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Purpose: To investigate motion compensated, self-supervised, model based deeplearning (MBDL) as a method to reconstruct free breathing, 3D Pulmonaryultrashort echo time (UTE) acquisitions.  Theory and Methods: A self-supervised eXtra Dimension MBDL architecture(XD-MBDL) was developed that combined respiratory states to reconstruct asingle high-quality 3D image. Non-rigid, GPU based motion fields wereincorporated into this architecture by estimating motion fields from a lowresolution motion resolved (XD-GRASP) iterative reconstruction. MotionCompensated XD-MBDL was evaluated on lung UTE datasets with and withoutcontrast and was compared to constrained reconstructions and variants ofself-supervised MBDL that do not consider respiratory motion.  Results: Images reconstructed using XD-MBDL demonstrate improved imagequality as measured by apparent SNR, CNR and visual assessment relative toself-supervised MBDL approaches that do not account for dynamic respiratorystates, XD-GRASP and a recently proposed motion compensated iterativereconstruction strategy (iMoCo). Additionally, XD-MBDL reduced reconstructiontime relative to both XD-GRASP and iMoCo.  Conclusion: A method was developed to allow self-supervised MBDL to combinemultiple respiratory states to reconstruct a single image. This method wascombined with GPU-based image registration to further improve reconstructionquality. This approach showed promising results reconstructing a user-selectedrespiratory phase from free breathing 3D pulmonary UTE acquisitions.

Motion Compensated Self Supervised Deep Learning for Highly Accelerated 3D Ultrashort Echo Time Pulmonary MRI