1mSv compare to 8mSv of that in chest CT exam (Health physics society, 2010). Radiation doses from CT vary between different 10

22 types of studies. Moreover, the number of CT scans that might lead to the development of a cancer varied widely depending on the specific type of CT examinations and the patient s age and sex. A study was performed on data collected at 4 institutions in the San Francisco Bay Area in California evaluated the lifetime attributable risk (LAR) of cancer incidence associated with radiation exposure from different CT examinations. The study discovered that an estimated 1 in 270 women who underwent CT coronary angiography at age 40 years will develop cancer from that CT scan (1 in 600 men), compared with an estimated 1 in 8100 women who had a routine head CT scan at the same age (1 in men) (Bindman et al, 2009). In addition, since the risk associated with younger patients is more critical, they found that for 20-year-old patients, the risks were approximately doubled, and for 60-year-old patients, they were approximately 50% lower. Another study by Berrington de Gonza lez et al, found that approximately future cancers could be related to CT scans performed in the US in On the other hand, there are a lot of studies that advocate CT scans for their benefits. One study that investigated the predicted cancer risk from body CT in young adults (18-35 years old) found that the risk of death from the current illness is more than an order of magnitude greater than dying from long term radiation induced cancer (Zondervan et al, 2013). As a result, the cancer risk from CT has a really small probability. As a result, patients should think of CT as a non-invasive way that helps in an early diagnosis and better life quality. In fact, as with any technology in the medical field, patient health and safety should be the priority. Therefore, recently, scientific researchers have been seeking ways to better quantify the amount of dose given to an 11

23 individual patient during a CT scan which will provide a better understanding of the benefit to risk ratio associated with CT studies CT dosimetry Computed tomography dose index (CTDI) was developed for the purposes of quality control programs to quantify the radiation output from a CT examination and does not directly quantify the patient s risk. It was first introduced by Shope et al, as a metric to quantify the radiation output from a CT examination consisting of multiple adjacent transverse rotations of the x-ray tube along the patient longitudinal axis. CTDI is always measured in an axial scan mode for a single rotation of the x-ray tube. CTDI is defined by the following equation: CTDI = 1 nt D(z)dz, (1.2) where D (z) is the dose function at a point, T is the nominal tomographic section thickness, and n is the number of tomographic produced in a single rotation which means the n value is increased with the increase in the number of detector rows of the MDCT scanners. However, the average dose of multiple scan dose profile reaches a limiting value when the first and last scans are largely separated from the central scan; as a result, they do not contribute measured dose to the central scan region. Following this definition, the FDA introduced integration limits of ±7T where T is as defined previously. CTDIFDA = 1 7T nt 7T D(z)dz, (1.3) CTDIFDA is measured using phantom that is at least 14 cm long with a diameter of 16 cm for head and 32 cm for the body. One problem with this definition is that it is limited to 12

24 the width of 14 cm; as a result, the regions outside this limits is ignored and do not contribute to the calculation. A newer metric called the CTDI 100 which is a representation of the dose accumulated at the center of 100 mm scan which means that it underestimates the dose beyond this region. As a result, the integration limits for equation (1.3) are ±50 mm. CTDI100 is measured with an ionization chamber that has an active length of 100mm. CTDI100 = 1 50mm nt 50mm D(z)dz, (1.4) Since the ion chamber is a gas filled detector, the dose measured will be in air. The meter reading represents the average exposure in roentgens or the air Kerma in mgy. As a result, to get the absorbed dose one can use the following equation (normalized to the nominal beam width, collimated beam): CTDI 100(mGy) = C.f(mGy R ).100mm.meter reading (R) N.T(mm), (1.5) where f is a conversion factor (8.76 mgy/r) for dose in air, and C is a calibration factor that corrects the meter reading for temperature and pressure (is nearly 1.0). Moreover, CTDI varies across the field of view which means that the dose distribution within the phantom is not homogenous. As a result, another modified version of CTDI was developed to average this distribution and was called weighted CTDI (CTDIw) across the FOV. CTDIw is computed by the following equation: CTDI w = 1 3 CTDI 100, center + 2 CTDI 100, periphery, (1.6) 3 Where CTDI100 is computed as in equation (1.5). Finally, during the acquisition of helical scan there are a series of scans which means that there might be some overlaps or even gaps between different series; as a result, we should 13

