This project consists of the development of a grating-based phase contrast mammography prototype for the in-vivo investigation of breast cancer. Clinically, we aim at improving the diagnostic power of mammography by exploiting the additional information provided by differential phase and dark-field signals.
In this project, we aim to investigate and promote the use of X-ray phase contrast microtomography as a complementary method for histopathological techniques. Exploiting the higher sensitivity of X-ray phase contrast is particularly suited for biological soft tissues, for which ordinary X-ray absorption does not provide enough image contrast.
We are developing a large-field-of-view grating-based CT system, with parameters suitable for a clinical dedicated breast imaging. The design is a compact gantry rotating around a breast of a patient lying on a bed directly above it. We aim to prove the diagnostic value of phase contrast in breast cancer diagnostics. We collaborate with GratXray, a spin-off with roots in the group, on bringing the technology to clinics.
This project focuses on the development of data processing and reconstruction pipelines to generate high quality reconstructions from sparse and highly noisy data obtained with the scanning protocols foreseen for first clinical tests. In particular, given the challenging imaging conditions, this project attempts to achieve this goal by developing customized data-driven deep learning algorithms to tackle the challenging ill-posed inverse problems that arise in grating interferometry breast computed tomography.
This research focuses on developing an iterative algorithm for the Grating-Interferometry-Breast-CT (GI-BCT), which allow us acquire high quality image and high acquisition speed for low does and large data. We aim to apply this algorithm to clinical practice and improve the detection and diagnosis of breast cancer.
X-ray scattering imaging can give access to microstructural information for features well below the setup resolution, in a large field of view, making this technique very interesting for the investigation of new materials. The objective of this study is to extend 2D omni-directional X-ray scattering imaging to 3D without need for a priori knowledge on the scatterer shape and/or space organization.
Catalyst layer is the smallest and the most critical component of a fuel cell. Understanding of the liquid water behavior in catalyst layers of PEFC bears a great potential for further improvement. In this work, X ray dark field imaging based on dual phase grating interferometry is used to access unresolvable structural information in such materials, which is otherwise inaccessible to conventional absorption-based full field imaging.
The main goal of this project is to design and implement a lab-based dual-phase grating interferometer (DP-XGI) for a multi-scale characterization of mineral building materials (MBM) and wood-based materials (WBM). Taking advantage of the tunability of the dark-field signal, we pursue to analyze the scattering objects with features in a range of hundreds of nano-meters, which is well beyond the intrinsic system resolution.
Based on quantum mechanics a new GI Monte Carlo simulation framework for GI setups is developed in this work, with the aim to simulate scattering and interference phenomena within one framework. After a proof of principle on smaller scales with flat gratings, the algorithm will be extended for the simulation of clinically relevant volumes and extended to bent gratings.