Application and Assessment of Deep Learning to Routine 2D T2 FLEX Spine Imaging at 1.5T

Motivation: Deep learning reconstruction in spine imaging has become widely available, but diagnostic quality and quantitative measurements still need to be verified in the clinical setting. Goal(s): Our goal was to validate application of deep learning to 2D FLEX spine imaging at 1.5T via IQ assessment and noise characterization. Approach: DL and conventionally reconstructed images were assessed by three radiologists. Noise characteristics were evaluated by calculation of total variation and number of detected edges. Fat fraction was also calculated. Results: The radiologists preferred DL in majority of cases (79.5%), with noise noticeably lower in DL images. The fat fraction measurements were very similar. Impact: The application of DL to routine 2D FLEX imaging in the spine provides enhanced diagnostic quality and decreased noise without sacrificing quantitative fidelity. Therefore, the application of DL can be confidently used at 1.5T in the clinic for patient care.