Magnetic Resonance Imaging (MRI) plays an important role in the diagnosis and monitoring of central nervous system (CNS) pathologies. There are various MRI modalities that enable the visualization of distinct structural features in neoplasms. Among these modalities, contrast-enhanced weighted T1 images (T1W-CE) are widely utilized, as they clearly delineate tumor boundaries and highlight cerebral vasculature. This modality relies on gadolinium-based contrast agents (GBCAs). However, due to risk of deposition in tissues after repeated administration and expensiveness, it is recommended to limit their use. To address this limitation, this study proposes a deep learning (DL) algorithm that generates synthetic T1W-CE images from non-contrast MRI sequences. The preliminary evaluation was performed using conventional, quantitative metrics and qualitative assessment by an expert neuroradiologist. This evaluation supports the potential feasibility of the proposed method in reconstructing synthetic T1W-CE MRI scans that closely resemble real contrast-enhanced images.