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MR-double-zero: proof-of-concept for a framework to autonomously discover MRI contrast

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

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Glang,  FM       
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/persons84372

Loktyushin,  A       
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;

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

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Citation

Mueller, S., Glang, F., Herz, K., Loktyushin, A., Scheffler, K., & Zaiss, M. (2022). MR-double-zero: proof-of-concept for a framework to autonomously discover MRI contrast. In 24. Jahrestagung der Deutschen Sektion der ISMRM (DS-ISMRM 2022) (pp. 12-13).


Cite as: https://hdl.handle.net/21.11116/0000-000B-08CA-9
Abstract
Introduction
The discovery of new MRI contrasts often happened hitherto by trial-and-error. Here we consider whether this can be
formulated as an optimization problem, still making use of a real MR scanner. Whereas traditionally an analytical description
of the contrast mechanism (a model) is required, thus having to make limiting assumptions, the presented approach requires
neither a model nor human interaction with the scanner; thus, we call this approach MR-double-zero following the previously
published model-based approach termed MR-zero [1].
Methods
Samples of different creatine concentrations (cCr=0…120mM) are created, T1 and T2 relaxation times are adjusted to in vivo
like values [2] and glucose is added as a confounding CEST pool. The MR scanner is controlled by an optimizer using Pulseq
files [3] sent via network to the host PC (Fig. 1). Data flow back to the optimizer ([4] implemented in [5]) on a local PC for
reconstruction. For each iteration the parameterized sequence gets updated by the optimizer and the data (MRI; up to 3
images) are mapped to the target (T=cCr) by linear regression as T=[MRI, MRI², …] β. Higher order powers of the pixel
intensities (e.g. MRI²) are included to enable more flexible mapping. The sequence consists of a 2D readout with an RF
preparation pulse train parameterized by B1,i, Δωi (off-resonance) , np,i (number of pulses) for i=1…3 images. Pulse duration tp,i
and delays td,i are fixed. It is pretended that relaxation effects are known but CEST is not.
Results
The proposed framework learned to map creatine concentration using off-resonant RF preparation. A direct mapping based
on T1 and T2 is not possible but the optimizer makes use of the “unknown” CEST mechanism. Within as little as 300 iterations
(duration ~3h), decent mapping independent of confounding glucose concentration is achieved by designing both acquisition
(B1,i, Δωi, np,i,) and mapping to the target (β coefficients) as shown in Fig. 2.
Discussion
In contrast to a previously published approach [6], both acquisition and evaluation are jointly optimized on a real MR scanner
without any predetermined model or human interaction besides providing the target and suitable samples. The proposed
method is intended as a paradigm shift towards autonomous, model-free and target-driven sequence design. Besides
sequence design, the framework may be used to calibrate system imperfections or for testing hypotheses as to whether and
how arbitrary targets could be accessed with MRI applied as a tool.