Abstract
Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate high quality images with a considerable speed-up compared to classical reconstruction methods. This is especially true for model-based learned (iterative) reconstruction schemes. However, applicability to large scale inverse problems is limited by available memory for training and extensive training times.

In this talk I will discuss applicability of learned image reconstruction approaches to tomographic data. In particular we will discuss various imaging scenarios and modalities, suitable approaches to design a robust learning task, as well as some solutions to obtain scalable learned image reconstruction for large scale and high dimensional data.

This CMIH seminar will take place in Meeting Room 11, CMS from 2pm