Current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy

Table 1 Summary of contemporary deep learning methods in image acquisition

Ref.	Architecture	Purpose
Fu et al[5], 2020	Cycle consistent generative adversarial network	To enable pseudo CT-aided CT-MRI image registration
Liu et al[6], 2019	Cycle generative adversarial network	To derive electron density from routine anatomical MRI for MRI-based SBRT treatment planning
Liu et al[7], 2019	3D Cycle-consistent generative adversarial network	To generate pelvic synthetic CT for prostate proton therapy treatment planning
Lei et al[8], 2020	Cycle generative adversarial network for synthesis and fully convolution neural network for delineation	To help segment and delineate of prostate target by pseudo MR synthesis from CT
Dong et al[9], 2016	Super resolution convolution neural network	To develop novel CNN for high- and low-resolution images mapping
Bahrami et al[10], 2016	Convolution neural network	To reconstruct 7T-like super-resolution MRI from 3T MR images
Qu et al[11], 2020	Wavelet-based affine transformation layers network	To synthesize superior quality of 7T MRI from its 3T MR images than existing 7T MR images
Yang et al[12], 2018	Generative adversarial network with Wasserstein distance and perceptual loss function	To denoise low-dose CT image and improve contrast for lesion detection
Chen et al[14], 2017	Deep convolution neural network	To train the mapping between low- and normal-dose images so to efficiently reduce noise in low-dose CT
Wang et al[13], 2019	Cycle-consistent adversarial network with residual blocks and attention gates	To improve the contrast-to noise ratio for low-dose CT simulation in brain stereotactic radiosurgery radiation therapy

CNN: Convolutional neural network; CT: Computed tomography; MRI: Magnetic resonance imaging.