25 account for the pitch factor which is defined as the ratio of the table travel per rotation (I) to the total collimated beam width (N T): Pitch = I NT, (1.7). From this definition, a volume CTDI (CTDIvol) can be estimated from the following equation: CTDIvol = CTDI w Pitch, (1.8) However, for an axial scan since the table is stationary there is no need to account for the pitch factor. The CTDIvol is determined from a PMMA cylindrical phantoms one for head (16-cm diameter) and the other is a body phantom (32-cm diameter). For each CT system, CTDIvol should be displayed before and after the scan is performed and it has been required of all manufacturers since 2002 Also, it is included in the patient s Digital Imaging and Communication in Medicine (DICOM) dose report. Finally, it is important to notice that CTDIvol is a scanner output that is measured in a specific size phantom and is independent of the patient size. There are some limitations of CTDIvol. As mentioned above, CTDIvol is a dose index and a good tool to compare the quality of different CT scanners. As a result, a very important limitation is that it does not give a measure of the dose absorbed by the patient. Secondly, since CTDIvol is size-independent, patients with different sizes have the same CTDIvol value if scanned using the same imaging parameters. Moreover, for body scans, CTDIvol underestimates the dose to the majority of the patients since it is measured with a 32-cm phantom diameter which corresponds to a very large patient, therefore smaller patients have higher doses for the same technique factors used with the 32-cm cylindrical phantom. These limitations encourage scientists to work more in improving the 14

26 usefulness of CTDIvol in patient dosimetry by using different conversation factors that account for patient size and attenuation Patient dosimetry In 2011, a task group was presented by the AAPM to address some of CTDIvol limitations. This task group (204) was charged with developing conversion factors that can be applied to the scanner output (CTDIvol) to estimate patient dose. These conversion factors account for patient dimensions (AP, lateral, AP+ lateral and effective diameter) and are derived from experimental and Monte Carlo data that were the results from four independent research groups. The resulting conversion factors are tabulated and one can use them after measuring the dimensions of the patient and find the corresponding conversion factor that can be multiplied by the CTDIvol to estimate the patient dose; in other words, size specific dose estimate (SSDE): SSDE204= CTDIvol x CF204, (1.9) Where CF is the conversion factor that resulted from TG-204 based on the dimensions of the imaged object. In addition, if the size variables (AP, LAT, Effective diameter) are not available, one might estimate the effective diameter of the patient by the patient s age to find the conversion factors. However, this might not be accurate since patient sizes are different at the same age (AAPM TG-204, 2011). Even though, TG-204 approach was a good way of estimating SSDE, there are some limitations. Since the LAT dimension can be measured from the CT radiograph, some errors might occur due to the magnification effect on the image if the patient is close to the x ray source. As a result, for this method the patient should be exactly 15

27 centered in the gantry. Also, SSDE, allows estimation of the dose at the center of the scan range and does not take into account variation in dose based on variation in scan length. In addition, in this method the one factor affecting the estimated dose is the patient size; however, in reality X-ray attenuation in the body impacts the absorbed dose by the region being imaged. For example, differences between chest and abdomen in terms of attenuation properties could not be explained with a simple measure of dimension such as effective diameter. The concern about the attenuation of the body was addressed by another task group the AAPM TG-220 that solved this problem by the use of the water equivalent diameter (Dw). The report of AAPM Task Group 220 was published in As mentioned previously, in this report, the main concept in determining patient dose depends on patient attenuation. The Dw is the diameter of water in a cylinder having the same X-ray attenuation of a patient. There are two methods by which you can determine the Dw. The first method is by using the CT image and simple tools available at the workstation to analyze the image. One can calculate the Dw using the following equation: Dw = 2 ( CT(x, y)roi + 1) A ROI π, (1.10) where CT(x, y)roi is the mean CT number in the (Region of Interest) ROI and A ROI is the area of the ROI in unit of square centimeters. The second method to determine Dw allows estimation of Dw before the scan starts therefore having dose estimation before acquiring the image by using the CT localizer. For this method it is recommended that this method be implemented by the scanner manufacturers because they know the relationship between the water attenuation and pixel values in the CT localizer. The reason is because pixel value might vary in the 16

