This study explores how deep learning can be used to reconstruct motion compensated 4D MRI images for patients with hepatocellular carcinoma (HCC), addressing one of the biggest challenges in liver imaging, which is respiratory motion. The researchers developed a deep learning model that processes MRI data to correct for movement caused by breathing, allowing clearer visualization of the tumor throughout different phases of motion. Nineteen patients were included in the study, and the reconstructed images were compared with those produced by traditional methods. The deep learning reconstructions showed noticeably sharper images, reduced motion artifacts, and improved tumor delineation accuracy, all of which are important for radiation therapy planning. The findings suggest that this approach could significantly enhance the precision of liver cancer treatment by giving radiation oncologists a more stable and accurate view of the tumor’s motion. By integrating motion correction directly into MRI reconstruction, this method reduces the need for additional imaging steps or manual adjustments, streamlining the workflow and minimizing uncertainty in dose targeting. Beyond its technical benefits, the study demonstrates how artificial intelligence can be used to solve long standing clinical challenges, offering a path toward more personalized, efficient, and effective cancer care.