However, HARDI typically requires a more complex data acquisition compared to DTI. High angular-resolution imaging (HARDI) methods like constrained spherical deconvolution (CSD) can address the limitations of DTI-FACT. These short-comings limit the accuracy of presurgical fiber tracking and motivate exploring alternative methods using automation, and higher-order model based tractography. Additionally, FT suffers from user-bias and limited reproducibility when manual region of interest (ROIs) definition is used, which is the most common approach in clinical practice. DTI-FACT is also confounded by lesion-effects, as perilesional edema can compromise sensitivity to fiber orientation, resulting in false-negatives, i.e., missing streamlines/tracts. DTI assumes a single fiber direction per voxel, which compromises the accuracy of modeled streamline trajectories in voxels containing complex fiber architecture, leading to underestimation of the true extent of fiber bundles. Due to its clinical accessibility and validation, the most commonly used FT approach in neurosurgical settings is diffusion tensor imaging (DTI)-based fiber assignment by continuous tracking (FACT).ĭTI-FACT suffers from multiple limitations making it suboptimal in neurosurgical practice. FT is the rendering of white matter fasciculi in 3D using dMRI signal contrast and computational modeling. Here we focused on presurgical mapping with diffusion MRI (dMRI)-based fiber tractography (FT). However, it may be the sole mapping method available in patients where the gold-standard is not feasible due to its complexity and potential complications. Magnetic resonance imaging (MRI)-based non-invasive brain mapping, normally complements and guides DES. The gold-standard for function-preserving brain surgery is intraoperative direct electrical stimulation (DES) during awake-surgery with image-guided neuronavigation. Modern neurosurgery strives to optimize functional preservation and therapeutic outcomes.
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