Self-learning MR

Sequence Discovery & Optimization - where we come from, where we go.

Session duration:	60m - 90m
Intended audience:	Physicists, Radiologists, AI-folks

Moderators:	Moritz Zaiss, Shaihan Malik (Confirmed: Zaiss, Malik)
First Speaker:	Tony Stöcker (Confirmed: yes)
Second Speaker:	Or Perlman (Confirmed: yes)
Third Speaker:	Moritz Zaiss (Confirmed: Yes)
Fourth Speaker:	Shaihan Malik (Confirmed: Yes)

Session description

In recent years, learning-based reconstruction and post-processing have transformed clinical MRI—making scans faster, sharper, and more informative. However, as data processing continues to improve, the focus is shifting back to data acquisition. With smaller artifacts becoming more apparent, we need to fix them again in acquisition. With reconstruction methods extracting all available information, the frontier of generation of new contrast features gets a revival.

This session addresses all kind of automized sequence discovery or optimization approaches - from historic EPG-based flip angle train optimization, to MRF schedules - trying to put it under the name "Self-learning MR". Yet, self-learning MR has many names: Optimal Control, End-to-End Optimization, Known-Operator Learning, Physics-Informed AI, Strategy Discovery, Optimal Design of Experiments, Dynamic Inverse Problems

While learning-based methods are well-known on the reconstruction side, we explicitly focus here on the MR preparation/acquisition side in the broadest sense. Of course, some tailored acquisitions can go hand in hand with tailored reconstruction.

All materials are shared via github: https://github.com/mritogether/ESMRMB2025_Self_learning_MR

The program will be the following:

Program for first hour: 4 talks

Welcome and Definition of the session (Focus on Acquisition, beyond k-space patterns and recon) (Shaihan and Moritz) 2 mins
Tony Stöcker, DZNE, Bonn: “EPG-based signal modeling and sequence optimization” (15 min)
Shaihan Malik, KCL, London: “Parallel Transmit, from fields to excitations to sequences” (15 min)
Moritz Zaiss, FAU, Erlangen: “End-to-end learning with MR-zero” (15 min)
Or Perlman, Tel Aviv University, “Towards Automatically Optimized Multi-Metabolite CEST Fingerprinting – When a Computational Graph Meets Proton Exchange" (15 min)

Program for the second half an hour: demos of open source “self-learning” tools

Svenja Niesen "Optimize it! EPGs in Python" (5 min) Paper: Availabe soon. Code:https://github.com/mrphysics-bonn/epg-tutorial
Jonathan Endres – Pulseq-zero (5 min): Paper: https://github.com/pulseq-frame/pulseq-zero/blob/main/abstract/abstract.md Code: https://github.com/pulseq-frame/pulseq-zero DSC + DESC
Felix Glang – Trajectory Optimization (5 min):

Paper: https://archive.ismrm.org/2021/4200.html Code:

Dario Bosch – fast pTX (5 min) Paper: https://doi.org/10.1007/s10334-023-01134-7 Code: https://github.com/dabosch/FastPtx
Nikita Vladimirov: – CEST MRF for everyone (5 min) Paper: https://doi.org/10.1038/s41596-025-01152-w Code: https://github.com/momentum-laboratory/molecular-mrf
Virtual:
- Jannik Stebani/Martin Blaimer - Optimization of quantification using Cramer-Rao Lower bound (5 min) Paper: Code:https://github.com/HarmonizedMRI/OpenMRF