Changes in regional hemodynamics are indicative of cerebrovascular disease. However, image-based monitoring is complicated by the unique flow and anatomies found in the brain, with accurate estimates requiring beyond state-of-the-art image resolutions. To address this, we combine a deep residual network, 4D Flow MRI, and physics-informed image processing to provide super-resolution flow images and coupled accurate quantification of intracranial relative pressure. The method is trained and validated on patient-specific in-silico data, highlighting how low resolution-biases are mitigated by super-resolution conversion. Data were also effectively generated at <0.5 mm in a representative in-vivo cohort, highlighting the potential of our presented approach.
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