Hello,
People often ask me for educational material on MRI physics and algorithms, to get started with. This page contains links to some nice, basic-level papers and online courses that my team found useful.
I hope that it will help those who want to enter the field and anyone who wants to get more expertise in MRI.
Enjoy and let me know if you find any other helpful resources! 😀
Efrat
MRI physics
Youtube resources:
How MRI works – part 1 – the NMR basics
https://www.youtube.com/watch?v=TQegSF4ZiIQ
:How MRI works – part 2- the Spin Echo
https://www.youtube.com/watch?v=M7yh0To6Wbs
Books
Peder Larson's Principles of MRI book and associated GitHub repository
McRobbie et al.'s book (has many figures) - MRI From Picture to Proton, Second edition, Cambridge University Press (2003)
Online courses
Daniel Ennis's excellent introduction to MRI course: https://www.youtube.com/@MWinter-tf2xp/videos
Stanford's Rad229 course (advanced material) - Danniel Ennis & Brian Hargreaves's course: https://www.youtube.com/@stanford-rad2299
Michael Lipton's course: https://www.youtube.com/playlist?list=PLPcImQzEnTpz-5TzxyyoYSbiAa9xdd89l
Radiology tutorials - MRI physics course: https://www.youtube.com/playlist?list=PLWfaNqiSdtzVkfJW2gO-unAYjcDji7-9i
MRI reconstruction
The following papers describe some basic concepts and algorithms in the field of MRI reconstruction. Please note that this does not aim to provide a thorough literature review, as numerous methods exist. Instead, these are some papers that my team found useful for newcomers to the field.
Intro to parallel imaging and compressed sensing
Miki Lustig’s educational paper on Compressed sensing MRI: Compressed sensing MRI
Hamilton et al. (UMich group) - excellent review of the basics of MRI (parallel imaging, k-space trajectories etc) by the UMich group:
Recent advances in parallel imaging for MRI
Jeff Fessler's review on model-based reconstruction - also explains the basics of signal formation: Model-based image reconstruction for MRI
SPIRiT paper - explains parallel imaging, GRAPPA & SPRiT: SPIRiT: Iterative self‐consistent parallel imaging reconstruction from arbitrary k‐space
A review paper on compressed sensing for body MRI (NYU group): Compressed sensing for body MRI
Intro to deep learning for MRI reconstruction
Aggarwal et al., (2019): MoDL: Model Based Deep Learning Architecture for Inverse Problems
Hammernik et al. (2022): Physics-driven deep learning for computational magnetic resonance imaging
Heckel et al. (2024) - our review on deep learning for MRI reconstruction: Deep learning for accelerated and robust MRI reconstruction
Spieker et al. (2024) - an excellent review on deep-learning motion correction methods for MRI: Deep learning for retrospective motion correction in MRI: a comprehensive review