کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8250396 | 1533384 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A convenient verification method of the entrance photo-neutron dose for an 18Â MV medical linac using silicon p-i-n diodes
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
فیزیک و نجوم
تشعشع
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: A convenient verification method of the entrance photo-neutron dose for an 18Â MV medical linac using silicon p-i-n diodes A convenient verification method of the entrance photo-neutron dose for an 18Â MV medical linac using silicon p-i-n diodes](/preview/png/8250396.png)
چکیده انگلیسی
Electron Linear Accelerators (linacs) used in radiotherapy treatments produce undesired photo-neutrons when they are operated at energies above 10Â MeV. (Neutron Contamination from Medial Electron Accelerators, 1984). These photo-neutrons contaminate the therapeutic beam and increase dose equivalent delivered to patients. In this work, the neutron entrance dose for an 18Â MV Varian Medical linac was measured using passive silicon p-i-n diodes. These detectors were calibrated in separate photon, electron and neutron fields. The silicon p-i-n diode detectors have shown excellent discrimination between fast neutron and photon radiation, with sensitivity to fast neutrons being â4000 times higher than to photons from a 60Co source in terms of absorbed dose to tissue. The neutron tissue absorbed dose was studied both on the surface and inside a cubic solid water phantom, both experimentally and also using Geant4 Monte Carlo simulations. The silicon p-i-n diodes were found to be useful for quick estimation of the fast neutron tissue dose and dose equivalent in pulsed, mixed radiation fields produced by a medical linac.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Radiation Measurements - Volume 106, November 2017, Pages 391-398
Journal: Radiation Measurements - Volume 106, November 2017, Pages 391-398
نویسندگان
Vanja Gracanin, Susanna Guatelli, Dean Cutajar, Iwan Cornelius, Linh T. Tran, David Bolst, Rhys Preston, Rashmi Gupta, Johnson Yuen, Marco Petasecca, Michael Lerch, Vladimir Prevertaylo, Anatoly Rosenfeld,