Time-resolved volumetric magnetic resonance imaging (4D MRI) could be used to address organ motion in image-guidedinterventions like tumor ablation. Current 4D reconstruction techniques are unsuitable for most interventional settings becausethey are limited to specific breathing phases, lack temporal/spatial resolution, and have long prior acquisitions or reconstructiontimes. Deep learning-based (DL) 4D MRI approaches promise to overcome these shortcomings but are sensitive to anotherproblem, which is domain shift. This work shows that transfer learning (TL) can help alleviate this key challenge. Data from20 healthy subjects were acquired and split into 16 source and 4 target domain subjects. We compare models trained fromscratch on target domain data with models fine-tuned from a pre-trained model. Significant improvements (P<.001) of the rootmean squared error (RMSE) of up to 12% (effect size d=-0.5), the mean displacement (MDISP) of up to 12.5% (d=-0.263), andthe deformation-normalized RMSE (DN_RMSE) of up to 15% (d=-0.679) are reported. The smaller the target domain dataamount, the larger the effect. This shows that TL significantly reduces beforehand acquisition time and improves reconstructionquality, rendering it a key component in making 4D MRI clinically feasible for the first time in the context of 4D organ motionmodels of the liver and beyond.