Abstract
PURPOSE: The aim of this study was to demonstrate the value of DCE MRI with high spatiotemporal resolution (GRASP) for differentiating paragangliomas and schwannomas in the head and neck. METHODS: In a retrospective PACS search of in total 410 patients who had undergone head & neck GRASP-MRI, we identified 6 patients with biopsy proven cervical paragangliomas (n = 3) and schwannomas (n = 3). Conventional MRI features were evaluated, lesion size was determined. Postprocessing in 4D-GRASP datasets was performed (1) based on reconstructions with a temporal resolution (Tres) of 4.1 s, qualitative time-intensity curve classification and semiquantitative parameter (T(peak), PH, ER(max) and Slope(max)) analysis, and (2) voxel-based mapping and qualitative and semiquantitative perfusion modeling based on reconstructions with a Tres of 1.6 s. Additionally, GRASP perfusion analysis was performed in another set of 5 patients with presumed cervical paragangliomas (n = 3) and schwannomas (n = 2) based on conventional imaging criteria and was correlated with conventional imaging findings. Due to the small sample size, both groups were compared qualitatively. RESULTS: In the time intensity curve classification of 4D GRASP reconstructions (Tres 4.1 s), biopsy proven paragangliomas were consistently characterized by a type-III rapid inflow wash-out pattern, compared to a type-I inflow pattern in the schwannoma group. In both temporal resolutions, semiquantitative analysis of time intensity curves demonstrated rapid wash-in, wash-out, and higher peak signal intensities in paragangliomas compared to schwannomas. In 5 presumed (non-biopsy-proven) paragangliomas and schwannomas, time intensity curves improved diagnostic certainty. CONCLUSIONS: Visual time intensity curve classification and semi-quantitative analysis of GRASP-MRI were, in this small retrospective series, sufficient to differentiate cervical paragangliomas from schwannomas. Utilization of this technique may further improve diagnostic confidence in lesions lacking conventional imaging features.