Abstract
PurposeThe 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. MethodsIn 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 (Tpeak, PH, ERmax and Slopemax) 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. ResultsIn 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. ConclusionsVisual 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.
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