Self-learning MR - Revision history

MoritzZaiss at 12:50, 29 September 2025

2025-09-29T12:50:21Z

← Older revision		Revision as of 14:50, 29 September 2025
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	\| Speaker2= Or Perlman		\| Speaker2= Or Perlman
	\| Speaker2_Confirmed= yes		\| Speaker2_Confirmed= yes
	\|Speaker3=Moritz Zaiss\|Speaker4=Shaihan Malik}}		\|Speaker3=Moritz Zaiss\|Speaker4=Shaihan Malik\|Speaker3_Confirmed=Yes\|Speaker4_Confirmed=Yes}}

	<!-- Add the full description of the session here below -->		<!-- Add the full description of the session here below -->

MoritzZaiss at 12:49, 29 September 2025

2025-09-29T12:49:34Z

← Older revision		Revision as of 14:49, 29 September 2025
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	\| Speaker2= Or Perlman		\| Speaker2= Or Perlman
	\| Speaker2_Confirmed= yes		\| Speaker2_Confirmed= yes
	}}		\|Speaker3=Moritz Zaiss\|Speaker4=Shaihan Malik}}

	<!-- Add the full description of the session here below -->		<!-- Add the full description of the session here below -->
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	* '''Or Perlman, Tel Aviv University, “Towards Automatically Optimized Multi-Metabolite CEST Fingerprinting – When a Computational Graph Meets Proton Exchange"''' (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: ~~4-6~~ demos of open source “self-learning” tools ===		=== 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:<nowiki>https://github.com/mrphysics-bonn/epg-tutorial</nowiki>		* '''Svenja Niesen "Optimize it! EPGs in Python"''' (5 min) Paper: Availabe soon. Code:<nowiki>https://github.com/mrphysics-bonn/epg-tutorial</nowiki>
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	* '''Dario Bosch – fast pTX''' (5 min) Paper: <nowiki>https://doi.org/10.1007/s10334-023-01134-7</nowiki> Code: <nowiki>https://github.com/dabosch/FastPtx</nowiki>		* '''Dario Bosch – fast pTX''' (5 min) Paper: <nowiki>https://doi.org/10.1007/s10334-023-01134-7</nowiki> Code: <nowiki>https://github.com/dabosch/FastPtx</nowiki>
	* '''Nikita Vladimirov: – CEST MRF for everyone''' (5 min) Paper: <nowiki>https://doi.org/10.1038/s41596-025-01152-w</nowiki> Code: <nowiki>https://github.com/momentum-laboratory/molecular-mrf</nowiki>		* '''Nikita Vladimirov: – CEST MRF for everyone''' (5 min) Paper: <nowiki>https://doi.org/10.1038/s41596-025-01152-w</nowiki> Code: <nowiki>https://github.com/momentum-laboratory/molecular-mrf</nowiki>
			* Virtual:
			** '''Jannik Stebani/Martin Blaimer - Optimization of quantification using Cramer-Rao Lower bound''' (5 min) Paper: Code:<nowiki>https://github.com/HarmonizedMRI/OpenMRF</nowiki>

MoritzZaiss: /* Session description */

2025-09-29T12:40:12Z

Session description

← Older revision		Revision as of 14:40, 29 September 2025
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	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.		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 ~~pulse design, and~~ 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		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

	~~Potential topics could be:~~		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.
	~~Self-learning MRF / CEST MRF / MRS~~
	~~Self-learning trajectories~~
	~~Self-learning contrast, sharpness~~
	~~Self-~~learning ~~over a real scanner~~
	~~Self~~-~~learning imperfection mitigation~~
	~~Self~~-~~learning pulses~~, ~~pTX~~/~~SMS pulses~~
	~~Self-learning flip angle trains~~, ~~phases~~

	~~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

	~~Speakers~~:		The program will be the following:
	~~Tony Stöcker - classic EPG-based optimizations of TSE, SFFP. pTX pulse design.~~
	~~Or Perlman - Overview on MRF, autoseq, and autoCEST.~~
	~~Moritz Zaiss – End-to-end sequence design~~

	~~We already have an interest group mailing list with 19 groups/people, who I could invite to submit an abstract if this session gets chosen.~~		=== Program for first hour: 4 talks ===

	~~The program will be the following~~:

	~~Option 1~~: (1:~~30h~~)		* 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)

	*Presentation by Tony Stöcker on history of ~~EPG~~-~~based optimization + advances + pTx ''(20 min)''~~		=== Program for the second half an hour: 4-6 demos of open source “self-learning” tools ===
	* Presentation by Moritz Zaiss on end to end learing ''(10 min)''
	* Presentation by Or Perlmann on AutoCEST''(10 min)''
	* Selected ESMRMB Abstracts that fit to the topic: ''(50 min)''

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

	~~Option 2~~: ~~(1h)~~		Paper: <nowiki>https://archive.ismrm.org/2021/4200.html</nowiki> Code:

	* ~~Presentation by Tony Stöcker on history of EPG-baased optimization + advances~~ ''~~(15 min)~~''		* '''Dario Bosch – fast pTX''' (5 min) Paper: <nowiki>https://doi.org/10.1007/s10334-023-01134-7</nowiki> Code: <nowiki>https://github.com/dabosch/FastPtx</nowiki>
	* Presentation by N. Boulant or Tony Stöcker on Ptx pulses ''(10 min)''		* '''Nikita Vladimirov: – CEST MRF for everyone''' (5 min) Paper: <nowiki>https://doi.org/10.1038/s41596-025-01152-w</nowiki> Code: <nowiki>https://github.com/momentum-laboratory/molecular-mrf</nowiki>
	* ~~Presentation by Moritz Zaiss on end to end learing~~ ''~~(15 min)~~''
	* Presentation by Or Perlmann on AutoCEST''(15 min)''
	* Discussion ''(5 min)''

