Improved Upper‐Airway Collapse Imaging in Obstructive Sleep Apnea Using Self‐Supervised Spiral Variational Manifold Learning

Purpose To propose a self‐supervised variational manifold learning framework for dynamic upper‐airway MRI reconstruction in obstructive sleep apnea from prospectively under‐sampled spiral data during natural sleep, without requiring fully sampled training data. Methods A CNN generator and low‐dimensional latent vectors were jointly optimized directly from prospectively under‐sampled spiral k‐space measurements, learning a subject‐specific temporal manifold without an explicit motion model. A validation loss guided adaptive learning rate provided principled early stopping without ground truth. Eleven concurrent axial slices from velo‐pharynx to hypo‐pharynx were reconstructed at 183 ms temporal resolution and acceleration across 8 OSA patients during natural sleep and 4 healthy volunteers performing the Müller maneuver. Reconstruction quality was compared against parallel imaging, compressed sensing, view sharing, and analysis‐manifold reconstruction. Three expert raters independently assessed image quality using a four‐point ordinal rubric. Results The proposed method resolved multi‐site airway collapse across 11 contiguous slices, including simultaneous visualization of complete concentric retro‐palatal collapse, lateral collapse at the velo‐pharyngeal to oro‐pharyngeal transition, and partial tongue‐base and oropharyngeal narrowing. Expert scores were superior across aliasing, spatial blurring, and temporal blurring categories (, pairwise Mann–Whitney U tests). Reconstructed airway dynamics were corroborated by concurrent respiratory effort and SpO signals, with up to 13 obstructive events identified within a single imaging run. Reconstruction artifacts occurred in 8 of 132 slices (6%) and did not compromise airway visualization. Conclusion Self‐supervised variational manifold learning demonstrates the feasibility of robust dynamic upper‐airway MRI during natural sleep, with potential to inform future OSA phenotyping and surgical‐planning studies.