Case report: Transvenous coil embolization of a high-grade Galenic dural arteriovenous fistula

Introduction: Galenic dural arteriovenous fistulas (dAVFs) are a rare form of dAVF and rarely described in the literature. Their distinct location requires different surgical approaches than dAVFs occurring at the nearby sites of the straight sinus and torcular Herophili, and their high risk of hemorrhage makes these dAVFs very challenging to approach surgically. In this report, we present a unique case of Galenic dAVF. Case description: The patient is a 54-year-old female who presented with a 2-year history of progressive headaches, cognitive decline, and papilledema. A cerebral angiogram demonstrated a complex dAVF to the vein of Galen (VoG). She underwent transarterial embolization with Onyx-18 which resulted in minimal reduction in arterial venous shunting. She subsequently underwent a successful transvenous coil embolization resulting in complete occlusion of dAVF. The patient’s postoperative course was complicated by interventricular hemorrhage; however, she had a remarkable clinical recovery with resolution of headaches and improvement in cognitive function. A follow-up angiogram completed 6 months post-embolization demonstrated very mild residual shunting. Conclusion: In the unique case presented here, we demonstrate the efficacy of transvenous embolization via an occluded straight sinus as an alternative therapeutic option to eliminate cortical venous reflux

Accurate and Robust Centerline Extraction from Tubular Structures in Medical Images

Extraction of centerlines is useful in interactive navigation and analysis of objects in medical images, such as the lung, bronchia, blood vessels, and colon. Given the noise and other imaging artifacts that are present in medical images, it is crucial to use robust algorithms that are (1) accurate, (2) noise tolerant, (3) computationally efficient, and (4) preferably do not require an accurate segmentation. We propose a new centerline extraction method that employs a Gaussian type probability model to estimate the boundaries of medical objects. The model is computed using an integration of the image gradient field. Probabilities assigned to boundary voxels are then used to compute a more robust distance field, that is less sensitive to noise. Distance field algorithms are then applied to extract the centerline. Noise tolerance of our method is demonstrated by adding Gaussian, Poisson and Rician noise to these datasets, and comparing results to traditional distance field based methods. Accuracy of our method was measured using two datasets with known centerlines, (1) a synthetically generated sinusoidally varying cylindrical dataset, and (2) a radiologist supervised segmented head MRT angiography dataset. Average errors fo