Simulating the approximate irregular field dose distribution in radiotherapy using an ultrasound tracking technique

Physica Medica 70 (2020) 19–27

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Original paper

Simulating the approximate irregular field dose distribution in radiotherapy using an ultrasound tracking technique

T

⁎

Lai-Lei Tinga, Ho-Chiao Chuangb, , Ai-Ho Liaoc,d, Chia-Chun Kuoa,e,f, Hsiao-Wei Yug, Cheng-Jia Yub, Der-Chi Tienb, Shiu-Chen Jenga,h, Jeng-Fong Chioua,g,i a

Department of Radiation Oncology, Taipei Medical University Hospital, Taipei, Taiwan Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan c Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan d Department of Biomedical Engineering, National Defense Medical Center, Taipei, Taiwan e Department of Radiation Oncology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan f School of Health Care Administration, College of Management, Taipei Medical University, Taipei, Taiwan g Taipei Cancer Center, Taipei Medical University, Taipei, Taiwan h School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan i Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan b

A R T I C LE I N FO

A B S T R A C T

Keywords: Irregular field dose distribution Ultrasound imaging tracking Respiration motion Radiochromic film Multileaf collimator opening

Purpose: This study used an ultrasound image tracking algorithm (UITA) in combination with a proposed simulation program for the approximate irregular field dose distribution (SPAD) to assess the feasibility of performing dose distribution simulations for two-dimensional radiotherapy. Methods: This study created five different types of multileaf collimator openings, and applied a SPAD to analyze the matrix position parameters for each regular field to generate a static program-simulation dose distribution map (PDDM), whose similarity was then compared with a static radiochromic film experimental-measurement dose distribution map (EDDM). A two-dimensional respiration motion simulation system (RMSS) was used to reproduce the respiration motion, and the UITA was used to capture the respiration signals. Respiration signals were input to the SPAD to generate two dynamic PDDMs, which were compared for similarity with the dynamic EDDM. Results: In order to verify the dose distribution between different dose measurement techniques, the gamma passing rate with 2%/2 mm criterion was used for the EDDM and PDDM, the passing rates were between 94.31% and 99.71% in the static field analyses, and between 84.45% and 96.09% for simulations with the UITA signal input and between 89.35% and 97.78% for simulations with the original signal input in the dynamic field analyses. Conclusions: Static and dynamic dose distribution maps can be simulated based on the proposed matrix position parameters of various fields and by using the UITA to track respiration signals during radiation therapy. The present findings indicate that it is possible to develop a reusable and time-saving dose distribution measurement tool.

1. Introduction Tumors in the thorax and abdomen (e.g., pancreas cancer, liver cancer, and lung cancer) receiving radiation therapy can move due to nonspontaneous movements such as the heartbeat, bloating, coughing, breathing, and bowel movement. This can result in the radiation dose not being accurately delivered to the lesion, which means that physicians must enlarge the scope of treatment, thereby potentially also

causing additional damage to surrounding normal tissue [1,2]. The related literature indicates that that respiration-induced tumor movements in thorax can be especially large near the diaphragm [3]. Seppenwoolde et al. [4] used different methods to observe three-dimensional lung tumor movements during radiation therapy, and found that the direction and distance of these movements varied with the tumor location. The average maximum amplitude in the right-left (RL), superior-inferior (SI), and anterior-posterior directions were

⁎ Corresponding author at: Department of Mechanical Engineering, National Taipei University of Technology, No. 1, Sec. 3, Chung-Hsiao E. Rd., Taipei 10608, Taiwan. E-mail address: [email protected] (H.-C. Chuang).

https://doi.org/10.1016/j.ejmp.2020.01.005 Received 29 July 2019; Received in revised form 6 January 2020; Accepted 6 January 2020 1120-1797/ © 2020 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

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Fig. 1. (a) Photography, (b) side-view drawing and (c) side-view photography of the experimental setup. a, LINAC; b, ultrasound imaging system; c, solid-water phantom; d, diaphragm phantom (ultrasound probe, universal chuck, and agarose); e, RMSS.

