MED4D

MED4D is a collaborative project involving Ruhr University Bochum (Prof. Holger Dette, PD Dr Nicolai Bissantz), the University of Lübeck (Prof. Jan Modersitzki, Dr Stefan Heldmann), and the University of Münster (Prof. Martin Burger, Prof. Klaus Schäfers, Dr Hendrik Dirks, Dr Frank Wübbeling). The project focuses on developing algorithms and applying them to dynamic tomographic imaging.

Clinical relevance is ensured by the clinical partners Münster University Hospital and University Medical Center Schleswig-Holstein, the technology partner Siemens Healthineers, and the pharmaceutical partner Novartis.

The project was funded by the Federal Ministry of Education and Research as part of the High-Tech Strategy 2020, Healthy Living, and administered by the project management organisation DESY – Deutsches Elektronen-Synchrotron.

The project ran from 1 November 2016 to 30 November 2019. The project has been completed.

Motivation

Detailed knowledge of processes and developments provides fundamental insights into the function of a living organism and possible pathologies. Modern tomographic imaging techniques provide non-destructive, spatially resolved views. However, these images can be severely corrupted by motion artefacts caused by breathing or the heartbeat. This is a major reason why modern quantitative imaging techniques that are already standard for the brain (neuroimaging) have so far only rarely been applied to organs that move substantially, such as the heart, kidneys, lungs, or liver.

Our project applies these techniques to selected cardiovascular (heart and circulation) and renal (kidney) problems in positron emission tomography (PET) and magnetic resonance imaging (MRI).

Heart example

Medicine, image processing, and mathematics interact directly in examinations of the heart. Medicine defines the diagnostic question, such as whether cardiac motion is impaired or blood flow to the heart muscle is reduced. MRI and CT scanners measure signals from many directions and at different points in time. Image processing corrects motion, separates anatomical structures, and combines image sequences. Mathematical models and reconstruction methods solve the underlying inverse problem: they turn incomplete and noisy measurements into spatial and temporal representations that can be assessed medically.

Kidney example

Kidney imaging also begins with a medical question, such as assessing the organ's shape, tissue structure, blood flow, or function. Ultrasound, CT, MRI, and nuclear medicine techniques provide different types of measurement data. Image processing reveals organ boundaries and internal structures, registers images over time, and reduces artefacts. Mathematics models how the measured signals propagate, stabilises the reconstruction, and quantifies changes. In this way, physical raw data are transformed into reliable images and measurements that support medical decisions.