Biosketch
Natascha Niessen pursues her PhD project in a joint collaboration between GE Healthcare and the Chair of Computational Imaging and AI in Medicine at TU Munich, as well as the department of psychiatry at LMU.
As part of her French-German double-degree she received her Engineering Diploma (M.Sc.) from CentraleSupélec and her M.Sc. in Electrical Engineering from TU Munich with a focus on medical imaging and machine learning. In her Master‘s thesis at Stanford University, she developed a novel approach for validating multi- compartment fitting algorithms for brain magnetic resonance imaging (MRI). Her research interests lie in the development of Deep Learning-enabled MRI reconstruction and early Alzheimer’s Disease prediction as part of the European PREDICTOM project.
relAI Research
Deep learning-enabled Magnetic Resonance Imaging for early Alzheimer’s Disease Prediction
Natascha’s research focuses on accelerating quantitative MRI sequences to assess tissue properties in neurodegenerative diseases such as Alzheimer’s disease. Reducing scan times is essential to improve clinical workflow, increase patient throughput, and minimize motion-related image degradation. She develops an advanced MRI acquisition strategy that shortens the scan time of the MPnRAGE MRI sequence by acquiring substantially less raw data than conventional protocols. To maintain high image quality despite this reduction, she applies a deep learning based reconstruction approach using implicit neural representations. This accelerated MPnRAGE sequence enables the acquisition of a novel dataset within the PREDICTOM study at LMU Klinikum Großhadern and provides a valuable foundation for research on the early detection and characterization of Alzheimer’s disease.