## 1 Introduction  
Texture analysis is a promising technique for extracting quantitative features from radiographic images to be correlated with clinical or pathological characteristics of a tissue [1, 2]. In the 1990s, several authors began applying texture analysis to mammographic images to identify and classify malignant masses or microcalcifications [3; 4; 5; 6; 7]. Other authors correlated texture features with BRCA1/BRCA2 mutations [8] or with breast cancer subtypes [9]. In a recent work by [PERSON] et al., texture analysis of digital mammographic images was deemed as an excellent tool for differential diagnosis of benign and malignant tumors, especially if combined with standard visual analysis by radiologists [10]. Digital breast tomosynthesis (DBT) is a relatively new technique compared to digital mammography (DM), and for this reason, studies on texture analysis applied to DBT are less widespread. [PERSON] et al. studied the correlation between texture features calculated from DBT images and risk factors such as breast percent density or Gail and Claus risk models [11; 12]. They concluded that texture features extracted from DBT images are more favorably correlated with risk factors than those extracted from DM. However, the authors also observed that the gray level distribution of DBT images may be affected by several factors such as the image acquisition geometry [13] and the reconstruction algorithm, hence suggesting that further research is needed to analyze the variability of features according to these parameters. At IRST institute, clinical DETs can be performed with two acquisition geometries that differ in terms of the acquisition angle (15\({}^{\circ}\) and 40\({}^{\circ}\)) and in-plane image resolution (0.15 and 0.10 mm/pixel). With the final aim of developing a computer-aided diagnosis (CAD) system based on texture features, we first needed to investigate whether different acquisition geometries had an impact on texture values. Several authors have demonstrated that the parameter that most influences DBT images is the DBT acquisition angle. In particular, [PERSON] et al. found that an increased acquisition angle positively correlated with improved z-resolution [14]. [PERSON] et al. observed that increasing the DBT acquisition angle improved the contrast-to-noise ratio of disk details, as well as the subjective scoring of image quality by multiple readers [15]. Other authors used a model-observer approach to study the detectability of small signals in simulated DBT datasets, reporting that the acquisition angle had a positive effect on the detectability of each signal size considered [16]. A model-observer approach was also used recently by [PERSON] et al., who studied the signal detectability of simulated DBT patterns by varying several acquisition parameters including angle, reconstruction filter, and slice thickness [17]. However, all these studies were performed on phantoms or simulated DBT datasets. The aim of the present study was to use texture analysis to characterize the impact of the acquisition angle on DBT images. This research was conducted on real DBT images of breast parenchyma originating from a clinical paired dataset (i.e., a single patient underwent dual DBT scan with 15\({}^{\circ}\) and 40\({}^{\circ}\) acquisition angle). To the best of our knowledge, no similar studies have been published elsewhere.