Ahmad Taufek Abdul Rahman

Review of doped silica glass optical fibre: Their TL properties and potential applications in radiation therapy dosimetry

Review is made of dosimetric studies of Ge-doped SiO(2) telecommunication fibre as a 1-D thermoluminescence (TL) system for therapeutic applications. To-date, the response of these fibres has been investigated for UV sources, superficial X-ray beam therapy facilities, a synchrotron microbeam facility, electron linear accelerators, protons, neutrons and alpha particles, covering the energy range from a few eV to several MeV. Dosimetric characteristics include, reproducibility, fading, dose response, reciprocity between TL yield and dose-rate and energy dependence. The fibres produce a flat response to fixed photon and electron doses to within better than 3% of the mean TL distribution. Irradiated Ge-doped SiO(2) optical fibres show limited signal fading, with an average loss of TL signal of ~0.4% per day. In terms of dose response, Ge-doped SiO(2) optical fibres have been shown to provide linearity to x and electron doses, from a fraction of 1 Gy up to 2 kGy. The dosimeters have also been used in measuring photoelectron generation from iodinated contrast media; TL yields being some 60% greater in the presence of iodine than in its absence. The review is accompanied by previously unpublished data.

Effective Atomic Number of Ge-Doped and Al-Doped Optical Fibers for Radiation Dosimetry Purposes

Optical fibers have been demonstrated by this group to show promising thermoluminescence (TL) properties with respect to ionizing radiation. Present research has focused on commercially produced single-mode telecommunication optical fibers manufactured by CorActive (Canada) either in the form of SiO2 optical fibers doped with either Ge or Al. Control of radiation dose is essential in performing an experiment in a biomedical context. One important aspect in this is the tissue equivalence of the dosimetric material. Mixtures or compounds that are similar in their radiation interaction characteristics to the soft tissue, bone or any other body constituents can be identified for this purpose. Effective atomic number of a medium prescribes its detection efficiency and tissue equivalence. To obtain the effective atomic number of the doped fibers, SEM (Scanning Electron Microscope) and EDXRS (Energy Dispersive X-ray Spectroscopy) analysis was performed to acquire the composition of the element inside the optical fibers. From our investigation, the value of Zeff is in the range of 11.5-13.4 and 11.7-13.7 for Ge-doped and Al-doped respectively (value of Zeff in soft tissue is 7.5 and Zeff for bone is between 11.6-13.8). Given that Ge- and Al-doped optical fibers are not soft-tissue equivalent, the assessment of dose deposition in such media would need to be corrected for an expected over-response. However, the value of Zeff is within the range of bone, making the optical fiber a strong candidate for use in skeletal radiation dosimetry.

Analysis of Photon Scattering Trends for Material Classification Using Artificial Neural Network Models

In this project, we concentrate on using the Artificial Neural Network (ANN) approach to analyze the photon scattering trend given by specific materials. The aim of this project is to fully utilize the scatter components of an interrogating gamma-ray radiation beam in order to determine the types of material embedded in sand and later to determine the depth of the material. This is useful in a situation in which the operator has no knowledge of potentially hidden materials. In this paper, the materials that we used were stainless steel, wood and stone. These moderately high density materials are chosen because they have strong scattering components, and provide a good starting point to design our ANN model. Data were acquired using the Monte Carlo N-Particle Code, MCNP5. The source was a collimated pencil-beam projection of 1 MeV energy gamma rays and the beam was projected towards a slab of unknown material that was buried in sand. The scattered photons were collected using a planar surface detector located directly above the sample. In order to execute the ANN model, several feature points were extracted from the frequency domain of the collected signals. For material classification work, the best result was obtained for stone with 86.6% accurate classification while the most accurate buried distance is given by stone and wood, with a mean absolute error of 0.05.