Machine learning for image reconstruction - Hammernik, Kerstin; Knoll, Florian (Elsevier, 2019)
This chapter provides an overview of current developments in the fast growing field of machine learning for medical image reconstruction. A comprehensive overview of recent developments is provided for a range of imaging applications. The main focus lies on a mathematical understanding how deep learning techniques can be employed for image reconstruction tasks, and how they can be connected to traditional approaches to solve inverse problems. Approaches are categorized based on the properties of the underlying optimization problems that need to be solved during the image reconstruction process and the domain(s) in which the neural networks process the data. Additional material includes discussions on availability and size of existing training data, initiatives towards data sharing and reproducible research, and the evaluation of the performance of machine learning based medical image reconstruction methods.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Convolutional neural networks; Data-driven regularization; Deep learning; Image reconstruction; Inverse problems; Medical imaging; Numerical optimization