Posture-dependent assessment of whole-spine curvature with a rotatable cryogen-free 1.5T MRI and automated Cobb angle analysis

Purpose: To evaluate posture-dependent spinal curvature in adolescents using a novel rotatable cryogen-free 1.5T MRI system and to establish a fully automated analysis pipeline for quantitative Cobb angle measurement. Methods: Thirty-two healthy adolescents (15 males, 17 females; mean age 15.0 ± 2.2 years) underwent whole-spine MRI in both supine (0°) and upright (90°) postures. Imaging was performed with a 3D coronal LAVA sequence. A custom pipeline integrated structural-similarity–based image stitching, deep learning–assisted segmentation and annotation, and Cobb angle computation from coronal projections. Cobb angles were compared between postures using paired t-tests with Bonferroni correction, and effect sizes were reported as Cohen’s d. Results: Imaging and automated analysis were successfully completed for all participants. Upright Cobb angles were significantly larger than supine across most regions: thoracic (6.5 ± 2.3° vs. 8.4 ± 3.1°, d = 0.65, corrected p = 0.003), lumbar (6.7 ± 3.9°vs 8.7 ± 4.9°, d = 0.43, corrected p = 0.060), cervicothoracic (5.0 ± 2.2°vs 6.3 ± 2.9°, d = 0.53, corrected p = 0.030), and thoracolumbar (8.1 ± 3.6° vs. 11.3 ± 4.6°, d = 0.74, corrected p < 0.001). Maximum Cobb angle increased from 9.1 ± 3.3° to 12.4 ± 3.9° (d = 0.83, corrected p < 0.001). Using the clinical threshold of 10°, 13 of 32 (41%) subjects were classified as negative in supine but positive in upright, indicating that supine imaging may underestimate spinal curvature magnitude in posture-dependent assessments. Conclusion: This study demonstrates the feasibility and clinical potential of a rotatable upright MRI system for radiation-free, posture-dependent assessment of spinal curvature. Upright imaging consistently revealed greater Cobb angles than supine imaging, particularly in the thoracolumbar region, and identified cases that would be underestimated with conventional methods. Combined with a fully automated analysis pipeline, this approach provides a reproducible and physiologically relevant framework for characterizing spinal curvature under natural loading conditions.