Objectives

The aim of this collaborative project is to develop and analyse mathematical models that enable spatially resolved evaluation of dynamic medical images and relate the results to clinically relevant parameters. The project addresses dynamic imaging: imaging methods in which multiple images or indirect measurements are acquired of an object that is moving and/or undergoing metabolic or physiological change. We will develop and apply mathematical models and methods to separate motion from kinetic change, analyse each independently, and distinguish different types of motion.

Cardiovascular studies

The main cardiovascular objective is to image physiological parameters such as perfusion, glucose metabolism, and innervation. Spatially resolved measurements of myocardial perfusion provide important information about cardiac dysfunction. Here, perfusion means the volume of blood flowing per unit time through a given mass of biological tissue. Detecting local regions of low perfusion at rest and, especially, under stress conditions (perfusion reserve) is central to the early detection of heart disease and to monitoring treatment, for example after coronary revascularisation following ischaemia or infarction. Disturbances in innervation, particularly of the right ventricular myocardium, often cause arrhythmias, but remain insufficiently studied in several diseases (cf. [63]). In cardiac imaging, motion is not merely an artefact to be corrected; it is also of intrinsic interest. Mapping local motion can reveal cardiac function and help diagnose dysfunction.

Renal studies

The objective is to determine tissue-specific physiological measures of kidney function from dynamic contrast-enhanced MRI (DCE-MRI) sequences. Assessment of kidney dysfunction remains unsatisfactory. The glomerular filtration rate (GFR) is the most important clinical parameter (cf. [65]), but standard protocols provide only a global value for each patient. Early and detailed information about reduced GFR is central to improving diagnosis and monitoring treatment (cf. [61]). Early detection requires spatially resolved information about GFR, for example from contrast-enhanced MRI.