Thursday, October 17, 2019 - 4:15pm - 5:00pm
Florian Knoll (NYU Langone Medical Center)
In this talk, I will provide an introduction to the use of machine learning and convolutional neural networks (CNNs) in the area of MR image reconstruction. Building on a general framework of inverse problems and variational optimization, I will focus on application examples from image reconstruction for accelerated Magnetic Resonance (MR) imaging. I will cover both methodological developments as well as clinical translation and validation.
Friday, October 18, 2019 - 10:15am - 11:00am
Nicole Seiberlich (University of Michigan)
Magnetic Resonance Fingerprinting (MRF) is a novel approach to collecting quantitative maps of MRI tissue properties in an efficient manner. Instead of focusing on collecting images weighted by specific tissue properties and using them to extract quantitative information, MRF works by extracting these quantitative maps directly from rapidly collected MRI signals.
Tuesday, October 15, 2019 - 1:45pm - 2:30pm
Frank Ong (Stanford University)
Slides and jupyter notebooks are available at
Monday, October 14, 2019 - 2:55pm - 3:45pm
Mariya Doneva (Philips Research Laboratory)
This lecture gives an overview of methods for scan time reduction in quantitative MRI based on regularized image reconstruction. Besides the generic constraints that can be used for image series, the known signal model in quantitative MRI permits designing a model-based constraint tailored to the specific application. This is a much stronger prior knowledge, which, provided that the model is accurate, enables even higher accelerations and improved image quality.
Subscribe to RSS - MRI