The developments of the last few years in computational hardware as well as algorithms, in particular the advent of methods suitable for training complex neural networks (Deep Learning), have opened up new possibilities for machine learning and automation in many applications. Magnetic Resonance imaging, postprocessing and interpretation, in fact radiology in general, are no exception.
The Future of Digital Technology in Medicine
Mark A. Griswold; Case Western Reserve University, Cleveland, OH, USA
MR Imaging in the Era of Deep Learning
Florian Knoll; NYU School of Medicine, Center for Advanced Imaging Innovation and Research (CAI2R), New York, NY, USA
Deep Learning for Parallel MRI Reconstruction: Overview, Challenges, and Opportunities
Kerstin Hammernik, et al., Imperial College London, UK
Deep Learning for Cardiovascular MR Image Reconstruction
Jennifer A. Steeden, Ph.D., University College London, UK, Lunch Symposium ESMRMB 2019, Rotterdam, NL
Exploring New Frontiers in MRI
Sascha Daeuber, Ph.D., Siemens Healthineers Lunch Symposium ESMRMB 2019, Rotterdam, NL
“It’s the data, stupid!” – Unlock the Potential of AI/ML in Radiology through Big Data Approaches
Elmar Merkle, University Hospital Basel, Switzerland
Artificial Intelligence in prostate MRI
Kyung Hyun Sung, Ph.D., University of California, Los Angeles, USA, 10th MAGNETOM World Summit
Artificial Intelligence for MRI
Heinrich von Busch, Ph.D, Siemens Healthineers, Erlangen, Germany