## 1 Introduction
Techniques such as the Direct Fourier Transform [8] and the Filtered Back-projection Method [9] are included in this category. These methods are effective when the irradiation rays do not experience diffraction and data are abundant, as they require a high number of directions and rays to produce high-resolution images. Algebraic methods approach reconstruction as a linear system problem, where the object's cross-section is represented by an array of unknowns derived from measured projections. In this sense, these methods are advantageous when data are limited, making them suitable for scenarios with reduced projections, low resolution, or complex geometries. Regularization techniques are often incorporated in order to improve image quality. Among the most widely studied and utilized methods are the Algebraic Reconstruction Technique (ART) [10] and Simultaneous Iterative Reconstruction Technique (SIRT), [11] along with their numerous variants detailed in the literature such as the Controlled Algebraic Reconstruction Technique (CART) [12] and Simultaneous Algebraic Reconstruction Technique (SART) [13]. However, they have the main limitation of a high computational cost when large reconstructions are required [14; 15]. Additionally, parameters such as the relaxation rate or the number of iterations can affect the stability and accuracy of the results from these methods, and it is not always easy to determine the optimal values [16]. Advancements in machine learning have opened new avenues for tomographic reconstruction, with artificial neural networks (ANNs) playing a prominent role. Generally, two principal approaches to neural network integration have been identified. In the first approach, post-processing enhancement, ANNs are employed to refine reconstructed images and mitigate artifacts produced by conventional reconstruction algorithms [17]. For instance, a Convolutional Neural Network (CNN) model was used in [18] to detect cracks and faults in tomographic reconstructions. Similarly, in [19], a CNN was trained to enhance reflection tomography images obtained via idealized back-projection. Moreover, U-Net architecture has been applied to the segmentation of reconstructed images [18; 19]. The second approach, end-to-end reconstruction, involves the development of neural network models that process raw projection data to directly generate reconstructed images. For example, [PERSON]. [20] employed a Multi-Layer Perceptron (MLP) to map Time-of-Flight (TOF) ultrasound measurements into defect images for reinforced polymer composites. Others, like [16], used the structure of MLP combined with an iterative reconstruction algorithm (named MLP-BPE), and tested damaged mortar specimens. Additionally, in [21], a Radial Basis Function Neural Network (RBFNN) was proposed to map different parameters (ultrasonic velocity, signal attenuation, and centroid frequency). This model demonstrated precise reconstruction capabilities in damaged specimens, including aluminum alloy and concrete specimens, which were validated using both simulated and experimental data. Despite the promising accuracy and efficiency offered by neural networks, both approaches demand substantial volumes of data for model training. Furthermore, this requirement becomes increasingly pronounced as the neural network model complexity grows. Obtaining real-world samples of damaged cement is a costly and resource-intensive task. In this study, we proposed the application of a neural network for tomographic reconstruction of damaged cylindrical mortar samples, with training conducted on simulated datasets. This approach addresses a significant limitation of conventional reconstruction algorithms, which normally assume a straight-line wave propagation, neglecting the diffraction and refraction effects from ultrasonic waves.