Colloquium Mathematics - Dr. F. Lucka

Title:
Challenges of Mathematical Image Reconstruction

Abstract:
Mathematical image reconstruction describes the process of computing images of quantities of interest from indirect observations through algorithms derived from rigorous mathematics. As the observation process can often be modeled by partial differential equations, image reconstruction problems are a classical example of inverse problems and draw from various fields of applied mathematics, including Bayesian inference, variational regularization, compressed sensing, computational optimization, machine learning and numerical analysis.
Mathematical image reconstruction became a key technique in a vast range of scientific, clinical and industrial applications and in this talk, I want to highlight some of its current trends and challenges, illustrated by my own work on biomedical imaging applications such as X-ray computed tomography (CT), photoacoustic tomography (PAT), electro- and magnetoencephalography (EEG/MEG), magnetic resonance imaging (MRI ) and ultrasound computed tomography (USCT).

Colloquium coordinators are Prof.dr. A.J. van der Schaft (a.j.van.der.schaft rug.nl), Dr. A.V. Kiselev (e-mail: a.v.kiselev rug.nl)