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RISS 인기검색어
- 검색키워드
- 저자 : Goh YoungMoon (검색결과 4 건)
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Dosimetric Impact of Ti Mesh on Proton Beam Therapy
Cho, Shinhaeng,Goh, Youngmoon,Kim, Chankyu,Kim, Haksoo,Jeong, Jong Hwi,Lim, Young Kyung,Lee, Se Byeong,Shin, Dongho Korean Society of Medical Physics 2017 의학물리 Vol.28 No.4
When a high density metallic implant is placed in the path of the proton beam, spatial heterogeneity can be caused due to artifacts in three dimensional (3D) computed tomography (CT) scans. These artifacts result in range uncertainty in dose calculation in treatment planning system (TPS). And this uncertainty may cause significant underdosing to the target volume or overdosing to normal tissue beyond the target. In clinical cases, metal implants must be placed in the beam path in order to preserve organ at risk (OARs) and increase target coverage for tumors. So we should introduce Ti-mesh. In this paper, we measured the lateral dose profile for proton beam using an EBT3 film to confirm dosimetric impact of Ti-mesh when the Ti-mesh plate was placed in the proton beam pathway. The effect of Ti-mesh on the proton beam was investigated by comparing the lateral dose profile calculated from TPS with the film-measured value under the same conditions.
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Jaeha Lee,Yeon Joo Kim,Youngmoon Goh,Eunyeong Yang,Ha Un Kim,Si Yeol Song,Young Seok Kim 대한방사선종양학회 2023 Radiation Oncology Journal Vol.41 No.3
Purpose: Surface-guided radiation therapy is an image-guided method using optical surface imaging that has recently been adopted for patient setup and motion monitoring during treatment. We aimed to determine whether the surface guide setup is accurate and efficient compared to the skin-marking guide in prostate cancer treatment. Materials and Methods: The skin-marking setup was performed, and vertical, longitudinal, and lateral couch values (labeled as “M”) were recorded. Subsequently, the surface-guided setup was conducted, and couch values (labeled as “S”) were recorded. After performing cone-beam computed tomography (CBCT), the final couch values was recorded (labeled as “C”), and the shift value was calculated (labeled as “Gap (M-S),” “Gap (M-C),” “Gap (S-C)”) and then compared. Additionally, the setup times for the skin marking and surface guides were also compared. Results: One hundred and twenty-five patients were analyzed, totaling 2,735 treatment fractions. Gap (M-S) showed minimal differences in the vertical, longitudinal, and lateral averages (-0.03 cm, 0.07 cm, and 0.06 cm, respectively). Gap (M-C) and Gap (S-C) exhibited a mean difference of 0.04 cm (p = 0.03) in the vertical direction, a mean difference of 0.35 cm (p = 0.52) in the longitudinal direction, and a mean difference of 0.11 cm (p = 0.91) in the lateral direction. There was no correlation between shift values and patient characteristics. The average setup time of the skin-marking guide was 6.72 minutes, and 7.53 minutes for the surface guide. Conclusion: There was no statistically significant difference between the surface and skin-marking guides regarding final CBCT shift values and no correlation between translational shift values and patient characteristics. We also observed minimal difference in setup time between the two methods. Therefore, the surface guide can be considered an accurate and time-efficient alternative to skin-marking guides.
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Feasibility of a Linear Diode Array Detector for Commissioning of a Radiotherapy Planning System
Hong Seung Mo,Lee Uiseob,Kim Sung-woo,Goh Youngmoon,Park Min-Jae,Jeong Chiyoung,Kwak Jungwon,조병철 한국의학물리학회 2023 의학물리 Vol.34 No.1
Purpose: Although ionization chambers are widely used to measure beam commissioning data, point-by-point measurements of all the profiles with various field size and depths are time- consuming tasks. As an alternative, we investigated the feasibility of a linear diode array for commissioning a treatment planning system. Methods: The beam data of a Varian TrueBeam® radiotherapy system at 6 and 10 MV with/without a flattening filter were measured for commissioning of an Eclipse Analytical Anisotropic Algorithm (AAA) ver.15.6. All of the necessary beam data were measured using an IBA CC13 ionization chamber and validated against Varian “Golden Beam” data. After validation, the measured CC13 profiles were used for commissioning the Eclipse AAA (AAACC13). In addition, an IBA LDA-99SC linear diode array detector was used to measure all of the beam profiles and for commissioning a separate model (AAALDA99). Finally, the AAACC13 and AAALDA99 dose calculations for each of the 10 clinical plans were compared. Results: The agreement of the CC13 profiles with the Varian Golden Beam data was confirmed within 1% except in the penumbral region, where ≤2% of a discrepancy related to machine- specific jaw calibration was observed. Since the volume was larger for the CC13 chamber than for the LDA-99SC chamber, the penumbra widths were larger in the CC13 profiles, resulting in ≤5% differences. However, after beam modeling, the penumbral widths agreed within 0.1 mm. Finally the AAA LDA99 and AAACC13 dose distributions agreed within 1% for all voxels inside the body for the 10 clinical plans. Conclusions: In conclusion, the LDA-99SC diode array detector was found to be accurate and efficient for measuring photon beam profiles to commission treatment planning systems.
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Park Min-Jae,Lee Gyemin,Kwak Jungwon,Jeong Chiyoung,Goh YoungMoon,Kim Sung-woo,Cho Byungchul,Song Si Yeol,Kim Jong Hoon,Jung Jinhong,Shin Young Seob,Oh Jungsu 한국물리학회 2024 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.84 No.9
To enhance effi ciency and minimize errors, we automated the quality assurance (QA) process in radiation oncology, specifi - cally laser localization. Additionally, we explored the use of a convolutional neural network (CNN) to enhance the detection of small cube-ball phantoms in noisy images. Laser localizations were measured manually on the acquired images. To automate the QA workfl ow, we developed a Linux server equipped with database and web servers. Digital Imaging and Communications in Medicine (DICOM) fi les were retrieved 40 times for 10 linear accelerators (LINACs). The center of the cube-ball phantoms was estimated through Gaussian fi tting. We applied CNN using 6,968 stored results to improve the estimation performance in noisy megavoltage (MV) images. Subsequently, both analysis time and accuracy were compared. Our hospital has been employing the automated QA system since 2018, notably reducing the time for laser localization from 30 min to just 1 min. The average and standard deviation (SD) of inter-observer variability in the X- and Y-axes were 0.06 ± 0.01 mm and 0.05 ± 0.01 mm, respectively. Absolute diff erences between manual assessment and Gaussian fi tting presented average and SD values of 0.40 ± 0.51 mm and 0.23 ± 0.24 mm, respectively. In contrast, absolute diff erences between manual assessment and CNN presented average and SD values of 0.12 ± 0.10 mm and 0.11 ± 0.09 mm, respectively. Overall, the automated QA system signifi cantly hastened procedures in our large hospital and improved the estimation of the cube-ball phantom’s position in noisy images through deep learning.
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