MAGNETOM World

AI in MR

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

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

Artificial Intelligence for MRI

Heinrich von Busch, Ph.D, Siemens Healthineers, Erlangen, Germany

Read article