program for the approximate irregular field dose distribution (SPAD) in both static and dynamic situations, which was achieved with the aid of cooperation with the Department of Radiation Oncology at Taipei Medical University Hospital. This study analyzed various dosing parameters for different field sizes as the basis for simulations of the static approximate irregular field. Moreover, a previously developed respiration motion simulation system (RMSS) [17] and ultrasound image tracking algorithm (UITA) [18] were used for tracking and capturing the real-time respiration-motion signals (henceforth respiration signals). The original and UITA tracking signals were input to the SPAD to simulate the dose distribution for the tumor and its surroundings during radiation therapy. Finally, the experimental data were quantified and the dose distribution simulation effect of the combination of the SPAD, UITA, and RMSS was evaluated based on the distribution of the received dose as measured using radiochromic film (GAFchromic EBT3).

24.6 ± 3.8 mm (mean ± SD), 2.8 ± 0.3 mm, and 8.2 ± 0.5 mm, respectively. Kyriakou et al. [5] used computed tomography to observe the movements of lung tumors in six patients, and found that the correlation between the tumor movement in the SI direction and the diaphragm movement was stronger for tumors closer to the diaphragm. In order to ensure that patients receive the correct radiation dose and dose distribution as designed by the treatment planning system (TPS), these should be verified by a dose measurement tool before applying the treatment; the patient would then only be treated if the dose error is within the permissible range. Dose measurement tools are mainly divided into two types: electronic signal measurements and measurements of changes to the internal properties of materials. The electronic signal measurements involve converting the current signal generated by radiation through the electrode into dose data, such as using an ionization chamber [6,7] and diode dosimeter [8]. The measurements of changes to the internal properties of materials are similarly divided into three types: polymer gel dosimeters, thermoluminescent dosimeters (TLDs), and radiochromic films. When a polymer gel dosimeter is irradiated, monomers react with the free radicals formed by water splitting to form a polymerism phenomenon that is proportional to the irradiation dose [9,10]. TLDs release visible light after absorbing radiation energy, and so the intensity and energy of the radiation can be obtained by measuring visible-light signals [11,12]. Radiochromic films exhibit varying shades of color through the sensitivity of their internal diacetylene crystals to radiant energy [13]. Palmer et al. [14] pointed out that radiochromic films are often used to measure planar dose distributions due to their superior properties compared to other two-dimensional detectors such as ionization chamber arrays, diode dosimeters, and gel dosimeters. Such films are therefore widely used in many clinical practices as a reliable tool for patient quality assurance before treatment [15]. In order to verify the dose distribution between different dose measurement techniques, the gamma passing rate has been found to be an effective evaluation method. Consider two dose distributions, each dose point contains data of position and dose. Through the formula which can calculate whether each dose point passes the criterion. Finally, the gamma passing rate can be derived, which shows the correlation of the two dose distributions [16]. The main purpose of the present study was to develop a simulation

2. Materials and methods 2.1. Experimental equipment The experimental equipment in this study included the RMSS, a motion control card (PCI-7344, National Instruments), ultrasound imaging system (UF-4000, Fukuda Denshi), video capture card (CE310B, AVerMedia), and linear accelerator (LINAC; Elekta). The RMSS consisted of a servomotor, ball screw, linear slide, and acrylic sheet. The experimental phantom was divided into a diaphragm phantom and a solid-water phantom (Solid Water®, Sun Nuclear). A bucket was filled with agarose and the diaphragm phantom was constructed from a rubber belt, which was attached to the inner wall of the bucket to simulate the diaphragm and as a target for ultrasound imaging tracking. The EBT3 film was inserted into the solid-water phantom and used as a radiation dose delivery target, and dose distribution analysis was performed. The experimental software included the motion tracking software of the UITA, the motion control software of the RMSS, and the proposed SPAD. The experimental setup is shown in Fig. 1. The UITA [18] was used in this study to track the motion of the diaphragm phantom in real time. The UITA filters the captured image except the target using threshold image processing, and then locks it 20

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Fig. 2. UITA images: (a) original ultrasound image and (b) threshold image.

calibration curve was used to convert the optical density into a dose value, and then the size of each regular field was analyzed. This indicated that dose distributions increased approximately linearly from 20% to 80%, and so matrix position parameters of 20%, 50%, and 80% of each regular field were used as the basis for the SPAD.

