The development of fast and accurate reconstruction algorithms is a central mathematical aspect of imaging. Most traditional image reconstruction methods can basically be classified in either analytical or iterative methods. Recently, a new class of image reconstruction methods appeared which use methods from machine learning, especially from deep learning. Initial results using deep learning techniques for image reconstruction demonstrate great promise, for example, for improving image quality, reducing computation time, or reducing radiation exposure. In this minisymposium leading experts will report on recent progress towards using machine learning for image reconstruction.

Finding best approximation pairs with Douglas-Rachford

Irene Waldspurger (CEREMADE (Université Paris-Dauphine))

Deep Learning Approaches for MR Image Reconstruction

Daniel Rueckert (Imperial College London)

Deep convolutional framelets: a general deep learning framework for inverse problems

Jong Chul Ye (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology)

Deep Learning for Photoacoustic Tomography from Sparse Data

Johannes Schwab (Universität Innsbruck)

Deep learning in computational microscopy

Yair Rivenson (University of California Los Angeles)

Task Based Reconstruction using Deep Learning

Ozan Öktem (KTH - Royal Institute of Technology)

Machine learning in compressed sensing

Stephan Antholzer (University of Innsbruck)

The cone-beam transform and spherical convolution operators

Michael Quellmalz (Technische Universität Chemnitz)

Organizers:

Markus Haltmeier (University Innsbruck)

Linh Nguyen (University of Idaho)

Keywords:

computed tomography, deep learning, image deblurring, image reconstruction, inverse problems, machine learning