DeepCEST 7 T: Fast and homogeneous mapping of 7 T CEST MRI parameters and their uncertainty quantification

Hunger L, Rajput JR, Klein K, Mennecke A, Fabian M, Schmidt M, Glang F, Herz K, Liebig P, Nagel AM, Scheffler K, Dörfler A, Maier A, Zaiß M (2022)

Publication Type: Journal article

Publication year: 2022

Journal

DOI: 10.1002/mrm.29520

Abstract

Purpose In this work, we investigated the ability of neural networks to rapidly and robustly predict Lorentzian parameters of multi-pool CEST MRI spectra at 7 T with corresponding uncertainty maps to make them quickly and easily available for routine clinical use. Methods We developed a deepCEST 7 T approach that generates CEST contrasts from just 1 scan with robustness against B-1 inhomogeneities. The input data for a neural feed-forward network consisted of 7 T in vivo uncorrected Z-spectra of a single B-1 level, and a B-1 map. The 7 T raw data were acquired using a 3D snapshot gradient echo multiple interleaved mode saturation CEST sequence. These inputs were mapped voxel-wise to target data consisting of Lorentzian amplitudes generated conventionally by 5-pool Lorentzian fitting of normalized, denoised, B-0- and B-1-corrected Z-spectra. The deepCEST network was trained with Gaussian negative log-likelihood loss, providing an uncertainty quantification in addition to the Lorentzian amplitudes. Results The deepCEST 7 T network provides fast and accurate prediction of all Lorentzian parameters also when only a single B-1 level is used. The prediction was highly accurate with respect to the Lorentzian fit amplitudes, and both healthy tissues and hyperintensities in tumor areas are predicted with a low uncertainty. In corrupted cases, high uncertainty indicated wrong predictions reliably. Conclusion The proposed deepCEST 7 T approach reduces scan time by 50% to now 6:42 min, but still delivers both B-0- and B-1-corrected homogeneous CEST contrasts along with an uncertainty map, which can increase diagnostic confidence. Multiple accurate 7 T CEST contrasts are delivered within seconds.

Authors with CRIS profile

Involved external institutions

Max-Planck-Institut für biologische Kybernetik / Max Planck Institute for Biological Cybernetics Germany (DE) University of Copenhagen Denmark (DK) Siemens AG, Healthcare Sector Germany (DE)

How to cite

APA:

Hunger, L., Rajput, J.R., Klein, K., Mennecke, A., Fabian, M., Schmidt, M.,... Zaiß, M. (2022). DeepCEST 7 T: Fast and homogeneous mapping of 7 T CEST MRI parameters and their uncertainty quantification. Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.29520

MLA:

Hunger, Leonie, et al. "DeepCEST 7 T: Fast and homogeneous mapping of 7 T CEST MRI parameters and their uncertainty quantification." Magnetic Resonance in Medicine (2022).

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