In order to establish diagnostic reference levels (DRLs) for multi-detector computed tomography (MDCT), four routine CT examinations were identified and a computer program was developed to collect data from 19 MDCT scanners in Iran. Mean values of Volume computed tomography dose index (CTDIvol) and dose-length product (DLP) in each site were calculated and the DRLs were defined as the 75th percentile of the distribution of the CTDIvol/DLP values for each examination. In terms of DLP, the DRLs of adult age group are 700, 290, 330, and 550 mGy cm for the Brain, Sinus, Chest, and Abdomen and Pelvis protocols, respectively. Although DRLs of this study are comparable to other international DRLs and in most cases are less than the international reference values, the great extent of dose distributions indicates that the CT imaging procedures in Iran should be optimized by applying diagnostic reference levels in order to decrease the radiation dose to patient undergoing CT examination.

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The neural network method has been used for the unfolding of neutron spectra in neutron spectrometry by Bonner spheres. A back propagation algorithm was used for training of neural networks. 4 mm x 4 mm bare LiI (Eu) and in a polyethylene sphere set: 2, 3, 4, 5, 6, 7, 8, 10, 12, 18 inch diameter have been used for unfolding of neutron spectra. Neural networks were trained by 199 sets of neutron spectra, which were subdivided into 6, 8, 10, 12, 15 and 20 energy bins and for each of them an appropriate neural network was designed and trained. The validation was performed by the 21 sets of neutron spectra. A neural network with 10 energy bins which had a mean value of error of 6% for dose equivalent estimation of spectra in the validation set showed the best results. The obtained results show that neural networks can be applied as an effective method for unfolding neutron spectra especially when the main target is neutron dosimetry.

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