Validation and Feasibility of Ultrafast Cervical Spine MRI Using a Deep Learning-Assisted 3D Iterative Image Enhancement System

Purpose: This study aimed to evaluate the feasibility of ultrafast (2 min) cervical spine MRI protocol using a deep learning-assisted 3D iterative image enhancement (DL-3DIIE) system, compared to a conventional MRI protocol (6 min 14s). Patients and Methods: Fifty-one patients were recruited and underwent cervical spine MRI using conventional and ultrafast protocols. A DL-3DIIE system was applied to the ultrafast protocol to compensate for the spatial resolution and signal-to-noise ratio (SNR) of images. Two radiologists independently assessed and graded the quality of images from the dimensions of artifacts, boundary sharpness, visibility of lesions and overall image quality. We recorded the presence or absence of different pathologies. Moreover, we examined the interchangeability of the two protocols by computing the 95% confidence interval of the individual equivalence index, and also evaluated the inter-protocol intra-observer agreement using Cohen's weighted kappa. Results: Ultrafast-DL-3DIIE images were significantly better than conventional ones for artifacts and equivalent for other qualitative features. The number of cases with different kinds of pathologies was indistinguishable based on the MR images from ultrafast-DL-3DIIE and conventional protocols. With the exception of disc degeneration, the 95% confidence interval for the individual equivalence index across all variables did not surpass 5%, suggesting that the two protocols are interchangeable. The kappa values of these evaluations by the two radiologists ranged from 0.65 to 0.88, indicating good-to-excellent agreement. Conclusion: The DL-3DIIE system enables 67% spine MRI scan time reduction while obtaining at least equivalent image quality and diagnostic results compared to the conventional protocol, suggesting its potential for clinical utility. Keywords: cervical spine, magnetic resonance imaging, fast imaging, deep learning