Fast Unsupervised MRI Reconstruction Without Fully-Sampled Ground Truth Data Using Generative Adversarial Networks

Abstract

Most deep learning (DL) magnetic resonance imaging (MRI) reconstruction approaches rely on supervised training algorithms, which require access to high-quality, fully-sampled ground truth datasets. In MRI, acquiring fully-sampled data is time-consuming, expensive, and, in some cases, impossible due to limitations on data acquisition speed. We present a DL framework for MRI reconstruction that does not require any fully-sampled data using unsupervised generative adversarial networks. We test our proposed method on 2D knee MRI data and 2D+time abdominal dynamic contrast enhanced (DCE) MRI data. In the DCE-MRI dataset, as is the case with many dynamic MRI sequences, ground truth was not possible to acquire and therefore, supervised DL reconstruction was not feasible. We show that our unsupervised method produces reconstructions which are better than compressed sensing in terms of image metrics and the recovery of anatomical structure, with faster inference time. In contrast to most deep learning reconstruction techniques, which are supervised, this method does not need any fully-sampled data. With the proposed method, accelerated imaging and accurate reconstruction can be performed in applications in cases where fully-sampled datasets are difficult to obtain or unavailable.