NeuroImage | 2019

Investigating the oxygenation of brain arteriovenous malformations using quantitative susceptibility mapping

 
 
 
 
 
 
 

Abstract


Brain arteriovenous malformations (AVMs) are congenital vascular anomalies characterized by arteriovenous shunting through a network of coiled and tortuous vessels. Because of this anatomy, the venous drainage of an AVM is hypothesized to contain more oxygenated, arterialized blood than healthy veins. By exploiting the paramagnetic properties of deoxygenated hemoglobin in venous blood using magnetic resonance imaging (MRI) quantitative susceptibility mapping (QSM), we aimed to explore venous density and oxygen saturation (SvO2) in patients with a brain AVM. We considered three groups of subjects: patients with a brain AVM before treatment using gamma knife radiosurgery (GKR); patients three or more years post-GKR treatment; and healthy volunteers. First, we investigated the appearance of AVMs on QSM images. Then, we investigated whether QSM could detect increased SvO2 in the veins draining the malformations. In patients before GKR, venous density, but not SvO2, was significantly larger in the hemisphere containing the AVM compared to the contralateral hemisphere (p\u202f=\u202f0.03). Such asymmetry was not observed in patients after GKR or in healthy volunteers. Moreover, in all patients before GKR, the vein immediately draining the AVM nidus had a higher SvO2 than healthy veins. Therefore, QSM can be used to detect SvO2 alterations in brain AVMs. However, since factors such as flow-induced signal dephasing or the presence of hemosiderin deposits also strongly affect QSM image contrast, AVM vein segmentation must be performed based on alternative MRI acquisitions, e.g., time of flight magnetic resonance angiography or T1-weighted images. This is the first study to show, non-invasively, that AVM draining veins have a significantly larger SvO2 than healthy veins, which is a finding congruent with arteriovenous shunting.

Volume 199
Pages \n 440-453\n
DOI 10.1016/j.neuroimage.2019.05.014
Language English
Journal NeuroImage

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