New Trends in Computational MRI: From Model Based to Data Driven Approaches in Data Acquisition and Image Reconstruction
IEEE Members: $349.00
Non-members: $459.00Length: 10:00:00
This course aims to provide a self-contained view of modern data acquisition and image reconstruction aspects in magnetic resonance imaging (MRI) from an engineering perspective with still physics based knowledge. To this end, it will specifically cover both model based and data driven computational approaches in MRI, concerning both accelerated data acquisition and image reconstruction strategies. It is specifically tailored to graduate students, researchers and industry professionals working in the medical imaging field who want to know more about the radical shift machine learning (ML) has introduced for MRI during the last few years. As MRI is the most widely used medical imaging technique for non-invasively probing soft tissues in the human body (brain, heart, breast, liver, etc), training PhD students, postdocs and researchers in electrical and biomedical engineering is strategic for cross-fertilizing the fields and for understanding the ML-related needs and expectations from the MRI side. In the last decade, the application of Compressed Sensing (CS) theory to MRI has received considerable interest and led to major improvements in terms of accelerating data acquisition without degrading image quality in low acceleration regimes. Two recent complementary research directions are starting to supplant this classical CS setting to reach highly accelerated regimes: First, the advent of deep learning solutions for MR image reconstruction, and second, the design of optimization and learning-based under-sampling schemes, notably non-Cartesian trajectories. Taken together, the combination of these approaches in a joint learning based framework offers new perspectives for valuable clinical applications.