Utility of 7 Tesla MRI for Preoperative Planning of Endoscopic Endonasal Surgery for Pituitary Adenomas

Objective  There is increasing interest in investigating the utility of 7 Tesla (7 T) magnetic resonance imaging (MRI) for imaging of skull base tumors. The present study quantifies visualization of tumor features and adjacent skull base anatomy in a homogenous cohort of pituitary adenoma patients. Methods  Eighteen pituitary adenoma patients were scanned at 7 T in this prospective study. All patients had reference standard-of-care clinical imaging at either 3 T (7/18, 39%) or 1.5 T (11/18, 61%). Visualization of tumor features and conspicuity of arteries and cranial nerves (CNs) was rated by an expert neuroradiologist on 7 T and clinical field strength MRI. Overall image quality and severity of image artifacts were also characterized and compared. Results  Ability to visualize tumor features did not differ between 7 T and lower field MRI. Cranial nerves III, IV, and VI were better detected at 7 T compared with clinical field strength scans. Cranial nerves III, IV, and VI were also better detected at 7 T compared with only 1.5 T, and CN III was better visualized at 7 T compared with 3 T MRI. The ophthalmic arteries and posterior communicating arteries (PCOM) were better detected at 7 T compared with clinical field strength imaging. The 7 T also provided better visualization of the ophthalmic arteries compared with 1.5 T scans. Conclusion  This study demonstrates that 7 T MRI is feasible at the skull base and identifies various CNs and branches of the internal carotid artery that were better visualized at 7 T. The 7 T MRI may offer important preoperative information that can help to guide resection of pituitary adenoma and reduce operative morbidity.

Aberrant Structural Network Architecture in Leber's Hereditary Optic Neuropathy. Minimum Spanning Tree Graph Analysis Application into Diffusion 7T MRI

Examining individuals with Leber's hereditary optic neuropathy (LHON) provides a rare opportunity to understand how changes in mitochondrial DNA and loss of vision can be related to changes in organization of the whole-brain structural network architecture. In comparison with the previous neuroimaging studies with LHON participants, which were focused mainly on analyzing changes which occur in different areas of the patient's brain, network analysis not only makes it possible to observe single white matter fibers' aberrations but also the whole-brain nature of these changes. The purpose of our study was to better understand whole-brain neural network changes in LHON participants and see the correlation between the clinical data and the changes. To achieve this, we examined fifteen LHON patients and seventeen age-matched healthy subjects with the usage of ultra-high filed 7T magnetic resonance imaging (MRI). Basing on the analysis on MRI diffusion tensor imaging (DTI) data, whole-brain structural neural networks were reconstructed with the use of the minimum spanning tree algorithm (MST) for every participant. Our results revealed that the structural network in LHON participants was altered at both the local and the global level. The global network structures of LHON subjects were less centralized with path-like organization and there was an imbalance in the main hub centrality. Moreover, the inspection of nodes and hubs in terms of their anatomical placement revealed that in the LHON participants the prominent hubs were located within the basal ganglia (i.e. bilateral caudate, left pallidum), which differed them from healthy controls. An analysis of the relationships between the global MST metrics and LHON participants' clinical characteristics revealed significant correlations between the global network metrics and the duration of illness. Furthermore, the nodal parameters of the optic chiasm were significantly correlated with the duration of illness and the averaged thickness of the right retinal nerve fiber layer (RNFL). These findings clearly showed that the progression of the disease is accompanied by alterations within the brain network structure and its efficiency.