IMC Colloquium Series: "Recent Advances in Medical Imaging"

Abstract

Images and visualization have become increasingly important in many areas of science and technology. Advances in hardware and software have allowed computerized image processing to become a standard tool in many scientific applications, including medical imaging. In this talk, we see how we model and solve the inverse problem of reconstructing a dynamic medical image where the signal strength changes substantially over the time required for data acquisition. We use a stochastic approach based on a Markov process to model the problem. We introduce a novel proximal approach and apply it during the Kalman filter algorithm to ensure positivity and spatial regularization. We test our method for the case of image reconstruction in time-dependent single photon emission computed tomography (SPECT). Numerical results corroborate the effectiveness of our approach.