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In Skeletal radiology

OBJECTIVE : To evaluate the diagnostic equivalency between an ultrafast (1 min 53 s) lumbar MRI protocol using deep learning-based reconstruction and a conventional lumbar MRI protocol (12 min 31 s).

MATERIALS AND METHODS : This study included 58 patients who underwent lumbar MRI using both conventional and ultrafast protocols, including sagittal T1-weighted, T2-weighted, short-TI inversion recovery, and axial T2-weighted sequences. Compared with the conventional protocol, the ultrafast protocol shortened the acquisition time to approximately one-sixth. To compensate for the decreased signal-to-noise ratio caused by the acceleration, deep learning-based reconstruction was applied. Three neuroradiologists graded degenerative changes and analyzed for presence of other pathologies. For the grading of degenerative changes, interprotocol intrareader agreement was assessed using kappa statics. Interchangeability between the two protocols was also tested by calculating the individual equivalence index between the intraprotocol interreader agreement and interprotocol interreader agreement. For the detection of other pathologies, interprotocol intrareader agreement was assessed.

RESULTS : For the grading of degenerative changes, the kappa values for interprotocol intrareader agreement of all three readers ranged from 0.707 to 0.804, indicating substantial to almost perfect agreement. Except for foraminal stenosis and disc contour on axial images, the 95% confidence interval of the individual equivalence index was < 5%, indicating the two protocols were interchangeable. For the detection of other pathologies, the interprotocol intrareader agreement rates were > 98% for each individual pathology.

CONCLUSIONS : Our proposed ultrafast lumbar spine MRI protocol provided almost equivalent diagnostic results to that of the conventional protocol, except for some degenerative changes.

Fujiwara Masahiro, Kashiwagi Nobuo, Matsuo Chisato, Watanabe Hitoshi, Kassai Yoshimori, Nakamoto Atsushi, Tomiyama Noriyuki


Deep learning, Diagnostic performance, Fast imaging, Lumbar spine, Magnetic resonance imaging