Application and assessment of deep learning to routine 2D T2 FLEX spine imaging at 1.5T

2D T2 FSE is an essential routine spine MRI sequence, allowing assessment of fractures, soft tissues, and pathology. Fat suppression using a DIXON-type approach (2D FLEX) improves water/fat separation. Recently, a deep learning (DL) reconstruction (AIR™ Recon DL, GE HealthCare) became available for 2D FLEX, offering increased signal-to-noise ratio (SNR), reduced artifacts, and sharper images. This study aimed to compare DL-reconstructed versus non-DL-reconstructed spine 2D T2 FLEX images for diagnostic image quality and quantitative metrics at 1.5T.

Forty-one patients with clinically indicated cervical or lumbar spine MRI were scanned between May and August 2023 on a 1.5T Voyager (GE HealthCare). A 2D T2 FLEX sequence was acquired, and DL-based reconstruction (noise reduction strength: 75%) was applied. Raw data were also reconstructed without DL. Three readers (CAQ-neuroradiologist, PGY-6 neuroradiology fellow, PGY-2 radiology resident) rated diagnostic preference (0 = non-DL, 1 = DL, 2 = equivalent) for 39 cases. Quantitative measures (SNR, total variation [TV], number of edges, and fat fraction [FF]) were compared using paired t-tests with significance set at p < .05.

Among evaluations, 79.5% preferred DL, 11% found images equivalent, and 9.4% favored non-DL, with strong inter-rater agreement (p < .001, Fleiss' Kappa = 0.99). DL images had higher SNR, lower TV, and fewer edges (p < .001), indicating effective noise reduction. FF remained statistically unchanged in subcutaneous fat (p = .25) but differed slightly in vertebral bodies (1.4% difference, p = .01).

DL reconstruction notably improved image quality by enhancing SNR and reducing noise without clinically meaningful changes in fat quantification. These findings support the use of DL-enhanced 2D T2 FLEX in routine spine imaging at 1.5T. Incorporating DL-based reconstruction into standard spine MRI protocols can increase diagnostic confidence and workflow efficiency. Further studies with larger cohorts and diverse pathologies are warranted to refine this approach and explore potential benefits for clinical decision-making.