Assad Oberai is a professor in the Department of Mechanical, Aerospace and Nuclear Engineering; Associate Director of the Scientific Computation Research Center; and member of the Inverse Problems Center at Rensselaer Polytechnic Institute. His research involves developing numerical methods for solving inverse problems and problems with multiple spatial and temporal scales. His work on multiscale problems includes modeling incompressible and compressible turbulent flows, MHD flows, multiphase flows, and problems with strong discontinuities.
Prof. Oberai received his Ph.D. in Mechanical Engineering from Stanford in 1998.
The Theory and Practice of Biomechanical Imaging
4 p.m., Monday, April 7, 2014
Room 2315 G.G. Brown Building, 2350 Hayward
It is now well recognized that a host of imaging modalities (including ultrasound, Magnetic Resonance Imaging, Second Harmonic Imaging, and optical microscopy) can be used to “watch” tissue as it deforms in response to an excitation. The result is a detailed map of the deformation field in the interior of the tissue. In Biomechanical Imaging (BMI) we use this deformation field in conjunction with a constitutive law to determine the spatial distribution of material properties of the tissue by solving an inverse problem. Images of material properties thus obtained can be used to quantify the health of the tissue. They can be used to detect, diagnose and monitor cancerous lesions, detect vulnerable plaque in arteries, diagnose liver cirrhosis, detect the onset of Alzheimer’s disease, and generate patient specific models for surgical training and planning. In this talk, Prof. Oberai will describe the mathematical and computational aspects of solving this class of inverse problems, and their applications in biology and medicine.
He will discuss the well-posedness of these problems and quantify the amount of displacement data necessary to obtain a unique property distribution. He will describe an efficient algorithm for solving the resulting inverse problem that makes use of the adjoint equations and a novel continuation strategy. He will also describe some recent developments based on Bayesian inference in estimating the variance in the estimates of material properties. He will conclude with the applications of these techniques in diagnosing breast cancer and in characterizing the mechanical properties and prestress of cells at sub-cellular resolution.