with a small white square on the target to be tracked. A tracking trajectory is drawn for the small white square, and the respiration signal of the target is instantly recorded through LabVIEW, as shown in Fig. 2. The ultrasound tracking is only in the coronal plane. The ultrasound imaging system (UF-4000, Fukuda Denshi, Tokyo, Japan) used in this study is with a transducer probe in curvi-linear. The frequency of ultrasound transducer is 3 MHz with a frame rate of 36 fps and the axial resolution is less than 2 mm. The depth of the ultrasound image is the actual depth of the diaphragm detected by the ultrasound probe, and the actual depth of each image is different. The maximum depth of measurement for the ultrasound system is 24 cm.

2.4. Simulation program of the approximate irregular field dose distribution and static simulation This study used MatLab’s graphical user interface to develop the SPAD to simulate static and dynamic dose distributions. In order to make the process more user-friendly, the developed SPAD has an interface that allows the user to input numerical values and displays graphics of the field patterns, as shown in Fig. 4(a). In addition, in order to obtain a more accurate dose distribution map (DDM), the developed program simulated the dose at a scale of 1 mm = 5.9055 pixels, as shown in Fig. 4(b). In the static dose distribution simulation, the SPAD performed dose simulations based on the linear extrapolation equation and the previously obtained regular field position parameters, and the value of Doseproportion was adjusted accordingly:

2.2. Designed multileaf collimator openings of varying complexities This study designed approximate irregular static multileaf collimator (MLC) openings of various complexities using an MLC with a leaf width of 10 mm. The five types (A–E) of MLC openings, as shown in Fig. 3, varied according to the MLC opening area, the MLC opening perimeter, and the presence of separate MLC openings. The MLC complexity of the opening in each MLC type was changed by changing the size and shape of the leafs. In MLC types A and B the openings had a constant total circumference, with the area of type A gradually increasing and the area of type B gradually decreasing. In type C the MLC openings had a slightly reduced area and the total perimeter gradually increased. The MLC openings of types D and E had a constant total area, and their total perimeters gradually increased, with type E comprising some with separate MLC openings. The total circumference area and area in all types of MLC opening designs are shown in Table 1. The experiments involved simulating a two-dimensional dose distribution and experimental irradiation during radiation therapy through designing several approximate irregular field patterns. The principle underlying the design of the proposed approximate irregular field patterns was based on the study of Götstedt et al. [19].

Y = Y0 − Doseproportion

(1)

where Y is the SPAD-calculated new dose value, Y0 is the previous dose value obtained by the SPAD, and Doseproportion is the average dose per pixel for the regular field between 20% and 80%. 2.5. Respiration signals In order to simulate the dose distribution of the approximate irregular field when phantom motion occurs, the respiration signals of three volunteers and a set of sine wave signals were taken from the database. Human respiration pattern signals were recorded using the ultrasound system to observe the diaphragm motion in real time, and the UITA was adopted to determine the position of the diaphragm in the SI and RL directions, with its motion trajectory recorded using LabVIEW. The respiration signals were categorized into three pattern types: Ba, baseline shift; Sud, sudden transitions between fast and slow breathing, and Db, deep breathing. The sine wave signal had a frequency of 0.167 Hz and its amplitudes in the SI and RL directions were10 mm and 5 mm, respectively, as shown in Fig. 5(d). Furthermore, pattern Sud is a human nature respiratory pattern signals recorded by UITA (ultrasound image tracking algorithm). The Sud signal is not a regular pattern signal defined as fast to slow or slow to fast. Instead, it is recorded from the volunteer’s respiratory pattern signals, which shows an irregular frequency change in a certain period of time, and thus, it is different in the transition of the frequency change.

