Respiratory motion models aim at improving the quality of free breathing image acquisition protocols and yield increased targeting accuracy during image guided interventions. Respiratory motion can deviate pre-defined targets and trajectories determined preoperatively during treatment procedures. In this context, motion models offer a mean to estimate spatio-temporal displacements of the organ and correct the target position in real time during an intervention. To construct a motion model, data of the entire organ of interest must be acquired. However, existing techniques for 3D dynamic imaging have poor spatial and temporal resolution. Therefore, to capture the organ’s temporal behavior, series of dynamic 2D slices covering the entire organ are typically acquired. Then, these slices are reordered retrospectively according to their motion phase within the respiratory cycle and stacked to form 3D dynamic volumes known as 4D images (3D + <i>t</i>). On the other hand, while numerous metrics were proposed to assess the spatial quality of the reordering, little attention has been paid to metrics that assess the coherent temporal behavior of the reconstructed dynamic volumes. This work proposes a method combining image-based matching approach with manifold alignment and compares it with two state of the art slice reordering methods. Methods were evaluated on a dataset of 7 volunteers using new metrics to assess the spatial quality and the temporal behavior, with the proposed method outperforming in terms of both spatial and temporal quality.