A data analysis pipeline comprising KUL neuroimaging tools was applied to analyze diffusion and anatomical MRI datasets with neoplastic lesions and perform diffusion tractography with anatomical priors. We utilized a novel framework for reducing the amount of false positive streamlines to allow for improved visualization of different fiber bundles. Results illustrate the meaningfulness of this approach in neurosurgery workflow through offering clinicians more information on the quality of perilesional fiber bundles as well as where to resect with care to preserve functionally eloquent areas.
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