Modeling human breathing using Fluid-Structure Interaction (FSI) is a challenging topic. Performing this in a subject-specific way is almost impossible as tissue properties and muscle interactions are very complex and vary a lot inter- and intra-subject. However, the problem can be inversed if the airway movement is known. Imaging modalities such as high resolution computed tomography (HRCT) scans make it possible to make a detailed anatomical model of the subject's airways. A HRCT at functional residual capacity (FRC) and at total lung capacity (TLC) gives the initial and the final geometry of a breathing cycle. Mapping the nodes of the TLC mesh to the nodes of the FRC mesh using a point set registration algorithm gives the transformation matrix of every node, resulting in a moving mesh which steers the flow. In this study, the nonrigid Coherent Point Drift Algorithm (CPD) is used. In CPD, the alignment of two node sets is considered as a probability density estimation problem and the Gaussian mixture model centroids (representing the TLC node set) is fitted to the FRC node set by maximizing the likelihood. Lower airway models of a healthy subject are reconstructed until the first generation of segmental airways at both TLC and FRC. The FRC model is then mapped to the TLC model using CPD. The volume difference between the mapped and the original model is 0.375% and the root mean square Hausdorff distance is 0.21mm. This initial study shows that CPD is a promising method in modeling the breathing movement.