Associate Professor
Erik Andreas Hanson
Field of work
Applied mathematics
Image processing
Modelling of flow in live tissues
Mathematics
Mathematical modelling
Mathematical image processing with applications to medicine and biology.
Data driven modelling of flow in live tissue.
Publications
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Perfusion estimation using synthetic MRI-based measurements and a porous media flow model
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A nonlinear multi-scale model for blood circulation in a realistic vascular system
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A computational fluid dynamics framework to generate digital reference objects for perfusion imaging
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Computational Study of Vessel Occlusion using a Nonlinear Multiscale Blood Flow Model
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A multi-scale flow model for studying blood circulation in the vascular system
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A study of blood flow regulation on collateral circulation using multiscale flow model
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Fractal vascular structure optimization based on a multi-scale blood flow model
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A new framework for assessing subject-specific whole brain circulation and perfusion using MRI-based measurements and a multi-scale continuous flow model
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Mathematics and Medicine: How mathematics, modelling and simulations can lead to better diagnosis and treatments
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Mathematical perfusion
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In vivo detection of chronic kidney disease using tissue deformation fields from dynamic MR imaging.
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Estimating the discretization dependent accuracy of perfusion in coupled capillary flow measurements
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Models suited for data assimilation
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Graph representations of vessel flow
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Modelling of brain perfusion
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Workflow sensitivity of post-processing methods in renal DCE-MRI
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Dynamic contrast-enhanced MRI measurement of renal function in healthy participants
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Physical models for simulation and reconstruction of human tissue deformation fields in dynamic MRI
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Reproducibility and agreement in quantification of renal function using 1.5-T DCE-MRI
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Poroelastic regularization of image registration
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Image processing methods for 4D magnetic resonance acquisitions from brain and kidney
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Segmentation-driven image registration-application to 4D DCE-MRI recordings of the moving kidneys
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Local/non-local regularized image segmentation using graph-cuts
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Combined motion correction and segmentation of DCE-MRI kidney data