# AI in MRI: A case for grassroots deep learning

Kurt G. Schilling, Bennett A. Landman

Magnetic Resonance Imaging · 2019 · Commentary

https://doi.org/10.1016/j.mri.2019.07.004

## Summary

This commentary introduces a special issue on machine learning in MRI, bringing together eighteen perspectives spanning image acquisition, processing, and modeling.

## Study overview

Schilling and Landman discuss the growing role of data-driven methods in MRI while introducing eighteen contributions to a special issue. The commentary frames developments across acquisition, image processing, and modeling from the perspective of researchers applying machine learning to practical imaging problems.

## Findings / topics

- The special issue spans machine learning approaches to MRI acquisition, processing, and modeling.

## Resources

- [Original work](https://linkinghub.elsevier.com/retrieve/pii/S0730725X19304370)
- [PDF](https://pmc.ncbi.nlm.nih.gov/articles/PMC8278255/pdf/nihms-1721243.pdf)
- [Open full text](https://pmc.ncbi.nlm.nih.gov/articles/PMC8278255/)

Lab page: https://www.microstructure-connectivity-lab.com/publications/ai-in-mri-grassroots/

This is a research summary, not the full article.