Diffusion MRI (dMRI)DMRIDiffusion MRI is a valuable tool to map brain microstructure and connectivity by analyzing water molecule diffusion in tissue. However, acquiring dMRIDMRI data requires to capture multiple 3D brain volumes in a short time, often leading to trade-offs in image quality. One challenging artifact is susceptibility-induced distortionSusceptibility distortion, which introduces significant geometric and intensity deformations. Traditional correction methods, such as \({\textrm{topup}}\) Topup, rely on having access to blip-upBlip-up and blip-downBlip-down image pairs, limiting their applicability to retrospective data acquired with a single phase encodingPhase Encoding direction. In this work, we propose a deep learning-basedDeep learning approach to correct susceptibility distortionsSusceptibility distortion using only a single acquisition (either blip-upBlip-up or blip-downBlip-down), eliminating the need for paired acquisitions. Experimental results show that our method achieves performance comparable to \({\textrm{topup}}\) Topup, demonstrating its potential as an efficient and practical alternative for susceptibility distortion correction in dMRIDMRI.