Deep learning-based noise reduction in low dose SPECT Myocardial Perfusion Imaging: Quantitative assessment and clinical performance

Comparison of different deep learning architectures for synthetic CT generation from MR images

“…The general adversarial networks (GAN)s were suggested by Goodfellow et al [40]. This network consists of two adversarial generative and discriminative models that are trained simultaneously [22,29]. The generative model learns to generate new data, while the discriminator model determines the probability of whether the input data is real or synthesized by the generator.…”

“…Recently, deep learning techniques have exhibited high performance in image segmentation, denoising, reconstruction, and, image synthesis in specific [26][27][28][29][30][31]. Convolutional neural networks (CNN)s, as a multi-layer model of interconnected neurons, can learn the complex non-linear mapping from MR to CT images to synthesize patient-specific pseudo-CTs.…”