Cortical Constraint Method for Diffuse Optical Brain Imaging

We develop a parametric, shape-based image reconstruction algorithm for the joint reconstruction of the optical absorption and diffusion coefficients in the brain using diffuse optical tomographic data. Specifically, we study the recovery of the geometry of an unknown number of 2D closed contours located on a 2D manifold (the cortex) in 3-space. We describe an approach for a brain model in which we assume the existence of a one-to-one map from the surface of the cortex to a subset of the plane. We use a new, parametric level set approach to map shapes on the plane to structures on the cortex. Our optimization-based reconstruction algorithm evolves shapes on the plane while finding absorption and reduced scattering values inside each shape. Preliminary numerical simulation results show the promise of our approach.