Flow-based interpretation of dynamic contrast enhanced imaging
The measurement of perfusion and filtration are important clinical parameters used in diagnosis, follow-up, and therapy. The aim of this project is to investigate a novel approach for the interpretation of dynamic medical imaging with emphasis on blood distribution and flow. Typical applications include characterisation of strokes and planning and evaluation of cancer treatment.
Medical image acquisition techniques like computerised tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET), can all be applied in a dynamic setting where the evolving distribution of an injected contrast agent is gathered together as a temporal sequence of images. Quantitative tissue characterisation (e.g. blood perfusion) from such data is currently performed locally by applying tracer-kinetic methodology to a single region of interest (ROI) or a voxel at a time.
The tracer concentration for an individual voxel will be considered as a flow problem. By modelling the flow from first principles and calibrate the models to observations via systematic assimilation techniques, our goal is to advance understanding and clinical utility of dynamic imaging interpretation.
In addition, this research aims to produce knowledge and technology that contributes to ICT solutions for enhancing productivity and efficiency within the health sector.
To constuct the geometry of the model, we have been working with techiques to detect blood vessels from MR images. To this goal, we have been in contract with a research group in Jena that has developed a MR-sequence that is better suited to segmentation of blood vessels than those from our project partners.
At the same time, we are working on how to join together disconnected segmented parts of blood vessels and how to propagate the vessels below the resolution of MR images.
On the perfusion side, we have been working with two different models and performed several simulations, both on sections of the brain (3D) and on a frog tongue (2D). Simulation and validation of the models is ongoing.