UTE radial MRI methods are powerful tools for probing lung structure and function. However, the challenge in directly using this scheme for high-resolution lung imaging applications is the long breath-hold needed. While self-gating approaches that bin the data to different respiratory phases are promising, they do not allow the functional imaging of the lung and are often sensitive to bulk motion. The main focus of this work is to introduce a novel motion compensated manifold learning framework for functional and structural lung imaging. The proposed scheme is robust to bulk motion and enables high-resolution lung imaging in around 4 minutes.