28 CT localizer from among different manufactures. Secondly, unlike with the CT image method, you have to account for the table attenuation because the pixel value is a measure of the total x-ray attenuation of all the objects between the x-ray source and the detector. This reason makes calculation of Dw even more difficult since you have to correct the pixel value for patient table attenuation The calculated Dw values using either one of the mentioned ways are utilized to derive conversion factors using multiple equations that resulted from the effort of different experimental and Monte Carlo studies reported by TG-204. From these conversion factor values that resulted from the water equivalent diameter, one can calculate the SSDE using the following equation: SSDE220= CTDIvol x CF220, (1.11) where CF220 is the conversion factor resulted from TG-220. In conclusion, even though TG-220 gives a good estimate of patient dose, there are some limitations. First, there are some issues when measuring the Dw from the CT localizer which limits the accuracy when using this approach. It is important to acquire the CT localizer while the patient is centered in the gantry. When the patient is farther or closer to the x-ray tube you encounter the issue of minification and magnification. When the patient is closer to the X-ray tube the image will appear magnified while the opposite when the patient is further from the x-ray tube (overestimate of the Dw). Secondly, when using the CT image method, it would fail if the field of view (FOV) is not large enough to include the full cross section of the body because the mean CT number within this FOV would not be the true representation of the attenuation of all voxels within the objet being imaged. Finally, TG-220 does not address the calculation of the dose when the 17

29 presence of foreign objects in the body such as metallic implants, as a result, we need to know the effect of metal on Dw calculations to get an accurate estimation of the dose Dose monitoring software (Bayer Radimetrics TM ) Bayer Radimetrics TM dose monitoring software is one of many packages in the market for dose tracking in CT. Several measurements are reported by the software for better evaluating the radiation risk and keeping the dose as low as possible. The software can report values of organ dose and effective dose of a scan that are calculated from Monte Carlo simulation. Also, values of SSDE are reported based on Dw calculations either from the CT localizer data or the axial images. Moreover, one can choose that SSDE calculation that are based on the effective diameter as introduced by TG-204. Effective diameter, Dw and SSDE values are calculated for each slice of a scan series to account for changes of CTDIvol based on the tube current modulation. Then the calculated values of each slice are averaged over the whole scan. In conclusion, with wide availability of CT scanners, a dose monitoring software represents an effective way of monitoring the dose in CT. As a result, the software provides better management of CT protocols and ultimately the dose to patients Metal artifact In general, an artifact in the image can be described as any discrepancy between the reconstructed value in the image and the true attenuation coefficient of the object 18

30 (Hsieh, 2003). CT systems just like other imaging modalities are prone to artifacts which can have different sources. Artifacts in the image could be a result of the system design, the X-ray tube, the detector, or the patient being imaged. When the images from CT scanners are evaluated by the radiologist, he/she is looking for the best representation of the human anatomy. As a result, the presence of artifacts in the image may lead to difficult diagnoses or misdiagnoses in some cases. Some patients have metallic hardware in their bodies such as heart pacemakers, heart stents, orthopedic implants, and dental fillings. On the other hand, the metal could be attached to the body for necessary purpose such as biopsy needles. The presence of these metallic objects in the body causes a streaking effect on the image with areas of increased and decreased density that obscure adjacent structures. The reason behind this artifact is the beam hardening effect by the metal which causes a high attenuation of the x-ray resulting in incomplete projection data. Also, when scanning a dense object like the case of metallic hardware, artifact may occur due to photon scatter, partial volume effects, photon starvation and data sampling error. Artifacts are different with different types of metals which could be the worst with stainless steel because of its higher attenuation coefficient comparing with other metallic hardware. Generally, less attenuating hardware material generates less missing projection data and therefore fewer artifacts (Kataoka, 2010).Moreover, the shape of the artifact changes depending on the shape of the metal. Consequently, more artifact can be expected with more complex shapes. There are different artifact correction methods introduced in the literature which will be discussed in this section. First, an image can be free of metal artifact by some 19

