Various researchers who have carried out national and international surveys have reported wide variations in patient dose arising from specific X-ray examinations. This study was carried out as a part of a comprehensive project to establish national diagnostic reference level (NDRL), for the first time, in Iran. Seven most common X-ray examinations in 11 projections were included. Thermo luminescence dosimeters (TLD-100) were used to measure entrance surface doses (ESDs). The study group consisted of 535 patients who were referred for X-ray examinations to 12 randomly selected public hospitals in Tehran County. Minimum, median, mean, maximum, first and third quartile values of ESDs are reported. Our results are evident that mean dose values of patients undertaking a specific examination are widely different in various hospitals. Wide dose differences may emerge from complex causes, but in general, low peak kilovoltage and high milli Amperes are associated with higher doses. The results of this work together with further data expected to emerge from the work in progress will provide a useful base to establish Iran's DRLs.

The national diagnostic reference levels (NDRLs) form an efficient, concise and powerful standard for optimising the radiation protection of a patient. With an aim to establish the first Iranian NDRLs, the nationwide survey of the frequency of medical X-ray examinations and entrance surface doses received by the patients during the most typical X-ray examinations has been performed. Seven most common X-ray examinations in 14 projections were included in the list of procedures under consideration. Hospitals of different sizes and levels using different X-ray machines were represented in the survey. The standard thermoluminescence dosimeters were applied to measure entrance surface dose (ESD). A total of 1601 patients were included in this study. The data were analysed statistically, and the minimum, median, mean, maximum, first and third quartile values of ESDs are reported. Finally, the proposed Iranian DRLs are compared with the international reference dose values reported by the European Commission, the International Atomic Energy Agency and the National Radiological Protection Board.

Interventional procedures, cine acquisitions and operation of fluoroscopic equipment in high-dose fluoroscopic modes, involve long fluoroscopic times which can lead to high staff doses. Also, Coronary angiography (CA) procedures require the cardiologist and assisting personnel to remain close to the patient, which is the main source of scattered radiation. Thus, radiation exposure is a significant concern for radiation workers and it is important to measure the radiation doses received by personnel and evaluate the parameters concerning total radiation burden. In this research, we investigated radiation doses to 10 cardiologists performing 120 CA procedures. Using thermo luminescent dosimeters doses to the wrists, thyroid and eyes per procedure were measured. Based on the measured dose values, maximum doses to the Left wrist, Right wrist, thyroid and eyes of cardiologist were measured 241.45 µSv, 203.17 µSv, 78.21 µSv and 44.58 µSv, respectively. The results of this study indicate that distance from the source, use of protective equipment’s, procedure complexity, equipment performance, and cardiologist experience are the principal exposure-determining variables. It can be conclude that if adequate radiation protection approaches have been implemented, occupational dose levels to cardiologists would be within the regulated acceptable dose limits.

This study was designed to evaluate the effect of the radiological technologists’ training on optimising the eye lens dose in brain computed tomography (CT) examinations. The lens dose of 50 adult patients was measured using thermoluminescent dosimeters before and after technologists’ training. Dose values of lenses, dose length product (DLP), volumetric CT dose index (CTDIvol) as well as image quality in terms of quantitative (contrast to noise ratio and signal to noise ratio) and subjective (artefact) parameters were compared before and after training. Lens dose values were 31.57 ± 9.84 mGy and 5.36 ± 1.53 mGy before and after training, respectively, which was reduced by ~83% (p < 0.05). The values of DLP, CTDIvol and image quality parameters were not significantly different (p > 0.05) and all images were diagnostically acceptable. Excluding the orbits from the scanning range is an efficient approach to optimize the lens dose; the training of the technologists has also a pivotal role in dose reducing.

We investigate the accuracy of direct attenuation correction (AC) in the image domain for myocardial perfusion SPECT (single-photon emission computed tomography) imaging (MPI-SPECT) using residual (ResNet) and UNet deep convolutional neural networks. MPI-SPECT 99mTc-sestamibi images of 99 patients were retrospectively included. UNet and ResNet networks were trained using non-attenuation-corrected SPECT images as input, whereas CT-based attenuation-corrected (CT-AC) SPECT images served as reference. Chang’s calculated AC approach considering a uniform attenuation coefficient within the body contour was also implemented. Clinical and quantitative evaluations of the proposed methods were performed considering SPECT CT-AC images of 19 subjects (external validation set) as reference. Image-derived metrics, including the voxel-wise mean error (ME), mean absolute error, relative error, structural similarity index (SSI), and peak signal-to-noise ratio, as well as clinical relevant indices, such as total perfusion deficit (TPD), were utilized. Overall, AC SPECT images generated using the deep learning networks exhibited good agreement with SPECT CT-AC images, substantially outperforming Chang’s method. The ResNet and UNet models resulted in an ME of −6.99 ± 16.72 and −4.41 ± 11.8 and an SSI of 0.99 ± 0.04 and 0.98 ± 0.05, respectively. Chang’s approach led to ME and SSI of 25.52 ± 33.98 and 0.93 ± 0.09, respectively. Similarly, the clinical evaluation revealed a mean TPD of 12.78 ± 9.22% and 12.57 ± 8.93% for ResNet and UNet models, respectively, compared to 12.84 ± 8.63% obtained from SPECT CT-AC images. Conversely, Chang’s approach led to a mean TPD of 16.68 ± 11.24%. The deep learning AC methods have the potential to achieve reliable AC in MPI-SPECT imaging.