Magnetic resonance imaging (MRI) is a non-invasive, radiation-free imaging modality widely used in clinical diagnosis. While 3D MRI offers higher spatial resolution for improved delineation of small lesions compared to 2D MRI, its acquisition is often time-consuming. To address this limitation, we propose a novel Multi-Contrast Volumetric Super-Resolution (MCVSR) method that synthesizes high-resolution (HR) 3D MRI images from a low-resolution (LR) 2D MRI scan and an auxiliary HR 3D MRI acquired with a different contrast as guidance. Our approach introduces two key innovations: a Discrete Wavelet Transform (DWT) module and a multi-scale Simple Attention Module (MS-SimAM). The DWT module decomposes the image features into frequency sub-bands, enabling the model to capture both global structures and fine details such as edges and textures. In addition, MS-SimAM enhances feature selection across varying receptive fields, facilitating the restoration of high-frequency details. Extensive experiments demonstrate that our method consistently outperforms existing single-/multi-contrast slice-/volume-based super-resolution methods. These results highlight the significant advantages of leveraging multi-contrast information to enhance the quality of clinical 2D MRI scans, offering a promising solution for accelerating 3D MRI acquisition without compromising diagnostic accuracy.