Accelerated and quantitative three-dimensional molecular MRI using a generative adversarial network
Weigand-Whittier J, Sedykh M, Herz K, Coll-Font J, Foster AN, Gerstner ER, Nguyen C, Zaiß M, Farrar CT, Perlman O (2022)
Publication Type: Journal article
Publication year: 2022
Journal
DOI: 10.1002/mrm.29574
Abstract
PurposeTo 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. MethodsThree-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. ResultsThe 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%$$ 3.8\pm 1.3\% $$ and 4.6 +/- 1.3%$$ 4.6\pm 1.3\% $$, respectively, and SSIM of 96.3 +/- 1.6%$$ 96.3\pm 1.6\% $$ and 95.0 +/- 2.4%$$ 95.0\pm 2.4\% $$, 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. ConclusionGAN-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.
Authors with CRIS profile
Maria Sedykh
Neuroradiologisches Institut
Moritz Zaiß
Professur für Multimodale Bildgebung in der klinischen Forschung
Involved external institutions
Massachusetts General Hospital
United States (USA) (US)
Harvard University
United States (USA) (US)
Max-Planck-Institut für biologische Kybernetik / Max Planck Institute for Biological Cybernetics
Germany (DE)
United States (USA) (US)
Harvard University
United States (USA) (US)
Max-Planck-Institut für biologische Kybernetik / Max Planck Institute for Biological Cybernetics
Germany (DE)
How to cite
APA:
Weigand-Whittier, J., Sedykh, M., Herz, K., Coll-Font, J., Foster, A.N., Gerstner, E.R.,... Perlman, O. (2022). Accelerated and quantitative three-dimensional molecular MRI using a generative adversarial network. Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.29574
MLA:
Weigand-Whittier, Jonah, et al. "Accelerated and quantitative three-dimensional molecular MRI using a generative adversarial network." Magnetic Resonance in Medicine (2022).
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