The application of AI methods in acquisition, reconstruction, post-processing or analysis are investigated. We develop reliable, robust, specific, and sensitive AI methods. The inclusion of AI into medical data processing can help to improve performance by (but not limited to) increasing precision, boosting quality of service, easing processing, reducing computational times, and reducing energy consumption.
As the demand for medical imaging grows, the imaging-related energy consumption and sustainable operation is becoming a pressing concern for radiology departments and practices. The development of efficient acquisition strategies with multi-parametric and dynamic imaging is thus essential. In this regard, one-stop-shop imaging solutions and AI-based processing (reconstruction, handling of motion, image quality control) enable a more comprehensive non-invasive tissue and metabolism characterization.
The aim is to provide an improved workflow with automatic data handling to derive clinical biomarkers that can be used in diagnosis.