Linear projection-based CEST parameter estimation
Glang F, Zaiss M, Laun FB, Fabian MS, German A, Khakzar K, Mennecke A, Liebert A, Herz K, Liebig P, Kasper BS, Schmidt M, Zuazua Iriondo E, Nagel AM, Dörfler A, Scheffler K (2022)
Publication Language: English
Publication Status: Published
Publication Type: Journal article
Future Publication Type: Journal article
Publication year: 2022
Journal
Publisher: NMR in Biomedicine
URI: https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/epdf/10.1002/nbm.4697
DOI: 10.1002/nbm.4697
Open Access Link: https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/epdf/10.1002/nbm.4697
Abstract
Isolated evaluation of multi-parametric in vivo CEST MRI often requires complex
computational processing for both correction of B0 and B1 inhomogeneity and contrast
generation. For that, sufficiently densely sampled Z-spectra need to be acquired. The list of
acquired frequency offsets largely determines the total CEST acquisition time, while
potentially representing redundant information. In this work, a linear projection-based multi parametric CEST evaluation method is introduced that offers fast B0 and B1 inhomogeneity
correction, contrast generation and feature selection for CEST data, enabling reduction of the
overall measurement time. To that end, CEST data acquired at 7T in 6 healthy subjects and in
one brain tumor patient were conventionally evaluated by interpolation-based inhomogeneity
correction and Lorentzian curve fitting. Linear regression was used to obtain coefficient vectors
that directly map uncorrected data to corrected Lorentzian target parameters. L1 regularization
was applied to find subsets of the originally acquired CEST measurements that still allow for
such a linear projection mapping. The linear projection method allows fast and interpretable
mapping from acquired raw data to contrast parameters of interest, generalizing from healthy
subject training data to unseen healthy test data and to the tumor patient dataset. The L1
regularization method shows that a fraction of the acquired CEST measurements is sufficient
to preserve tissue contrasts, offering up to 2.8-fold reduction of scan time. Similar observations
as for the 7T data can be made for data from a clinical 3T scanner. Being a fast and interpretable
computation step, the proposed method is complementary to neural networks, which have been
recently employed for similar purposes. The scan time acceleration offered by the L1
regularization (‘CEST-LASSO’) constitutes a step towards better applicability of multi parametric CEST protocols in clinical context.
Authors with CRIS profile
Moritz Zaiß
Frederik Bernd Laun Professur für quantitative MR-Bildgebung (Heisenberg-Professur) Angelika Mennecke Einrichtungen, die zum Universitätsklinikum Erlangen gehören Andrzej Liebert Institute of Radiology Burkhard Kasper Lehrstuhl für Neurologie Manuel Schmidt Department of Neuroradiology Enrique Zuazua Iriondo Lehrstuhl für Dynamics, Control, Machine Learning and Numerics (Alexander von Humboldt-Professur) Armin Michael Nagel Professur für metabolische und funktionelle MR-Bildgebung Arnd Dörfler
Frederik Bernd Laun Professur für quantitative MR-Bildgebung (Heisenberg-Professur) Angelika Mennecke Einrichtungen, die zum Universitätsklinikum Erlangen gehören Andrzej Liebert Institute of Radiology Burkhard Kasper Lehrstuhl für Neurologie Manuel Schmidt Department of Neuroradiology Enrique Zuazua Iriondo Lehrstuhl für Dynamics, Control, Machine Learning and Numerics (Alexander von Humboldt-Professur) Armin Michael Nagel Professur für metabolische und funktionelle MR-Bildgebung Arnd Dörfler
Involved external institutions
Germany (DE)How to cite
APA:
Glang, F., Zaiss, M., Laun, F.B., Fabian, M.S., German, A., Khakzar, K.,... Scheffler, K. (2022). Linear projection-based CEST parameter estimation. NMR in Biomedicine. https://doi.org/10.1002/nbm.4697
MLA:
Glang, Felix, et al. "Linear projection-based CEST parameter estimation." NMR in Biomedicine (2022).
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