2.3. Experimental analysis of dosage parameters for regular field and radiation settings The EBT3 film was irradiated with a standard dose using a 6-MV LINAC with the following parameters: delivery dose = 100 cGy, dose rate = 100 MU/min, radiation field size = 2 cm × 2 cm to 10 cm × 10 cm, source-to-skin distance = 100 cm, and EBT3 film insert depth = 1.5 cm. After receiving the radiation dose, the EBT3 film was allowed to stand for 24–48 h, and then scanned into a TIF file by a calibrated scanner (image color resolution = 48 bits, spatial resolution = 150 dpi, and no color correction), and FilmQA Pro software (developed by GAFchromic) was used for further analysis. The dose 21

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Fig. 3. Five types of MLC opening designs.

and two phantoms) was mounted on a LINAC bed and the radiation source was adjusted to align the EBT3 film inside the solid-water phantom. The previously acquired respiration signals were input to the control program of the RMSS to drive that system, the upper diaphragm

2.6. Experimental method for simulating doses in the presence of respiration motion The experimental equipment (ultrasound imaging system, RMSS, 22

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Table 1 Total circumference area and area in all types of MLC opening designs. A-1 Circumference area Area

A-2

A-3

B-1

B-2

B-3

C-1

C-3

D-1

D-2

D-3

E-1

E-2

E-3

16 mm

16 mm

16 mm

32 mm

32 mm

32 mm

44 mm

48 mm

56 mm

32 mm

36 mm

40 mm

22 mm

32 mm

42 mm

12 mm2

13.2 mm2

14 mm2

60 mm2

52 mm2

48 mm2

94 mm2

90 mm2

84 mm2

44 mm2

44 mm2

44 mm2

30 mm2

30 mm2

30 mm2

2.8. Evaluation of the approximate irregular field dose distribution

phantom, and the solid-water phantom. The LINAC then directly delivered a dose of 100 cGy in different field patterns to the EBT3 films. Meanwhile, the ultrasound imaging system was used to monitor the movements of the tracking target in the SI and RL directions, and transmitted target motion images to the computer. We used our previously developed UITA to track the target movement and capture its motion signals, and then input the respiration signals (original input signal and UITA tracking signal) as the basic program-simulation dose distribution map (PDDM) data for the respiration motion simulation performed by the SPAD. The principle of dynamic dose distribution simulation performed by the SPAD is shown in Fig. 6. The previously simulated static PDDM was used as the reference dose distribution map (RDDM), and since it took about 90 s to perform the radiation dose transfer, the matrix data values of the RDDM were divided by 900, resulting in the RDDM spanning 0.1 s, and the dose superposition was performed in combination with different respiration signals to simulate the dynamic PDDM. Five field patterns were selected (A-3, B-3, C-1, D-1, and E-2) and combined with three human respiration signals, and one field pattern (E-1) was applied in combination with the sine wave signal for simulating the dynamic dose distribution.

EBT3 film was used as a verification tool for verifying the accuracy of the SPAD results. This study validated the SPAD results by comparing the experimental-measurement dose distribution map (EDDM) and PDDM under static and dynamic (i.e., respiration motion) states based on the gamma passing rate [16]. The gamma passing rate is a measure of the difference between two dose distribution profiles based on evaluating the relationship between dose and position. The screening values for parameters ΔDM/ΔdM used in this study (where DM is the dose tolerance and dM is the distance tolerance) were 5%/3 mm, 3%/3 mm, and 2%/2 mm. These values were chosen based on ICRU (International Commission on Radiation Units and Measurements) Report 42. Dose 5%/3 mm is the criterion where 5% is the dose tolerance, and 3 mm is the distance tolerance. The gamma index can be calculated through the Eqs. (3) and (5) which can determine whether each dose point passes the criterion or not. If the results equal or less than 1, means the dose point passes the criterion. The passing rate of all dose points is called gamma passing rate showing the correlation of the two dose distributions. Gamma index analysis was performed for each pixel of the static and dynamic EDDM and PDDM as well as for each pixel of the dynamic EDDM and PDDM during respiration motion, but dose values lower than 10% of the highest dose were not calculated [19,20], as shown in Eqs. (3) and (4). The matching-filter condition was considered as a pass; otherwise it failed, such as indicated in Eq. (5). This study compared the percentage of pixels passed—namely the gamma passing rate—as a verification result.