MriFranz: Accepted

2025-05-07T12:33:30Z

Accepted

← Older revision		Revision as of 14:33, 7 May 2025
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			[[Category:Accepted_MS_2025]]

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MoritzZaiss: speakers confirmed

2025-02-24T08:47:27Z

speakers confirmed

← Older revision		Revision as of 10:47, 24 February 2025
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	\| Moderators_Confirmed= Zaiss, Malik		\| Moderators_Confirmed= Zaiss, Malik
	\| Speaker1= Tony Stöcker		\| Speaker1= Tony Stöcker
	\| Speaker1_Confirmed= No		\| Speaker1_Confirmed= yes
	\| Speaker2= Or Perlman		\| Speaker2= Or Perlman
	\| Speaker2_Confirmed= No		\| Speaker2_Confirmed= yes
	}}		}}

MoritzZaiss: duration

2025-02-21T17:08:47Z

duration

← Older revision		Revision as of 19:08, 21 February 2025
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	\| title= Self-learning MR		\| title= Self-learning MR
	\| short_text= Sequence Discovery & Optimization - where we come from, where we go.		\| short_text= Sequence Discovery & Optimization - where we come from, where we go.
	\| duration= 60m		\| duration= 60m - 90m
	\| audience= Physicists, Radiologists, AI-folks		\| audience= Physicists, Radiologists, AI-folks
	\| Moderators= Moritz Zaiss, Shaihan Malik		\| Moderators= Moritz Zaiss, Shaihan Malik

MoritzZaiss: /* Session description */ updated

2025-02-21T17:08:25Z

Session description: updated

← Older revision		Revision as of 19:08, 21 February 2025
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	The program will be the following:		The program will be the following:

			Option 1: (1:30h)

			*Presentation by Tony Stöcker on history of EPG-based optimization + advances + pTx ''(20 min)''
			* Presentation by Moritz Zaiss on end to end learing ''(10 min)''
			* Presentation by Or Perlmann on AutoCEST''(10 min)''
			* Selected ESMRMB Abstracts that fit to the topic: ''(50 min)''


			Option 2: (1h)

	* Presentation by Tony Stöcker on history of EPG-baased optimization + advances ''(15 min)''		* Presentation by Tony Stöcker on history of EPG-baased optimization + advances ''(15 min)''
	* Presentation by N. Boulant or Tony Stöcker on Ptx pulses ''(15 min)''		* Presentation by N. Boulant or Tony Stöcker on Ptx pulses ''(10 min)''
	* Presentation by Moritz Zaiss on end to end learing ''(15 min)''		* Presentation by Moritz Zaiss on end to end learing ''(15 min)''
	* Presentation by Or Perlmann on AutoCEST''(15 min)''		* Presentation by Or Perlmann on AutoCEST''(15 min)''
	* Discussion ''(15 min)''		* Discussion ''(5 min)''

MoritzZaiss: malik comnfirmed

2025-02-21T16:58:34Z

malik comnfirmed

← Older revision		Revision as of 18:58, 21 February 2025
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	\| audience= Physicists, Radiologists, AI-folks		\| audience= Physicists, Radiologists, AI-folks
	\| Moderators= Moritz Zaiss, Shaihan Malik		\| Moderators= Moritz Zaiss, Shaihan Malik
	\| Moderators_Confirmed= Zaiss		\| Moderators_Confirmed= Zaiss, Malik
	\| Speaker1= Tony Stöcker		\| Speaker1= Tony Stöcker
	\| Speaker1_Confirmed= No		\| Speaker1_Confirmed= No

MoritzZaiss: This session addresses all kind of automized sequence discovery or optimization approaches - from historic EPG-based flip angle train optimization, to pulse design, and MRF schedules - trying to put it under the name "Self-learning MR".

2025-02-21T16:55:09Z

This session addresses all kind of automized sequence discovery or optimization approaches - from historic EPG-based flip angle train optimization, to pulse design, and MRF schedules - trying to put it under the name "Self-learning MR".

New page

{{Member Session
| title= Self-learning MR
| short_text= Sequence Discovery & Optimization - where we come from, where we go.
| duration= 60m
| audience= Physicists, Radiologists, AI-folks
| Moderators= Moritz Zaiss, Shaihan Malik
| Moderators_Confirmed= Zaiss
| Speaker1= Tony Stöcker
| Speaker1_Confirmed= No
| Speaker2= Or Perlman
| Speaker2_Confirmed= No
}}



== 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 pulse design, and 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

Potential topics could be:
Self-learning MRF / CEST MRF / MRS
Self-learning trajectories
Self-learning contrast, sharpness
Self-learning over a real scanner
Self-learning imperfection mitigation
Self-learning pulses, pTX/SMS pulses
Self-learning flip angle trains, phases

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.

Speakers:
Tony Stöcker - classic EPG-based optimizations of TSE, SFFP. pTX pulse design.
Or Perlman - Overview on MRF, autoseq, and autoCEST.
Moritz Zaiss – End-to-end sequence design

We already have an interest group mailing list with 19 groups/people, who I could invite to submit an abstract if this session gets chosen.

The program will be the following:

* Presentation by Tony Stöcker on history of EPG-baased optimization + advances ''(15 min)''
* Presentation by N. Boulant or Tony Stöcker on Ptx pulses ''(15 min)''
* Presentation by Moritz Zaiss on end to end learing ''(15 min)''
* Presentation by Or Perlmann on AutoCEST''(15 min)''
* Discussion ''(15 min)''