The overarching goal of this work is to develop and validate novel deep learning algorithms for segmenting the parasagittal dural (PSD) space, which has been hypothesized to harbor cerebral lymphatic channels, from standard non-contrast anatomical imaging. Specifically, contrasted based MRI studies have recently suggested that the PSD may be important for CSF clearance, however existing methods for evaluating this space require administration of exogenous contrast and time-consuming manual tracing, thereby limiting generalizability. We propose a new segmentation method using non-contrasted MRI, and we validate this method in a mixed cohort of older adults with and without neurodegeneration.
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