Accelerated and Quantitative 3D Semisolid MT/CEST Imaging using a Generative Adversarial Network (GAN-CEST)

by   Jonah Weigand-Whittier, et al.

Purpose: To substantially shorten the acquisition time required for quantitative 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 3 different sites, using 3 different scanner models and coils. A generative adversarial network supervised framework (GAN-CEST) 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-CEST 3D acquisition time was 42-52 seconds, 70 reconstruction of the entire brain took 0.8 seconds. An excellent agreement was observed between the ground truth and GAN-based L-arginine concentration and pH values (Pearson's r > 0.97, NRMSE < 1.5 subject yielded a semi-solid volume fraction and exchange rate NRMSE of 3.8±1.3 95.0±2.4 in a cardiac patient, yielded NRMSE < 7 exchange parameters. In regions with large susceptibility artifacts, GAN-CEST has demonstrated improved performance and reduced noise compared to MRF. Conclusion: GAN-CEST can substantially reduce the acquisition time for quantitative semisolid MT/CEST mapping, while retaining performance even when facing pathologies and scanner models that were not available during training.


page 22

page 23

page 25

page 26

page 27

page 28


Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction

Magnetic resonance imaging (MRI) is one of the best medical imaging moda...

Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI Reconstruction

Deep learning based generative adversarial networks (GAN) can effectivel...

UPHDR-GAN: Generative Adversarial Network for High Dynamic Range Imaging with Unpaired Data

The paper proposes a method to effectively fuse multi-exposure inputs an...

Coupling Rendering and Generative Adversarial Networks for Artificial SAS Image Generation

Acquisition of Synthetic Aperture Sonar (SAS) datasets is bottlenecked b...

High-fidelity Direct Contrast Synthesis from Magnetic Resonance Fingerprinting

Magnetic Resonance Fingerprinting (MRF) is an efficient quantitative MRI...

Attention-Guided Generative Adversarial Network to Address Atypical Anatomy in Modality Transfer

Recently, interest in MR-only treatment planning using synthetic CTs (sy...

Please sign up or login with your details

Forgot password? Click here to reset