Program Information
New Tumor Modeling Using 3D Gel Dosimeter for Brain Stereoctactic Radiotherpy (SRT)
K Chang*, M Kim , J Kwak , S Kim , Y Ji , B Cho , S Yoon , S Lee , Asan Medical Center, Seoul, Seoul
Presentations
SU-F-T-580 (Sunday, July 31, 2016) 3:00 PM - 6:00 PM Room: Exhibit Hall
Purpose:The purpose of this study is to develop new tumor model using 3D printing with 3D dosimeter for brain stereoctactic radiotherpy (SRT).
Methods:BANG³ polymer gel was prepared and the gel-filled glass vials were irradiated with a 6 MV photon beam to acquire the calibration curve that present the change of R2 (1/T₂) value with dose. Graded doses from 0 to 12 Gy with an interval of 2 Gy were delivered. A kit for calibration of gel dosimeter and an 2 tumor model phantoms with a spherical shape were produced using a 3D printer with a polylactic acid after its 3D images were created using Autodesk software. 3D printed tumor phantoms and EBT3 films were irradiated to compare with treatment plan. After irradiation, vials for calibration and tumor model phantoms were scanned at 9.4T MRI. The spin-spin relaxation times (T₂) according to the each dose were calculated to evaluate the dose response. Acquired images were analyzed using an ImageJ. Scanned MRI images of tumor models were transferred treatment planning system and these were registered to the CT images. In all treatment plans, two arc plans (CW and CCW) were designed to deliver 50 Gy for 10 fractions. For first PTV, treatment plan was accurately designed that 95% of dose to cover 100% of PTV. But 2nd PTV was not intentionally cover 100% of PTV to evaluate the intensity of delivered tumor phantom with polymer gel. We compared the 3D dose distributions obtained from measurements with the 3D printed phantom and calculated with the TPS.
Results: 3D printed phantom with a polymer gel was successfully produced. The dose distributions showed qualitatively good agreement among gel, film, and RTP data.
Conclusion: A hybrid phantom represents a useful to validate the 3D dose distributions for patient-specific QA.
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