Abstract
Diffusion-weighted imaging (DWI)-based tractography has gained increasing popularity as a method for detailed visualization of white matter (WM) tracts. Different imaging techniques, and more novel, advanced imaging methods provide significant WM structural detail. While there has been greater focus on improving tract visualization for larger WM pathways, the relative value of each method for cranial nerve reconstruction and how this methodology can assist surgical decision-making is still understudied. Images from 10 patients with posterior fossa tumors (4 male, mean age: 63.5), affecting either the trigeminal nerve (CN V) or the facial/vestibular complex (CN VII/VIII), were employed. Three distinct reconstruction methods [two tensor-based methods: single diffusion tensor tractography (SDT) (3D Slicer), eXtended streamline tractography (XST), and one fiber orientation distribution (FOD)-based method: streamline tractography using constrained spherical deconvolution (CSD)-derived estimates (MRtrix3)], were compared to determine which of these was best suited for use in a neurosurgical setting in terms of processing speed, anatomical accuracy, and accurate depiction of the relationship between the tumor and affected CN. Computation of the tensor map was faster when compared to the implementation of CSD to provide estimates of FOD. Both XST and CSD-based reconstruction methods tended to give more detailed representations of the projections of CN V and CN VII/VIII compared to SDT. These reconstruction methods were able to more accurately delineate the course of CN V and CN VII/VIII, differentiate CN V from the cerebellar peduncle, and delineate compression of CN VII/VIII in situations where SDT could not. However, CSD-based reconstruction methods tended to generate more invalid streamlines. XST offers the best combination of anatomical accuracy and speed of reconstruction of cranial nerves within this patient population. Given the possible anatomical limitations of single tensor models, supplementation with more advanced tensor-based reconstruction methods might be beneficial.
Highlights
Diffusion-weighted imaging (DWI) is a neuroimaging method that assays the random movement of water molecules to reconstruct the structure of white matter (WM) fibers (Behrens and Johansen-Berg, 2009; Jones et al, 2013; Soares et al, 2013)
As processing time is a major concern for neurosurgeons hoping to incorporate DWI-based technology into their practice, we measured processing time for the following steps in this procedure: (i) correcting for motion-related and eddy currentinduced artifacts, (ii) Registration between T1 anatomical and diffusion images for anatomical localization, (iii) 3D tumor modeling, (iv), diffusion model creation, (v) Appropriate seed selection, and (vi) the tracking algorithm itself
Ensuring appropriate seed selection for cranial nerves (CNs) VII/VIII in patients presenting with vestibular schwannomas (P05–P10) was time-consuming (1–2 hr per patient for P05–P10)
Summary
Diffusion-weighted imaging (DWI) is a neuroimaging method that assays the random movement of water molecules to reconstruct the structure of white matter (WM) fibers (Behrens and Johansen-Berg, 2009; Jones et al, 2013; Soares et al, 2013) Within the brain, this movement is affected by structural features such as axons of WM (Mori and van Zijl, 2002; O’Donnell and Westin, 2011). Deterministic single diffusion tensor tractography (SDT) has been successfully used to reconstruct representations of large WM fiber tracts within patient populations with supratentorial tumors (Potgieser et al, 2014) While this method tends to provide reliable results when there is one major fiber bundle of interest, it performs less so in areas where multiple WM fiber bundles are present (Wedeen et al, 2008). With an estimated 63–90% of WM voxels containing multiple fiber bundles (Jeurissen et al, 2013), this can be a considerable problem for neurosurgical planning, in terms of following the anatomical course of a nerve when it encounters other fiber bundles
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