31 techniques from the operators. The technologist can ask the patient to remove any metallic material before acquiring the image. However, for non-removable items in the body such as dental fillings and surgical clips one can scan the required anatomy while avoiding the metallic materials nearby. For example, the operator can tilt the gantry of the scanner during head scan to avoid artifacts caused by dental fillings. In addition, another way of reducing metallic artifact is by increasing the kvp setting to be able to better penetrate these dense objects in the body. A final technique in this category is to use thin section width to minimize metal artifacts that occur due to contribution of partial volume artifact, an object that partially enters the slice causing two different objects within one voxel. The second category in reducing metallic artifact is by using special software correction provided from the manufacturer. In general, these algorithms work by operating in a projection space which means measuring the ray-sums that do not pass through the metal and estimating the ray-sums passing through the metal object. Then, the corrected projections are reconstructed to provide the artifact reduced image. Different types of algorithms are introduced by different manufacturers. One of these is the recently-developed commercially available algorithm by Toshiba, Single Energy Metal Artifact Reduction (SEMAR). The algorithm can be applied in conjunction with FBP or AIDR. SEMAR is found to improve lesion detectability and image quality in patients with oral cavity lesions as discussed by Funama et al. As shown in Figure 1-4, where the tumor in the oral cavity is not determined with the non-semar image; however, after applying SEMAR, the tumor is easily determined. In addition, SEMAR 20

32 shows significant image quality improvements when scanning patients with hip prostheses and aneurysm embolization coils which was investigated by Sonoda et al. Figure 1-4. On the left, an oral cavity tumor does not appear with the non SEMAR image. On the right, the CT image after applying the SEMAR algorithm. (Funama et al, 2015) Objectives of the study The overall objective of this research, is to study the effect of a patient induced artifact, specifically metal artifact, on the accuracy of size-specific dose estimation. In addition, the study has the following specific objectives: 1- Measure CTDIvol of a CT scanner. 2- Perform measurements of dose to tissue in a conventional PMMA nested CTDI phantom in presence of metal rods at different phantom diameter, type of metals, amount of rods and beam energy. 3- Utilizing the axial CT images resulted from the second objective, calculate the water equivalent diameter (Dw) and effective diameter and subsequently calculate the SSDE using TG-220 and TG-204 conversion factors and the measured 21

33 CTDIvol from the first objective. Reconstruction algorithms will be varied to include FBP, AIDR 3D and application of SEMAR. 4- Evaluate the accuracy of the SSDE in comparison to the measured dose in the second objective. 5- Using Radimetrics software to perform a comparison of the measured Dw, effective diameter, and SSDE by the software to that calculated manually using the Image J software. 22

34 Chapter 2 Materials and Methods This chapter will introduce the methods and materials used to seek the objectives of this study. The axial CT images of a nested CTDI phantom, borrowed from the Ohio State University Medical Physics Department, Columbus, Ohio, were mainly used for the study. This phantom is made of polymethyl methacrylate (PMMA) and includes a 16-cm diameter head phantom and 32-cm diameter body phantom configured in a nested manner (Figure 2-1). The phantom is constructed with a center hole and eight peripheral holes (four in the head phantom and four in the body phantom). Also, the head phantom has four inner holes (Figure 2-2). PMMA rod inserts for these holes are included. 23

35 Figure 2-1 A conventional nested CTDI Phantom utilized for the study. Figure 2-2 Illustration of the nested CTDI phantom used to acquire the images for the study using both parts of the phantom (Head and Body). In addition, the CT images were acquired using a Toshiba Aquilion ONE Vision CT scanner located at the University of Toledo Medical Center (UTMC), Toledo, Ohio. The scanner consists of a wide bore of 78cm in diameter. A maximum of 640 CT slices 24

36 can be acquired in one rotation in 0.35 seconds. The detector consists of 320 elements each with a size of 0.5mm which enables a volume coverage of 16-cm in one rotation. A last unique ability for this scanner is the metal artifact reduction ability using a specific algorithm called SEMAR. To perform the measurements, the phantom is placed on the scanner table and needs to be aligned such that the axis of the phantom is at the isocenter of the scanner and centered in all three planes. Figure 2-3. Alignment and centering of the CTDI Body phantom using the laser light before starting the measurements. To measure the dose in air a RaySafe CT sensor (pencil chamber) with an active length of 10cm has been utilized. The technique factors used for the study are shown in Table (2.1). 25

