## 1 Introduction
Regarding scatter estimation, there are some experimental methods, such as the beam stop array (BSA), which acquire a projection of only the scatter radiation (primary photons are removed), which is then subtracted from the standard projection [18]. However, most techniques to estimate scatter radiation are software-based [19; 20; 21]. In this field, Monte Carlo (MC) simulations are the gold standard. MC codes reproduce, in a realistic and very accurate way, the interactions of photons (photoelectric absorption, and Rayleigh and Compton scattering) on their path through the human body. Therefore, these models are able to provide precise estimations of scatter [9; 13; 19]. The major drawback of MC simulations is the high computational time they require, which makes it difficult to implement them in real-time clinical practice [9; 19]. Model-based methods make use of simpler physical models, so they are faster than MC at the cost of much lower accuracy [13]. Similarly, kernel-based models approximate the scatter signal using an integral transform of a scatter source term multiplied by a scatter propagation kernel [11; 21]. Nevertheless, this method depends on each acquisition setup (geometry, image object, or X-ray beam spectrum), so it is not easy to generalize [19]. Recently, deep-learning algorithms have been widely used for several medical imaging analysis tasks, like object localization [22; 23], object classification [24; 25], or image segmentation [26; 27], while multi-modal learning techniques have been developed to employ both images and text data to perform diagnosis classification, medical image report retrieval, and radiology report generation [28; 29]. In particular, convolutional neural networks (CNNs) have succeeded in image processing, outperforming traditional and state-of-the-art techniques [3; 26; 30]. Among them, U-Net, first proposed by [PERSON] et al. in 2015 [26] for biomedical image purposes, and its subsequent variants, such as MultiResUNet, are the most popular networks [30]. In particular, some works make use of CNNs to estimate scatter either in CXR or computed tomography (CT). [PERSON] et al. [19] used a U-Net-like deep CNN to estimate scatter in CT images, training the network with MC simulations. In a similar way, [PERSON] and [PERSON] [9] used MC simulations to train a CNN for image restoration, i.e., the scatter image was estimated and was then subtracted from the CNN input image (thescatter image), obtaining, as output, the scatter-corrected image. In that case, they applied the CNN to CXRs, which are then used to reconstruct the corresponding cone-beam CT image. [PERSON] et al. [13] also used a U-Net in combination with prior knowledge of the X-ray scattering, which is approximated by bivariate B-splines so that spline coefficients help to calculate the scatter image for head and thorax datasets. Dual-energy X-ray imaging was first proposed by [PERSON] and [PERSON] in 1976 [31]. This technique takes advantage of the dependence of the attenuation coefficient with the energy of the photon beam and the material properties, i.e., its mass density and atomic number. In dual-energy X-ray, two projections using two different energy spectra in the range of 20-200 kV are acquired. They can provide extra information with respect to common single-energy radiography, allowing for better distinguishing of different materials [32; 33; 34]. In this way, dual-energy X-ray imaging may improve the diagnosis of oncological, vascular, and osseous pathologies [32; 35]. Specifically, in CXR, dual acquisitions are utilized to perform dual-energy subtraction (DES), generating two separate images: one of the soft tissue and one of the bone. Soft-tissue selective images, in which ribs and clavicle shadows are removed, have been proven to enhance the detection of nodules and pulmonary diseases [36; 37]. Several studies have made use of dual-energy X-ray absorption images along with deep-learning methods to enhance medical image analysis.