论文标题
使用生成对抗网络（GAN-CEST）加速和定量3D半固体MT/CEST成像
Accelerated and Quantitative 3D Semisolid MT/CEST Imaging using a Generative Adversarial Network (GAN-CEST)
论文作者
论文摘要
目的：实质上缩短了定量3D化学交换饱和转移（CEST）和半固体磁化转移（MT）成像所需的采集时间，并允许快速化学交换参数图重建。方法：L-精氨酸幻象，全脑，全脑和小腿肌肉的三维CEST和MT磁共振指纹（MRF）数据集，使用3个不同的位点，使用3个不同的位点，使用3个不同的Scanner模型和COIL获得3T临床扫描仪，从健康志愿者，癌症患者和心脏病患者中获取来自健康志愿者，癌症患者和心脏病患者的小腿肌肉。然后，设计和训练了生成的对抗网络监督框架（GAN-CEST），以学习从减少的输入数据空间到定量交换参数空间的映射，同时保留感知和定量内容。结果：Gan-Cest 3D采集时间为42-52秒，比CEST-MRF短70％。整个大脑的定量重建需要0.8秒。地面真相和基于GAN的L-精氨酸浓度和pH值之间观察到了一个极好的一致性（Pearson的R> 0.97，NRMSE <1.5％）。来自脑肿瘤受试者的Gan-cest图像产生的半固体量分数和汇率NRMSE为3.8 $ \ pm $ 1.3％和4.6 $ \ pm $ 1.3％，分别为96.3 $ \ pm $ \ pm $ 1.6％和95.0 $ \ pm pm $ 2.4％。半固体交换参数的NRMSE <7％和SSIM> 94％的小腿肌肉交换参数的映射。与MRF相比，在具有较大敏感性伪像的区域中，Gan-Cest表现出改善的性能和噪声降低。结论：Gan-Cest可以大大减少定量半固体MT/CEST映射的获取时间，同时即使在训练过程中无法使用的病理和扫描仪模型时，也可以保持性能。
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% shorter than CEST-MRF. The quantitative 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%). GAN-CEST images from a brain-tumor subject yielded a semi-solid volume fraction and exchange rate NRMSE of 3.8$\pm$1.3% and 4.6$\pm$1.3%, respectively, and SSIM of 96.3$\pm$1.6% and 95.0$\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-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.