37 Table 2.1. Technique factors used for the scans using both phantoms (body and head) without and with the metal rods in place. Technique Scan mode mas Detector Calibrated FOV Rotation time(s) kvp Configuration Body Volume mmx80 Large 0.5 Body Volume mmx80 Large 0.5 One should notice that CTDIvol should be measured with no table increment. After finishing the setup of the phantom, the exposure value of the chamber reading in Roentgens (R) has been recorded for the center and then for the periphery holes. During the measurements, much attention has been paid to not include more than an exposure of one 360 degree rotation of the x-ray tube since CTDIvol should be measure at one rotation. The exceed of one rotation of the system could be detected by the dose profile shown in the RaySafe screen interface where one peak represents one full rotation and more than one dose profile in the screen is an indication of the presence of the exposure of more than one rotation of the x-ray tube. As a result, the chamber reading at this effect was ignored and another exposure was performed. Since these meter readings represent the average exposure over the chamber length, the CTDI100 as explained by chapter one was calculated according to equation (1.5) at the center and average of the periphery values. The f-value used to convert exposure to dose in air is 8.76 mgy/r. Moreover, a weighted CTDI was calculated using equation (1.6) which then equals CTDIvol (when no metal presence) since we are acquiring the data with no table movement meaning that we are accounting for a pitch factor equal to unity. The CTDIvol measurements were repeated three times at 120 and 100 kvp, respectively. An average of the values will be used in the calculation to estimate SSDE. In this study, the CTDIvol measurements were done just for 26

38 the body phantom at two beam energies. What has been done first is similar to the annual verification of the measured CTDIvol values with the values reported by the scanner. Figure 2-4. The Raysafe screen shows the dose profile of the acquisition where one peak appears as an indication of the exposure of one 360 degree rotation of the X-ray tube. Next, to simulate the presence of metal prostheses, metal rods were inserted into one or more holes in place of the PMMA rods. Three different type of metals rods were used: stainless steel (SST) alloy 316 rods with a diameter of 12.7 mm (0.5 inches), cobalt-chromium (Co-Cr) alloy rods with a diameter of 11.3 mm, and titanium (Ti) rods with a diameter of 12.7 mm (0.5 inches).. These metals have been chosen in the study for their availability in different clinical application and their unique properties. First of all, 316L stainless steel alloy which has high corrosion resistant when in direct contact with biological fluid due to having a low carbon content of 0.03%. It is used for cranial plates, 27

39 orthopedic fracture plates, dental implants, spinal rods, joint replacement prostheses, stents and catheters. Next, is cobalt- chromium alloy which also has been introduced to clinical use at the same period as the stainless steel and has been utilize for orthopedic implants and dental fillings. Finally is titanium which is relatively new in its application as a medical implant for the replacement of a biological tissue and known for its strength and lighter weight compare to stainless steel. For each tube voltage, the same procedure has been repeated after placing the metal rod(s) in the phantom. Measurements have been done first with one metal rod of each type at a specific hole. As in Figure 2-5, one of the measurements performed on the study where the chamber was at the 9 o clock peripheral position and a SST rod was placed in the inner 6 o clock peripheral hole of the head phantom at 120kVp. Then, the quantity of the metals have been increased and measurements were recorded for further analysis. However, dose to tissue have been calculated instead of dose to air by applying an f-value of 9.4 mgy/r to the formula in equation 1.5. Measurements were taken for body and head phantom using the same protocol in Table 2.1where the head phantom was a representation of a smaller body size. Then, an average weighted tissue dose (AWTD) was calculated using the following equation: AWTD = 1 3 Center dose periphery dose (2.1) The AWTD was calculated for each metal type with different quantity. The acquired images were transferred to Image J Software for further measurements. 28

