Acceleration of Quantitative Semisolid MT/CEST Imaging using a Generative 
Adversarial Network (GAN-CEST)

Weigand, JPW; Sedykh, M; Herz, K; Coll-Font, J; Nguyen, S; Zaiss, M; Farrar, CT; Perlman, O

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Meeting Abstract

Acceleration of Quantitative Semisolid MT/CEST Imaging using a Generative Adversarial Network (GAN-CEST)

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Herz, 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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https://submissions.mirasmart.com/ISMRM2022/Itinerary/ConferenceMatrixEventDetail.aspx?id=205
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Citation

Weigand, J., Sedykh, M., Herz, K., Coll-Font, J., Nguyen, S., Zaiss, M., et al. (2022). Acceleration of Quantitative Semisolid MT/CEST Imaging using a Generative Adversarial Network (GAN-CEST). In Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM 2022).

Cite as: https://hdl.handle.net/21.11116/0000-000A-5C97-5

Abstract

Quantitative metabolite concentration and pH biomarker maps, as provided by semisolid MT/CEST-MR-Fingerprinting (MRF), constitute a useful means for determining the molecular origin of pathology. However, the lengthy dictionary generation time and the prolonged 3D acquisition time may hinder clinical dissemination. Here, we developed a generative adversarial network (GAN), aimed to drastically shorten the 3D semisolid MT/CEST-MRF acquisition time and circumvent the need for dictionary generation. In-vitro and in-vivo experiments in 4 volunteers and a patient were conducted at 3 different sites using 3 different scanner models, showing substantial reduction in scan time, while retaining a good agreement with ground-truth reference