X-ray tomography has developed into an advanced field of experimental research, utilizing not just the absorption contrast, but also phase, chemical and directional information to characterize the interior structure of the scanned object. Achieving the best possible results is becoming more and more an interdisciplinary effort, combining state-of-the-art experimental hardware, careful experiment design, mathematical modeling, customized algorithms and high performance computing.
Combining expertise from all these domains in a single focused research effort is a challenging task. The interplay between the many disciplines involved in this field is complex and the goals of the different communities are often conflicting. On one side of the spectrum, there are the experiments, which often lack quantifiable goals and quality measures. On the other side of the spectrum, there are purely mathematical results for which the route towards practical applications is often unclear.
In this lecture I will present our efforts at CWI Amsterdam, and at the Vision Lab in Antwerp to develop software, methodology, and algorithms aimed at making advanced computational methods directly applicable to large, high-end experimental datasets. As concrete use cases, I will outline how advanced computation can lead to significant improvements in image quality for 4D (time-resolved) tomography of evolving structures and low-dose tomography of discrete objects.