MR Academy: Physics & Engineering

Overview: With the drives towards faster, higher resolution, and quantitative imaging, MRI scientists are increasingly facing scenarios where conventional Fourier transform-type reconstruction methods are unable to generate high quality images from raw data collected by the scanner.  Advanced reconstruction methods like Compressed Sensing appear promising for such applications, but how to bridge the gap between the standard, basic methods and these is often unclear. The goal of this mini-course is to introduce students to this exciting class of advanced reconstruction methods and instill a basic working understanding of key techniques.

Physics & Engineering

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Title & Link Video Quiz Speaker Year Difficulty Audience Type
Reconstruction Methods for Undersampled Data Jeff Fessler, Ph.D. 2011 Intermediate Physicists & Engineers
Compressed Sensing in MRI Michael Lustig, Ph.D. 2013 Intermediate Physicists & Engineers
MR Fingerprinting Dan Ma, Ph.D. 2017 Intermediate Physicists & Engineers
Insights into Learning-Based MRI Reconstruction Kerstin Hammernik, Ph.D. 2017 Intermediate Physicists & Engineers

Machine Learning in MRI

This course was jointly created by the Cross-Cutting & Emerging Technologies and the Physics and Engineering tables. The questions and videos are the same.

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Title & Link Video Quiz Speaker Year Difficulty Audience Type
What Exactly Is Deep Learning? Bradley J. Erickson, M.D.,Ph.D. 2018 Beginner Physicists & Engineers
Nuts & Bolts: How Does DL Work? Thomas Pock, Ph.D. 2018 Beginner Physicists & Engineers
Limitations & Caveats of Deep Learning Jeffrey A. Fessler, Ph.D. 2018 Beginner Physicists & Engineers
Applications: Image Acquisition & Reconstruction Lei Ying, Ph.D. 2018 Beginner Physicists & Engineers
Applications: Image Processing, Analysis & Interpretation Daniel Rueckert, Ph.D. 2018 Beginner Physicists & Engineers
How to Jump Start Your Deep Learning Research Florian Knoll, Ph.D. 2018 Beginner Physicists & Engineers