Medical University of Graz

Advances in medical imaging have enabled unprecedented ability to image cardiac anatomy and function. So far these technologies have had relatively modest clinical impact as the analysis of such rich multi-modal datasets has proven challenging. In silico models hold vast potential to better harness such datasets by enabling their integration into quantitative frameworks that can aid in gaining better mechanistic insight into cardiac function in health and disease, and thus paving the way towards optimal therapeutic strategies. Our objective is to develop the most advanced biophysically detailed in-silico model of total electro-mechano-fluidic function of the heart. This model will be parametrized, verified and used to study cause-effect relationships between flow and pressure and their impact upon pumping performance. A novel set of features such as combined models of both heart and attached outflow vessels and the computational efficiency will provide a unique platform for translational research. This ambitious endeavor is feasible only by combining the expertise of the applicant in modeling soft tissue mechanics and his supervisors in modeling electrophysiology (Gernot Plank, MUG) and blood flow (Shawn Shadden, UC Berkeley). Clinical input and datasets for model parametrization and validation are provided by Titus Kühne (DHZ Berlin) and by clinical collaborators of Prof. Shadden at UCSF. During the return-phase, the applicant will use the infrastructure of Prof. Plank’s lab and the large network of academic and industrial collaborations as an incubator for building up his own research group in computational hemodynamics. This is ideal in many regards, as the expertise of the applicant's group will be entirely orthogonal to the expertise in Prof. Plank's lab, thus promoting a fast pathway towards full indepence, and core expertise necessary for further developing and maintaining a highly complex computing environment is synergistically shared between the labs.

Multiple sclerosis (MS) is the most common autoimmune disorder to affect the central nervous system. Demyelination of nerve cells leads to a slowing of electrical signals, causing visual and sensorimotor deficits, cognitive decline and mood changes. Magnetic Resonance Imaging (MRI) plays a central role to in diagnosis through the identification of plaques and active lesions. Recently, functional MRI (fMRI) and Quantitative Susceptibility Mapping (QSM) have been shown to provide insights into the pathogenesis of MS and possible biomarkers of MS subtypes (relapsing-remitting (RR), primary progressive (PP) and secondary progressive (SP)). fMRI shows aberrant neuronal activation in response to motor tasks and changes to the motor resting state networks which are different in RR, PP and SP MS, for instance, whereas QSM provides images of the magnetic susceptibilities of different tissues, revealing iron deposits and demyelination. This action proposes the development of a new MRI sequence which will allow fMRI and QSM data to be acquired simultaneously rather than in two separate scans. This will drastically reduce the scan time, which is vital as many MS patients find it hard to stay still during an MRI. The combined fMRI-QSM sequence used to examine, in different MS subtypes and genders, reorganization of motor function and disruption of functional connectivity (from fMRI) in relation to the distribution of plaques, iron and demyelination (from QSM). The sequence programming in this interdisciplinary project will be carried out under the supervision of Prof. Barth, a physicist who developed the MR method on which the fMRI-QSM scan will be based, at the University of Queensland. In the return phase Prof. Enzinger, one of Europe’s leading MS neurologists, will supervise the clinical study with the combined sequence. This action stands to lead to a step-change in the international standing of the researcher and benefit the host and the European research area.