2.7. Definition of system delay time and tracking errors When simulating the respiration motion, the delay time of the ultrasound imaging system meant that the target could not be accurately tracked. The effects of the tracking error and delay time of the ultrasound imaging system were investigated by observing the target motion state during respiration motion and checking data transmission. The tracking error of the system was quantified based on the diaphragm phantom displacement detected by the ultrasound image, and the rootmean-square error (RMSE) was obtained from the RMSS motor encoder position signal (actual position) and the UITA-captured respiration signal (tracking position):

γ (rEBT 3) = min {Γ(rEBT 3, rSimulation )} ∀ (rSimulation ),

Γ(rEBT 3, rSimulation ) =

γ 2 (rEBT 3, rSimulation ) δ 2 (rEBT 3, rSimulation ) + , 2 2 ΔdM ΔDM

(4)

where r(rEBT 3, rSimulation ) is the distance difference between the EDDM and PDDM points, δ (rEBT 3, rSimulation ) is the dose difference between the EDDM and PDDM points, ΔdM is the distance tolerance (2 mm or 3 mm), and ΔDM is the dose tolerance (2%, 3%, or 5%). If

∑i = 1 (RMSSi − UITAi )2 n

(3)

where

n

RMSE =

C-2

(2)

where RMSSi is the RMSS motor encoder value at time point i (actual position), and UITAi is the UITA value at time point i (tracking position).

0 ≤ γ (rEBT 3) ≤ 1, calculation passes, γ (rEBT 3 ) > 1, calculation fails.

Fig. 4. SPAD for MLC type B-3: (a) MLC opening geometry input interface and (b) static dose simulation. 23

(5)

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Fig. 5. The respiration signals of the three volunteers (a–c) and the sine wave signal (d).

Fig. 6. Schematic diagram of the principle of dynamic dose distribution simulation performed by the SPAD.

3. Results

ultrasound imaging, image transmission, and UITA analysis all contributed a delay time that affected the simulation results. The ultrasound image sampling rate was 36 Hz and the total delay time of the ultrasound imaging system was 310 ms. Table 3 lists the total average errors of ultrasound tracking for the different respiration patterns. In addition to the sine wave, pattern Sud had the best tracking effect, and the RMSEs in the SI and RL directions were 3.99 ± 0.24 mm and 2.01 ± 0.12 mm, respectively. The main reason for this findings is that pattern Sud had a respiration signal with smaller changes in amplitude and the lowest frequency among the three respiration patterns. The RMSE was smaller in direction RL than in direction SI. This was due to that the amplitude being small in direction RL, and so the tracking effect was relatively good.

3.1. Static simulation of the gamma index Comparing the overall trends of the EDDM and PDDM revealed that the field patterns for the five types of MLC mostly exceeded 99% for gamma passing rates of 5%/3 mm and 3%/3 mm. Furthermore, after changing to the more-stringent 2%/2 mm condition, the passing rate was still almost 94%, as presented in Table 2. These findings indicate the high similarity between the EDDM and PDDM. 3.2. System delay and UITA tracking errors When the SPAD was used to simulate respiration motion, the 24

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points. These points had a gamma index of > 1, and all of the other points passed. The gamma passing rate of the three respiration patterns all exceeded 97%, representing that the similarity of the SPAD results was at least 97%. In addition, when the gamma evaluation condition was changed to the more-stringent 2%/2 mm condition, the similarity still exceeded 91%. The effectiveness of the developed SPAD was verified by using the original respiration signal (not tracked by the UITA) and combining with the field pattern for the dynamic field simulation; the results are presented in Table 5. Compared with the UITA tracking, the gamma passing rate exceeded 93% under the standard gamma index of 3%/ 3 mm, and was > 89% under the more-stringent 2%/2 mm condition.

