ALT One of the biggest challenges in pediatric MRI is managing motion artifacts. Sedation or anesthesia is frequently used, to minimize the motion artifacts. However, these approaches carry increased medical risks and additional costs. Motion correction techniques offer a promising solution, however, no single method has yet gained widespread clinical acceptance due to the stringent requirements for robustness, reliability, and seamless integration into clinical workflows. Data-driven retrospective motion correction offers a promising solution to these challenges. It derives the motion trajectory information from the acquired k-space data of the imaging sequence and produces the motion-corrected images without the need for additional tracking hardware or disruptive sequence modifications. Moreover, a significant advantage over prospective techniques is that radiologists can directly compare the motion-modeled reconstruction with the original, non-corrected images.