Prof. Dr. Florian Knoll
The goal of my research is the development and application of 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, I am 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. I serve as the deputy editor of Magnetic Resonance in Medicine for articles from this type of research.
The research of our group is funded by the following grants:
- NIH R01EB024532: Machine learning MR image reconstruction for accelerated musculoskeletal imaging. With Patricia Johnson (NYU), Dana Lin (NYU) and Thomas Pock (Graz University of Technology)
- NIH R21EB027241: Quantitative multi-compartment MR-fingerprinting. With Jakob Asslaender (NYU).
- NIH P41EB017183 TR&D 1 of the Center for Advanced Imaging and Innovation (CAI2R). With Dan Sodickson (NYU).
- NIH R01EB029957: A comprehensive deep learning framework for MRI reconstruction. With Rizwan Ahmad (Ohio State University).
As a strong supporter of reproducible research in the field of imaging, I currently serve as the outgoing chair for the ISMRM reproducible research study group. During my time at NYU I 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, we made available a dataset of raw k-space data for more than 1300 knee MRI scans and more than 7000 brain MRI scans.
Code to reproduce the results for some of my 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).
I complete list of my publications is available on google scholar.