3D MR Neurography of craniocervical nerves: comparing DESS and post-contrast STIR with deeplearning-based reconstruction at 1.5T.

Abstract

Background and purpose: 3D MR neurography is a useful diagnostic tool in head and neck disorders, but neurographic imaging remains challenging in this region. Optimal sequences for nerve visualization have not yet been established and may also differ between nerves. While deep learning reconstruction can enhance nerve depiction, particularly at 1.5T, studies in the head and neck are lacking. The purpose of this study was to compare DESS and post-contrast STIR sequences in deep-learning -reconstructed 3D MR neurography of the extraforaminal cranial and spinal nerves at 1.5T.

Materials and methods: Eighteen consecutive exams of 18 patients undergoing head-and-neck MRI at 1.5T were retrospectively included (mean age: 51 ± 14 years, 11 female). 3D DESS and post-contrast 3D STIR sequences were obtained as part of the standard protocol, and reconstructed with a prototype deep learning algorithm. Two blinded readers qualitatively evaluated visualization of the inferior alveolar, lingual, facial, hypoglossal, greater occipital, lesser occipital and greater auricular nerves, as well as overall image quality, vascular suppression and artifacts. Additionally, apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratios (aCNR) of the inferior alveolar and greater occipital nerve were measured. Visual ratings and quantitative measurements, respectively, were compared between sequences using Wilcoxon signed-rank test.

Results: DESS demonstrated significantly improved visualization of the lesser occipital nerve, greater auricular nerve and proximal greater occipital nerve (p < 0.015). Post-contrast STIR showed significantly enhanced visualization of the lingual nerve, hypoglossal nerve and distal inferior alveolar nerve (p < 0.001). The facial nerve, proximal inferior alveolar nerve and distal greater occipital nerve did not demonstrate significant differences in visualization between sequences (p > 0.08). There was also no significant difference for overall image quality and artifacts. Post-contrast STIR achieved superior vascular suppression, reaching statistical significance for one reader (p = 0.039). Quantitatively, there was no significant difference between sequences (p > 0.05).

Conclusions: Our findings suggest that 3D DESS generally provides improved visualization of spinal nerves, while post-contrast 3D STIR facilitates enhanced delineation of extraforaminal cranial nerves.

Abbreviations: DESS = Dual-echo steady-state; DL = Deep learning; FN = Facial nerve; GAN = Greater auricular nerve; GON = Greater occipital nerve; HN = Hypoglossal nerve; IAN = Inferior alveolar nerve; LN = Lingual nerve; LON = Lesser occipital nerve; MRN = Magnetic resonance neurography.