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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
