Table 2. Application of Deep Learning to Reduce Image Noise or Artifacts.

Study	Year	CT Scans for Test (n)	Low Dose Data	Network	Summary
Wolterink et al. [23]	2017	28	Phantom	GAN	Noise reduction in low-dose CCTA; 3D GAN
Green et al. [24]	2018	45	Synthetic data	FCN	Noise reduction in low-dose CCTA; FCN for per voxel prediction
Lossau et al. [25]	2019	4	Synthetic data	CNN	Motion artifact recognition and quantification; CNN
Tatsugami et al. [26]	2019	30	Synthetic data	CNN	Deep learning–based imaging restoration; lower image noise and better CNR compared with hybrid IR images
Kang et al. [20]	2019	50	Patients data with different cardiac phase	GAN	Noise reduction in low-dose CCTA (multiple cardiac phase data); 2D cycle-consistent GAN (CycleGAN)
Benz et al. [52]	2020	43	Synthetic data	NA	Comparison between model-based IR and deep-learning image reconstruction in CCTA
Hong et al. [49]	2020	82	Synthetic data	FCN	Applying a deep learning–based denoising technique to CCTA along with IR for additional noise reduction

CCTA = coronary computed tomography angiography, CNN = convolutional neural network, CNR = contrast-to-noise ratio, D = dimensional, FCN = fully convolutional neural network, GAN = generative adversarial network, IR = iterative reconstruction, NA = not available