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Abstract #0281

Intracranial super-resolution 4D Flow MRI – using deep-learning to map flow and relative pressure in the brain

David Marlevi1,2, Edward Ferdian3, Jonas Schollenberger4, Maria Aristova5, Brandon Hardy4, Elazer R Edelman1, Susanne Schnell5,6, C. Alberto Figueroa4, David A Nordsletten4,7, and Alistair A Young3,7
1Massachusetts Institute of Technology, Cambridge, MA, United States, 2Karolinska Institutet, Stockholm, Sweden, 3University of Auckland, Auckland, New Zealand, 4University of Michigan, Ann Arbor, MI, United States, 5Northwestern University, Chicago, IL, United States, 6University of Greifswald, Greifswald, Germany, 7King's College London, London, United Kingdom


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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