Kimia Gholami

In this article, we investigate quantum entanglement (QE) filtering to address the challenges in multi-isotope positron emission tomography (PET) or in PET studies utilizing radiotracers with dual-positron and prompt gamma emissions. Via GATE simulation, we demonstrate the efficacy of QE filtering using a one-of-a-kind cadmium zinc telluride (CZT) PET system. establishing its viability as a multimodal scanner and ability to perform QE filtering. We show the high Compton scattering probability in this CZT-based scanner with 44.2% of gammas undergoing a single scatter prior to absorption. Additionally, the overall system sensitivity as a standard PET scanner (11.29%), QE-PET scanner (6.81%), and Compton Camera (11.29%) is quantified. Further, we find a 23% decrease in the double Compton scatter (DCSc) frequency needed for QE filtering for each mm decrease in crystal resolution and an increase in mean absolute error (MAE) of their δθs from 6.8∘ for 1 mm resolution to 9.5∘, 12.2∘, and 15.3∘ for 2, 4, and 8 mm resolution, respectively. These results reinforce the potential of CZT detectors to lead next generation PET systems taking full advantage of the QE information of positron annihilation photons.

Stress evaluation plays a pivotal role in the design of material systems, often accomplished through the finite element method (FEM) for intricate structures. However, the substantial costs and time requirements associated with multi-scale FEM analyses have prompted a growing interest in adopting more efficient, machine-learning-driven strategies. This study investigates the utilization of advanced machine learning techniques for predicting local stress fields in composite materials, presenting it as a superior alternative to traditional FEM approaches. The primary objective of this research is to develop a predictive model for stress field maps in composite components featuring diverse configurations of fibers distributed within the matrix. To achieve this, we employ a Convolutional Neural Network (CNN) with a specialized U-Net architecture, enabling the correlation of spatial fiber organization with the resultant von Mises stress field. The CNN model was extensively trained using four distinct data sets, encompassing uniform fibrous structures, non-uniform fibrous structures, irregularly shaped fibrous structures, and a comprehensive combination of these data sets. The trained U-Net models demonstrate exceptional proficiency in predicting von Mises stress fields, yielding impressive structural similarity index scores (SSIM) of 0.977 and mean squared errors (MSE) of 0.0009 on a dedicated test set. This research harnesses 2D cross-sectional imagery to establish a surrogate model for finite element analysis, offering an accurate and efficient approach for predicting stress fields in composite material design, irrespective of geometric complexity or boundary conditions.

Extracting the mechanical properties of a composite hydrogel; e.g., bioglass (BG)–collagen (COL), is often difficult due to the complexity of the experimental procedure. BGs could be embedded in the COL and thereby improve the mechanical properties of COL for bone tissue engineering applications. This paper proposed a deep-learning-based approach to extract the mechanical properties of a composite hydrogel directly from the microstructural images. Four datasets of various shapes of BGs (9000 2D images) generated by a finite element analysis showed that the deep neural network (DNN) model could efficiently predict the mechanical properties of the composite hydrogel, including the Young’s modulus and Poisson’s ratio. ResNet and AlexNet architecture were tuned to ensure the excellent performance and high accuracy of the proposed methods with R-values greater than 0.99 and a mean absolute error of the prediction of less than 7%. The results for the full dataset revealed that AlexNet had a better performance than ResNet in predicting the elastic material properties of BGs-COL with R-values of 0.99 and 0.97 compared to 0.97 and 0.96 for the Young’s modulus and Poisson’s ratio, respectively. This work provided bridging methods to combine a finite element analysis and a DNN for applications in diverse fields such as tissue engineering, materials science, and medical engineering.