40 Figure 2-5. One of the measurements using one metal rod. At the workstation, utilizing the Image J software, an ROI was drawn to find the mean CT number and the area of the ROI. The ROIs in each series included the whole cross section of the phantom with care taken not to include irrelevant objects such as the scanner table. In contrast, including some air in the region would not affect the accuracy of the measurements since air has an attenuation coefficient of almost zero. Figure 2-6 shows one of the measurements done in the study where the image was for the body phantom with the chamber at the center position and a Co-Cr alloy rod at the 3 o clock head periphery position. The reconstruction algorithm applied for this image was AIDR 3D without SEMAR; as a result, there was significant metal artifact observed. 29

41 Figure 2-6. A circular ROI has been drawn manually to include the phantom cross section with a little amount of air without any other unrelated objects around the phantom. The CT number was averaged over nearly all the images within a given scan series (Figure 2-7). However, the first and last image of the series was excluded from the measurements since the cone beam causes the peripheral portion of the phantom to be truncated in the image (Figure 2-8). 30

42 Figure 2-7. Using the ROI manager in the Image J software an average of the CT numbers and areas in the ROIs have been calculated. Figure 2-8. Cone beam effect causing the peripheral portion of the phantom to be truncated in the image. Calculation of Dw was performed according to TG-220 by using equation (1.10). Subsequently, these Dw values were utilized to derive conversion factors using the equation: 31

43 y = a e bx, (2.2) where y is the conversion factor, a has a value of , b has a value of , and x is the Dw, also is the effective diameter of the phantom in case calculation are based on the geometric based method without accounting for the different component attenuation in the phantom. Equation 2.2 represent the best fit for the work of four experimental groups presented by TG 204. Even though the equation was first introduced for the use with the effective diameter it can be used, with no further correction, for Dw since it is measure of the water attenuation equivalent object diameter. In general, the effective diameter of the object being imaged is defined as the diameter of a circle whose area is the same as the object projected. As a result, in our study since we are scanning a precisely circular cross section, the diameter of the phantom represents the effective diameter (32-cm for body and 16-cm for head phantom). These calculated conversion factors using Dw and the phantom effective diameter were multiplied by the measured CTDIvol to determine SSDE using equations 1.11 and 1.9 respectively. The percentage errors were calculated for each estimated dose compared to the measured AWTD. Different reconstruction algorithms were applied to the raw data from each of the acquired scans. The applied reconstructions algorithms were FBP both with and without SEMAR applied and AIDR 3D both with and without SEMAR applied for a total of four reconstructed image sets per acquired scan. The CT number and the area of the ROI were determined as previously described for each of the reconstructed image sets. Finally, a random sampling of the scans were chosen to perform a comparison of the manually calculated values of Dw, effective diameter and SSDE with that measured 32

44 by the Radimetrics dose monitoring software. Figure 2-9 shows an example of some of the parameters calculated by the Radimetrics software for an image series.. Figure 2-9. Example calculations by the Radimetrics software. 33

45 Chapter 3 Results and Discussion 3.1 CTDI vol Measurements Using the method described in the previous chapter, values of CTDIvol at 120 and 100 kvp were ± 0.05 and ± 0.04 mgy, respectively. The scanner reports values of 20.5 and 11.9 mgy for 120 and 100 kvp, respectively, using the technique factors in Table 2.1. The measured values of CTDIvol are acceptable as according to the American College of Radiology (ACR) criteria which indicates that the measured CTDIvol should be within 20% of the reported value by the scanner (ACR, 2012). 3.2 SSDE Determination and Comparison with the Measured Dose Effect of metal type on dose estimation Initial measurements were done with one rod of each metal type placed in the body phantom (at the 3 o clock hole of the periphery of the head part, see figure 2.2) at 120kVp, the chamber reading at the center and periphery were within ± 0.4 % and ±