Table 2 Similarity between the EDDM and PDDM. MLC type

Gamma passing rate (%) 5%/3 mm

3%/3 mm

2%/2 mm

A-1 A-2 A-3

98.71 99.79 99.98

98.56 99.64 99.98

94.67 95.84 98.81

B-1 B-2 B-3

99.89 99.36 99.97

99.56 99.41 99.97

97.28 94.31 99.71

C-1 C-2 C-3

99.96 99.72 99.87

99.95 99.69 99.84

99.39 97.18 98.57

D-1 D-2 D-3

99.82 99.91 99.94

99.41 99.78 99.89

97.57 97.67 98.95

E-1 E-2 E-3

100 99.99 99.89

100 99.97 99.79

98.33 98.82 98.05

4. Discussion In this study we designed MLC openings of various sizes with different irregularities and complexities. Only the field patterns (MLC openings) that performed in the static analysis with relatively good results in Gamma index analysis were suitable for further dynamic analysis. For example, in the pattern D series, we took the worse results (D1) in Gamma index analysis during the static analysis, to verify with E2 case. Because the total perimeter of case E2 and case D1 is the same but the area of two cases is different. The dynamics analysis results show that under the same perimeter, the smaller the field area pattern, the higher the similarity (Gamma index). GAFchromic EBT3 film was used as a planar dose verification tool to evaluate the feasibility of the two-dimensional SPAD results, due to its excellent spatial resolution (25 µm). The findings of static field simulations (see Table 2) revealed that linear extrapolation provided reliable dose simulations. For dynamic field simulations when the RMSS moves, the EDDM provided the optical density produced by the LINAC delivering a dose to the upper EBT3 film (not using UITA tracking). The respiration signals used by the PDDM were derived from the original respiration signals and the diaphragmatic phantom motion signals tracked by the UITA. The delay time of ultrasound imaging and the tracking error of UITA were found to directly affect the dynamic PDDM produced by the SPAD, as shown in Fig. 7. The rejection zone for the gamma passing rate is usually in the low-dose area because the tracking signal differs from the original signal, causing the dose produced by the SPAD to be superimposed on the wrong position, which causes a dose difference, which is expressed in the dose area. This study found that the gamma passing rate in the approximate irregular field distribution simulation for the 2%/2 mm gamma index exceeded 94%. The similarities between the two dynamic simulations (UITA tracking signals and original signals) exceeded 84% and 89%, respectively. Baeza et al. [21] developed a two-dimensional dose prediction model and verified it with a TPS and radiochromic film. Their dose analysis under two static fields for the lung when using the gamma index of 2%/2 mm yielded passing rates of 90.9%, 81.1%, 92%, and 90%. Vedelago et al. [9] used Monte-Carlo simulations for two different polymer gel dosimeters (Fricke, NIPAM) for two-dimensional dose verification for the gamma index of 2%/1 mm, with field size of 3 × 3 cm2 and 10 × 10 cm2. Their results showed that 98% of the analysis points met the quality requirements, which does not differ markedly from the results obtained in the present study. However, the advantages of our study include that the simulations were performed using an approximate irregular field and during dynamic respiration. This study used an ultrasound imaging system for real-time tracking of the internal organ motion, which is a noninvasive tracking method that does not involve the use of radiation. The monitoring system proposed by Mcnair et al. [22] directly obtained the instantaneous motion map of the internal structure of the patient by fluoroscopy. However, their monitoring method will cause the patient to receive an additional radiation dose and is considered an invasive method. Shah et al. [23] implanted a sensor near the tumor and used electromagnetic waves to detect its motion. Although their system also did not involve

Table 3 Total tracking error under different respiration patterns. Respiration pattern

RMSE (mm)

Ba Sud Db Sine wave

SI

RL

6.29 ± 0.09 3.99 ± 0.24 4.18 ± 0.24 2.82

3.05 ± 0.03 2.01 ± 0.12 2.05 ± 0.11 1.42

3.3. Dynamic simulation of the gamma index The dynamic field dose distribution simulations were performed using five field patterns combined with three different respiration patterns (UITA tracking), and evaluated based on the gamma passing rate. The results show that the gamma passing rate exceeded 91% under the standard gamma index of 3%/3 mm, where E-1_Sud was as high as 99.84%, as indicated in Table 4. Fig. 7 shows the gamma image results obtained by calculating the B-3 field pattern under the 3%/3 mm gamma evaluation condition for the three respiration patterns. The panels on the right hand side of Fig. 7 show the positions of the rejected Table 4 Similarity between the EDDM and PDDM for the UITA signals. MLC type_pattern

