Accelerated and quantitative three‐dimensional molecular MRI using a generative adversarial network. Issue 5 (31st December 2022) - Weigand‐Whittier, Jonah; Sedykh, Maria; Herz, Kai; Coll‐Font, Jaume; Foster, Anna N.; Gerstner, Elizabeth R.; Nguyen, Christopher; Zaiss, Moritz; Farrar, Christian T.; Perlman, Or
Abstract : Purpose: To substantially shorten the acquisition time required for quantitative three‐dimensional (3D) chemical exchange saturation transfer (CEST) and semisolid magnetization transfer (MT) imaging and allow for rapid chemical exchange parameter map reconstruction. Methods: Three‐dimensional CEST and MT magnetic resonance fingerprinting (MRF) datasets of L‐arginine phantoms, whole‐brains, and calf muscles from healthy volunteers, cancer patients, and cardiac patients were acquired using 3T clinical scanners at three different sites, using three different scanner models and coils. A saturation transfer‐oriented generative adversarial network (GAN‐ST) supervised framework was then designed and trained to learn the mapping from a reduced input data space to the quantitative exchange parameter space, while preserving perceptual and quantitative content. Results: The GAN‐ST 3D acquisition time was 42–52 s, 70% shorter than CEST‐MRF. The quantitative reconstruction of the entire brain took 0.8 s. An excellent agreement was observed between the ground truth and GAN‐based L‐arginine concentration and pH values (Pearson's r > 0.95, ICC > 0.88, NRMSE < 3%). GAN‐ST images from a brain‐tumor subject yielded a semi‐solid volume fraction and exchange rate NRMSE of 3 . 8 ± 1 . 3 % 3.8± 1.3% and 4 . 6 ± 1 . 3 % 4.6± 1.3% , respectively, and SSIM of 96 . 3 ± 1 . 6 % 96.3± 1.6% and 95 . 0 ± 2 . 4 % 95.0± 2.4% , respectively. The mapping of the calf‐muscle exchange parameters in a cardiac patient, yielded NRMSE < 7% and SSIM > 94% for the semi‐solid exchange parameters. In regions with large susceptibility artifacts, GAN‐ST has demonstrated improved performance and reduced noise compared to MRF. Conclusion: GAN‐ST can substantially reduce the acquisition time for quantitative semi‐solid MT/CEST mapping, while retaining performance even when facing pathologies and scanner models that were not available during training.