A Cross Domain Generative Network for Accelerated MRI

Compressed sensing (CS) is a method for accelerating MRI acquisition by acquiring less data through undersampling of Fourier space (k-space). Existing deep learning-based CSMRI methods are commonly concerned with optimizing datadriven network models with input undersampled data points and an efficient learning framework. Generative modelling is a learning framework employed in different applications for learning an abstract distribution of observed data and thereby generating new data points similar to the true features. In this regard, the current work proposes a Generative Adversarial Network (GAN) based Cross Domain Extrapolation Generative Adversarial Network (CdE-GAN) that incorporates an extrapolation mechanism through decoder-type architectural design to represent the fine details with a large set of pixels. The results obtained from the experiments show that the extrapolation network enables robust and accurate estimation of missing frequencies, alleviating the structural artifacts at higher acceleration/downsampling factors compared to state-of-the-art methods.