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LANS Informal Seminar: Andreas Linninger
February 23, 2011 @ 15:00 CST
Seminar Title: The need for super-computations in biomedical simulation of the brain
Speaker: Andreas Linninger, Professor, UIC
Date/Time: 2011-02-23 15:00
Location: Bldg 240, Room 4301
Description:
Recent advances in quantitative imaging allow unprecedented views into whole organisms of humans or test animals, specific organs or cellular chemistry in vivo. Novel imaging modalities make possible the scientific investigation of spatio-temporal distribution of therapeutic drugs or velocity fields of body fluids like blood or cerebrospinal fluid. Our group has integrated the accelerating capabilities of quantitative imaging techniques with rigorous computational fluid mechanics methods to study complex reaction and transport phenomena occurring in biological systems.
This presentation will demonstrate the need for massive computing power for addressing open questions in intracranial dynamics and drug transport. The first example fuses advanced medical imaging modalities with rigorous computational techniques to quantify the interaction of cerebrospinal fluid (CSF) dynamics with cerebral blood and soft deformable brain tissues. A fully coupled dynamic model of the entire human brain predicts intracranial pressures, CSF flow pulsations as well as brain tissue displacements in normal and pathological conditions such as hydrocephalus. The second example discusses large scale CFD simulations of invasive delivery of drugs in anisotropic brain tissue for the treatment of neurodegenerative diseases. Finally, we present our approach for simulation and control of cerebral blood flow based in a massive network model of the entire cerebral vasculature.
Image reconstruction techniques are demonstrated to create a seamless bridge from real patient-specific anatomical spaces to large computational meshes to perform realistic simulations of blood and cerebrospinal fluid flow, or drug distribution in anisotropic tissues. The case studies will also highlight limitations of current computational power and the potential knowledge gain that could be achieved with more massive computations.