46 %, respectively, for all three metal types which then resulted in ± 0.03 mgy for the measured AWTD. When estimating the SSDE as according to TG-204 no difference is expected with different metal type since the estimation of the SSDE204 is based on a fixed effective diameter. A minimal measured difference in the SSDE220 is observed between the three metal types as shown in Table 3.1. Also, a much larger error was measured using the TG- 204 method which gave an overestimation of the SSDE by not accounting for the attenuation of the metal whereas an error of less than 1% was measured with TG-220 methods of SSDE estimation. Table3.1. AWTD and SSDE values with the % error for the body phantom at 120kVp using one rod of different metal types. Metal D, center D, periphery AWTD Dw SSDE220 % Error SSDE204 % Error Type (mgy) (mgy) (mgy) (cm) (mgy) (mgy) SST % % TI % % Co-Cr % % Notice that all previous data were acquired using the body phantom where the metal rods represent a small percentage of the total body. The same method was performed using the head phantom at the same protocol to represent the effect of the metal with small body sizes (the metal rod was placed in the inner 6 o clock hole of the head phantom). As was found with the body phantom, the measured AWTD at the center 35

47 and periphery were within ± 1% and ±0.5%, respectively, for different metal types resulting in an average ± 0.17 mgy. The dose estimated using TG-204 will be the same for all type of metals. With the small phantom, TG-204 gave a better estimation of the doses (at one metal rod) as shown in Table 3.2 whereas underestimation with larger errors were measured when using TG-220 method which is due to a slight overestimation of the water equivalent diameter. Table3.2. AWTD and SSDE values with the % error for the head phantom at 120kVp using one rod of different metal types. Metal D, center D, periphery AWTD Dw SSDE % Error SSDE % Error Type (mgy) (mgy) (mgy) (cm) TG-220 TG-204 (mgy) (mgy) SST % % TI % % Co-Cr % % Since the three metals did not yield a significant difference on the measured AWTD or the calculated SSDE using the body and head phantom, we can conclude that SSDE calculation using TG-220 and TG-204 is independent of metal type for large and small body sizes at 120 kvp. 36

48 Effect of different reconstruction algorithms on dose estimation As mentioned previously, the estimation of the doses following TG-220 depends on the water equivalent diameter which can be calculated from the CT number (Hounsfield Unit). The CT number could be affected by the selected reconstruction algorithm. As a result, Dw and subsequent SSDE220 calculations for the body phantom were performed for the same images discussed in section The resulting data is presented in Table 3-3. The chart in Figure 3.1 depicts the percent difference between the SSDE220 for the different reconstruction algorithms as compared to AIDR 3D where AIDR 3D+S and FBP+S are the reconstruction with SEMAR. Similar results have been observed for the head phantom where the variations of SSDE220 relative to the standard reconstruction are within ±0.2% between the images reconstructed with and without metal artifact reduction algorithm (Figure3-2). 37

49 % Difference % Difference % % % AIDR 3D+S % % % % % % FBP Reconstruction Algorithm FBP+S Figure 3-1. Variation of SSDE with different reconstruction algorithms with and without application of SEMAR relative to AIDR 3D, using one metal rod of three types placed in the body phantom at 120kVp % % % % % FBP FBP+S % AIDR 3D+S % % % Reconstruction Algorithm Figure 3-2. Variation of SSDE with different reconstruction algorithms with and without application of SEMAR relative to AIDR 3D, using one metal rod of three types placed in the head phantom at 120kVp. As a result, we can conclude that SSDE calculation and therefore DW values are independent of the type of reconstruction algorithm applied. 38

50 Table 3.3. The average CT number, Dw and their standard deviation for each reconstruction algorithm at different metal type. Body phantom Type of reconstruction Mean CT number Mean Dw (cm) Standard Deviation of Dw AIDR 3D AIDR 3D+S FBP FBP+S Head Phantom AIDR 3D AIDR 3D+S FBP FBP+S Even though the SSDE were independent of the type of reconstruction applied, the severity of the artifact and the amount of noise were not. Images with FBP showed the highest noise and distortion around the metal rod. After applying, SEMAR, we can clearly see the improvement on the image quality in terms of metal artifact reduction with both AIDR 3D or FBP (Figure3-3 and 3-4). 39

51 Figure 3-3. On the left, the effect of the SST rod on image quality of an AIDR 3D reconstructed image where SST showed the most severe artifact compared to other metals used in the study. On the right the same image after applying SEMAR. Figure 3-4. On the left, the effect of the SST rod on image quality of a FBP reconstructed image where SST showed the most severe artifact compared to other metals used in the study. On the right the same image after applying SEMAR. 40

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