Gamma passing rate (%) 5%/3 mm

3%/3 mm

2%/2 mm

E-1_Sud

99.95

99.84

96.09

A-3_Ba A-3_Sud A-3_Db

97.95 98.35 96.54

96.94 97.79 96.06

91.82 92.75 93.36

B-3_Ba B-3_Sud B-3_Db

97.55 99.38 99.87

97.33 99.06 98.66

94.87 93.70 91.75

C-1_Ba C-1_Sud C-1_Db

92.35 96.03 95.63

91.83 94.8 94.75

85.04 84.45 87.37

D-1_Ba D-1_Sud D-1_Db

98.26 97.29 98.79

97.69 96.64 98.24

92.30 87.99 92.91

E-2_Ba E-2_Sud E-2_Db

99.37 99.13 99.52

98.68 98.81 99.02

91.64 91.49 93.35

25

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Fig. 7. Three-dimensional gamma comparisons between the SPAD results (UITA tracking) and the experimentally measured EBT3 film under the 3%/3 mm gamma index for (a) B-3_A, (b) B-3_B, and (c) B-3_C.

the use of radiation, implanting the sensor into the patient represents an invasive procedure. The present study used the SPAD with the UITA as a dose

verification tool during radiotherapy. As long as the dosing parameters of each regular field were obtained, the proposed dose simulation program can still be applied even when using different LINACs. 26

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Acknowledgments

Table 5 Similarity between the EDDM and PDDM for the input signals. MLC type_pattern

This work was supported by the National Taipei University of Technology and Taipei Medical University Hospital under Contract USTP-NTUT-TMU-108-03. The authors express their appreciation to the Taipei Medical University Hospital, Taiwan for providing financial support and facilities for this study.

Gamma passing rate (%) 5%/3 mm

3%/3 mm

2%/2 mm

E-1_Sud

99.98

99.86

97.78

A-3_Ba A-3_Sud A-3_Db

99.98 99.62 99.92

99.87 99.35 99.82

95.53 95.49 93.92

B-3_Ba B-3_Sud B-3_Db

99.54 99.99 99.91

99.30 99.95 99.00

96.91 96.64 94.84

C-1_Ba C-1_Sud C-1_Db

93.69 97.63 98.01

93.21 96.81 97.13

89.67 89.89 89.35

D-1_Ba D-1_Sud D-1_Db

99.61 99.28 99.55

99.23 98.59 99.19

94.09 91.12 93.91

E-2_Ba E-2_Sud E-2_Db

99.08 99.99 99.97

98.38 99.96 99.82

93.85 94.90 95.49

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Although the measurement accuracy in this study was not as high as in an ionization chamber, a two-dimensional dose simulation could still be performed. In addition, no complicated front-end work was required compared to using TLDs. In contrast to using a polymer gel dosimeter, the accuracy of our proposed method is not affected by environmental factors such as temperature. Moreover, the simulations performed in this study only took a few minutes, and the comparisons with the results obtained using EBT3 film indicated that they are repeatable. The main limitation of this study was related to the delay time of the ultrasound imaging system, since the associated tracking error means that the respiration signal input to the SPAD contains an error, which thus affects the accuracy of the SPAD results. In addition, this study tracked the matrix data points on the ultrasound image that had the highest intensity. A tracking position error will occur if the ultrasound image becomes blurred due to a rapid respiration signal (> 0.4 Hz). In the future, new algorithms should be developed or an ultrasound system with faster processing speed should be used to reduce the imaging latency. In addition, the dose field simulation program used in this study does not reveal the current dose distribution in real time. The future development of a real-time dose field simulation program could be combined with our previously developed RMCS to immediately display both the uncompensated and compensated dose distributions, making it easier for the radiologist to understand the current treatment status. 5. Conclusions This study simulated the radiation dose distribution using an ultrasound imaging system to monitor the movement of a diaphragm phantom and combined this with the UITA and SPAD. The simulated dose distribution was verified in experiments performed with EBT3 film, whose results show that the proposed method is feasible for dose simulations. The SPAD can simulate the two-dimensional dose distribution under various LINAC conditions, including static and dynamic DDMs, as long as matrix position parameters of 20%, 50%, and 80% of the regular field and the respiration signals measured by the UITA are used during treatment. Future improvements to the proposed simulation program combined with the EBT3 verification process will yield a reliable and rapid tool for measuring radiation dose distributions.

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