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

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).

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 Urheber:
Weigand, JPW, Autor
Sedykh, M, Autor
Herz, K1, Autor           
Coll-Font, J, Autor
Nguyen, S, Autor
Zaiss, M1, Autor           
Farrar, CT, Autor
Perlman, O, Autor
Affiliations:
1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              

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 Zusammenfassung: 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

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 Datum: 2022-05
 Publikationsstatus: Online veröffentlicht
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Titel: Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM 2022)
Veranstaltungsort: London, UK
Start-/Enddatum: 2022-05-07 - 2022-05-12

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Titel: Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM 2022)
Genre der Quelle: Konferenzband
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Seiten: - Band / Heft: - Artikelnummer: 2713 Start- / Endseite: - Identifikator: -