Adaptive calibration of Gafchromic EBT3 film using generalized additive neural networks

被引:0
作者
Chang, Liyun [1 ]
Chen, Pang-Yu [2 ]
Yeh, Shyh-An [1 ,3 ]
Kang, Chen-Lin [4 ,5 ]
Su, Chun-Ting [1 ,3 ]
Lee, Tsair-Fwu [6 ,7 ,8 ]
机构
[1] I Shou Univ, Dept Med Imaging & Radiol Sci, Kaohsiung, Taiwan
[2] Sinlau Christian Hosp, Dept Radiat Oncol, Tainan, Taiwan
[3] Eda Hosp, Dept Radiat Oncol, Kaohsiung, Taiwan
[4] Chang Gung Univ, Coll Med, Kaohsiung Chang Gung Mem Hosp, Dept Radiat Oncol, Kaohsiung, Taiwan
[5] Shu Zen Jr Coll Med & Management, Dept Med Imaging & Radiol, Kaohsiung, Taiwan
[6] Natl Kaohsiung Univ Sci & Technol, Med Phys & Informat Lab Elect Engn, Kaohsiung 80778, Taiwan
[7] Kaohsiung Med Univ, Grad Inst Clin Med, Kaohsiung 807, Taiwan
[8] Kaohsiung Med Univ, Dept Med Imaging & Radiol Sci, Kaohsiung 80708, Taiwan
关键词
Radiation dosimetry; Gafchromic film; Quality assurance; Neural network; Film dose calibration; FLAT-BED SCANNER; PDD METHOD; ENERGY-DEPENDENCE; DOSIMETRY SYSTEM; DOSE-RESPONSE; PHOTON; ELECTRON; RANGE; UNCERTAINTY; ACCURACY;
D O I
10.1038/s41598-025-92568-7
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
In radiation therapy, precise dose distribution is essential for minimizing damage to normal tissues. Gafchromic EBT3 film is widely used to assure the quality of two-dimensional dosimetry but requires frequent recalibrations due to changes in sensitivity over time. This study presents a new calibration method using a Keras-based generalized additive neural network (GANN) to address film aging. EBT3 films from four lots were calibrated with a 6 MV photon beam and scanned on Epson scanners. The GANN method achieved percentage differences between calibrated and delivered doses within 5%, comparable to traditional recalibration methods, with an overall uncertainty of approximately 2%. It demonstrated improved stability and accuracy, significantly reducing the need for frequent recalibrations and providing a robust solution for long-term film dosimetry.
引用
收藏
页数:11
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