Multi-scale coupling of vascular hemodynamics for AI-based standardized evaluation of neurological pathologies PIs: Prof. Sylvia Saalfeld, PD Dr.-Ing. habil. Philipp Berg – SPP2311

Schematic illustration of the desired project plan containing the addressed scales. In the beginning of the project the focus lies on modeling (considering different scales in space and time) and at the end of the project course, the clinical usability is assessed.

Aim:

The project “SCALE” aims at a precise and patient-specific description of neurovascular pathologies considering multi-scale hemodynamic modelling. Furthermore, AI-based methods for a standardized evaluation and the development of a clinically usable scoring systems will be applied.

Description:

Neurovascular diseases can lead to severe limitations and disabilities in affected individuals and are also among the leading causes of death in Germany. Particularly patient-specific changes in the cerebral vessels are expressed, for example, in the form of so-called intracranial aneurysms (permanent, balloon-like vessel bulges) or arteriovenous malformations (abnormal connections of arterial and venous vessels without capillary bed). Although continuously evolving imaging modalities enable a reliable diagnosis, individual risk assessment is highly complex, subject to numerous influencing variables, and too simplified in clinical practice due to the lack of models. As a result, the optimal treatment decision is challenging.
In the context of this research project, a holistic approach to evaluate neurovascular pathologies shall be realized by means of multi-scale modeling. First, the cardiovascular hemodynamics are described by means of a one-dimensional model. Subsequently, the neurovascular circulation and the venous system are mapped in 3D using computational fluid dynamics. Through this highly individualized and AI-assisted approach, the aforementioned pathologies can be precisely described morphologically and hemodynamically to computationally track their growth and remodeling processes along the time scale. For the time scale, time-dependent 4D flow measurements and tomographic image data are employed as well as longitudinal studies.
After the successful realization of the modeling “from the aorta to the vein”, the project aims to standardize the developed in-silico models via a usability module within the framework. In parallel, high-resolution in vitro validation measurements will be performed to ensure the plausibility of the models. Finally, the transfer of the developments into a scoring system is planned in order to prepare an application in the clinical environment. The standardization as well as the scoring system will exploit methods based on artificial intelligence (AI). This comprises the image- and mesh-based preprocessing and evaluation of flow simulation (with focus on deep learning) as well as the classification of extracted parameters (with focus on machine learning).
In summary, the planned holistic approach to assess neurovascular pathologies enables a highly interdisciplinary combination of patient-specific hemodynamics with medical imaging, AI-based image processing and evaluation, and simulative description. Consequently, transferring these influencing variables and conditions into a standardized assessment system can enable a precise and risk-free assessment of the actual disease state for the patient.