Utilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessment.

Purpose: This study aimed to evaluate Artificial Neural Network (ANN) modeling to estimate the significant dose length product (DLP) value during the abdominal CT examinations for quality assurance in a retrospective, cross-sectional study. Methods: The structure of the ANN model was designed considering various input parameters, namely patient weight, patient size, body mass index, mean CTDI volume, scanning length, kVp, mAs, exposure time per rotation, and pitch factor. The aforementioned examination details of 551 abdominal CT scans were used as retrospective data. Different types of learning algorithms such as Levenberg-Marquardt, Bayesian and Scaled-Conjugate Gradient were checked in terms of the accuracy of the training data. Results: The R-value representing the correlation coefficient for the real system and system output is given as 0.925, 0.785, and 0.854 for the Levenberg-Marquardt, Bayesian, and Scaled-Conjugate Gradient algorithms, respectively. The findings showed that the Levenberg-Marquardt algorithm comprehensively detects DLP values for abdominal CT examinations. It can be a helpful approach to simplify CT quality assurance. Conclusion: It can be concluded that outcomes of this novel artificial intelligence method can be used for high accuracy DLP estimations before the abdominal CT examinations, where the radiation-related risk factors are high or risk evaluation of multiple CT scans is needed for patients in terms of ALARA. Likewise, it can be concluded that artificial learning methods are powerful tools and can be used for different types of radiation-related risk assessments for quality assurance in diagnostic radiology.

New approach to removal of hazardous Bypass Cement Dust (BCD) from the environment: 20Na2O-20BaCl2-(60-x)B2O3-(x)BCD glass system and Optical, mechanical, structural and nuclear radiation shielding competences.

In the present work, aiming to collaborate in the removal of Bypass Cement Dust (BCD) from the environment, we studied a system consisting of three glasses prepared from analytical reagent grade chemicals with the following composition: 20Na2O-20BaCl2-(60-x)B2O3-xBCD, where (x = 0, 10, and 20 %). BCD is an important contributor of many respiratory human health issues. In this work we investigate their optical, physical and gamma-ray shielding properties. The experimental results of mass attenuation coefficients are contrasted with the FLUKA Monte Carlo code and the XCOM database at 0.081, 0.356, 0.662, 1.173, and 1.332 MeV photon energies. Additionally, the mechanical, structural, and optical properties of these glasses were measured. A rising peak with an increase of BCD concentration in the region from 450 cm-1 to 480 cm-1 was observed. The results show that shielding properties such as the mass attenuation coefficient (µm), the effective atomic number (Zeff), and the effective electron density (Nel) increase as BCD fraction increases. The half value layer (HVL), the tenth value layer (TVL), and the mean free path (MFP) decrease as the BCD content increases. It is noticed that 20Na2O-20BaCl2-(60-x)B2O3-xBCD, where (x = 0, 10, and 20 %), has the highest optical conductivity value at x = 20%. It was found that the gradual addition of BCD content increases the hardness of the studied glasses.