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Meeting Abstract

MR-double-zero: Can a machine discover new MRI contrasts, such as metabolite concentration?

MPS-Authors
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Mueller,  S
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons230667

Glang,  F
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons216025

Herz,  K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84187

Scheffler,  K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons214560

Zaiss,  M
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://submissions.mirasmart.com/ISMRM2022/itinerary/ConferenceMatrixEventDetail.aspx?id=242
(Abstract)

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Citation

Mueller, S., Glang, F., Herz, K., Scheffler, K., & Zaiss, M. (2022). MR-double-zero: Can a machine discover new MRI contrasts, such as metabolite concentration? In Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM 2022).


Cite as: https://hdl.handle.net/21.11116/0000-000A-5CB7-1
Abstract
Discovery of MR contrast and/or conventional sequence parameter optimization usually requires a theoretical model to describe MR physics. Here we investigate if novel contrasts can be found by directly running numerical optimization on a real MRI scanner instead of a simulation. To this end, a derivative-free optimization algorithm is set up to repeatedly update and execute a parametrized sequence on the scanner and map the acquired signals to a given target contrast. As proof-of-principle, we show that this enables creatine concentration mapping by learning a CEST-prepared sequence, which is found solely based on known target concentrations in a phantom.