Prof. Dr. Florian Knoll

Topic: Physics Informed Neural Networks for Predicting Physical Systems Behavior

Affiliation: Department Artificial Intelligence in Biomedical Engineering (AIBE)

About

Prof Knoll develops and applies machine learning methods to medical imaging, and their translation into clinical practice so that they can help patients on a day-to-day level. In particular, he is interested in data acquisition and image reconstruction methods that make magnetic resonance imaging faster, more robust against image artifacts, allow imaging of new anatomical or pathological processes, make image interpretation easier and more standardized by moving from qualitative image contrasts to quantitative biomarkers for disease processes, and increase its global availability and accessibility. He serve sas the deputy editor of Magnetic Resonance in Medicine for articles from this type of research.

As a strong supporter of reproducible research in the field of imaging, he currently serves as the outgoing chair for the ISMRM reproducible research study group. During his time at NYU he also initiated and served as the scientific lead of the fastMRI data sharing initiative, and the associated image reconstruction challenge. In collaboration with Facebook Artificial Intelligence Research, a dataset of raw k-space data for more than 1300 knee MRI scans and more than 7000 brain MRI scans was made available.

Code to some of his papers can be obtained from:

• https://github.com/FlorianKnoll (Matlab and Python)
• Model based DTI reconstruction (Matlab)
• AGILE (CUDA)
• IRGN-TGV (Matlab)
• gpuNUFFT (CUDA and Matlab)
• Second order Total Generalized Variation (TGV) constrained reconstruction (Matlab).

A complete list of his publications is available on google scholar.