DATA AUGMENTATION FOR DEEP LEARNING BASED AC-CELERATED MRI RECONSTRUCTION

Abstract

Deep neural networks have emerged as very successful tools for image restoration and reconstruction tasks. These networks are often trained end-to-end to directly reconstruct an image from a noisy or corrupted measurement of that image. To achieve state-of-the-art performance, training on large and diverse sets of images is considered critical. However, it is often difficult and/or expensive to collect large amounts of training images. Inspired by the success of Data Augmentation (DA) for classification problems, in this paper, we propose a pipeline for data augmentation for image reconstruction tasks arising in medical imaging and explore its effectiveness at reducing the required training data in a variety of settings. We focus on accelerated magnetic resonance imaging, where the goal is to reconstruct an image from a few under-sampled linear measurements. Our DA pipeline is specifically designed to utilize the invariances present in medical imaging measurements as naive DA strategies that neglect the physics of the problem fail. We demonstrate the effectiveness of our data augmentation pipeline by showing that for some problem regimes, DA can achieve comparable performance to the state of the art on the fastMRI dataset while using significantly fewer training data. Specifically, for 8-fold acceleration we achieve performance comparable to the state of the art with only 10% of the training data for multi-coil reconstruction and with only 33% of the training data for single-coil reconstruction. Our findings show that in the low-data regime DA is beneficial, whereas in the high-data regime it has diminishing returns.

1. INTRODUCTION

In magnetic resonance imaging (MRI), an extremely popular medical imaging technique, it is common to reduce the acquisition time by subsampling the measurements, because this reduces cost and increases accessibility of MRI to patients. However, since due to the subsampling, there are fewer equations than unknowns, the signal is not uniquely identifiable from the measurements. To overcome this challenge there has been a flurry of activity over the last decade aimed at utilizing prior knowledge about the signal, in a research area referred to as compressed sensing (Candes et al., 2006; Donoho, 2006) . Classical compressed sensing methods reduce the required number of measurements by utilizing prior knowledge about the images during the reconstruction process, often via a convex regularization that enforces sparsity in an appropriate transformation of the image. More recently, deep learning techniques have been used to enforce much more nuanced forms of prior knowledge (see Ongie et al. (2020) and references therein for an overview). The most successful of these approaches aim to directly learn the inverse mapping from the measurements to the image by training on a large set of training data consisting of signal/measurement pairs. This approach often enables faster reconstruction of images, but more importantly, deep learning techniques yield significantly higher quality reconstructions. This in turn enables fewer measurements further reducing image acquisition times. For instance, in an accelerated MRI competition known as FastMRI Challenge (Zbontar et al., 2018) , all the top contenders used deep learning reconstruction techniques. Contrary to classical compressive sensing approaches, however, deep learning techniques typically rely on large sets of training data often consisting of images along with the corresponding measurement. This is also true about the use of deep learning techniques in other areas such as computer vision and Natural Language Processing (NLP) were superb empirical success has been observed.

