ESMRMB Lectures on MR Online Course 2022: Machine Learning in MR Imaging
The course takes place over 2 days:
On day 1, we provide a basic introduction to machine learning for imaging, ranging from classification to regression. We introduce basic building blocks to build your first neural network, in both real-valued and complex-valued domains. Once the neural networks are defined, we will talk about training procedures, loss functions, and database selection. The presented building blocks are then linked to MR image reconstruction. We have a quick recap of the basics in MRI reconstruction, and we will then see the basic building blocks in action. Recent developments and applications on machine learning in MR reconstruction are highlighted. Hands-on examples are provided, including guided tutorials, and tasks that are carried out as homework.
On day 2, we present the solutions to the homework. We end our course with presentations of invited speakers, talking about efficient learning strategies and practical implementations of machine learning applications in MRI.
- Learn the basics of machine learning
- Learn how to build and train a neural network
- Link machine learning to MR reconstruction
- Learn how to process complex-valued data
- Learn about recent machine learning applications for MR reconstruction
Kerstin Hammernik
Artificial Intelligence in Healthcare and Medicine, Technical University of Munich, Germany
Department of Computing, Imperial College London, United Kingdom
k.hammernik [at] tum.de
Thomas Küstner
Medical Image and Data Analysis (MIDAS.lab), Unversity Hospital of Tübingen, Germany
thomas.kuestner [at] uni-tuebingen.de
- The basics of MRI reconstruction -
Workbook_MRI_reconstruction.ipynb
Data were acquired on a 3T Siemens Magnetom Vida at the Institute of Biomedical Imaging, Graz University of Technology, Austria. Data should be only used for educational purpose.