Deep learning in magnetic resonance image reconstruction

Abstract

Summary Magnetic resonance (MR) imaging visualises soft tissue contrast in exquisite detail without harmful ionising radiation. In this work, we provide a state‐of‐the‐art review on the use of deep learning in MR image reconstruction from different image acquisition types involving compressed sensing techniques, parallel image acquisition and multi‐contrast imaging. Publications with deep learning‐based image reconstruction for MR imaging were identified from the literature (PubMed and Google Scholar), and a comprehensive description of each of the works was provided. A detailed comparison that highlights the differences, the data used and the performance of each of these works were also made. A discussion of the potential use cases for each of these methods is provided. The sparse image reconstruction methods were found to be most popular in using deep learning for improved performance, accelerating acquisitions by around 4–8 times. Multi‐contrast image reconstruction methods rely on at least one pre‐acquired image, but can achieve 16‐fold, and even up to 32‐ to 50‐fold acceleration depending on the set‐up. Parallel imaging provides frameworks to be integrated in many of these methods for additional speed‐up potential. The successful use of compressed sensing techniques and multi‐contrast imaging with deep learning and parallel acquisition methods could yield significant MR acquisition speed‐ups within clinical routines in the near future.

On Real-time Image Reconstruction with Neural Networks for MRI-guided Radiotherapy

“…[51,52] To perform reconstructions above the relatively low resolutions used for motion tracking on an MRI-Linac will require lighter-weight reconstruction networks. [18,53] One light-weight implementation of AUTOMAP is decomposed-AUTOMAP (dAU-TOMAP) which replaces dense layers with orthogonal 'domain transform' layers. [54] While dAUTOMAP performs strongly for Cartesian trajectories, it assumes that data is acquired in orthogonal directions, making it unsuitable for reconstruction of nonuniform data.…”

“…Recently, neural networks have enabled fast, accurate reconstruction of undersampled MRI data. [17][18][19][20] However, despite these prospects, the successful deployment of neural networks for real-time imaging applications on systems including MRI-Linacs (see Fig. 1a) still hinges on the availability of training data and utilization of a reconstruction framework suitable for more challenging, non-uniformly sampled image reconstruction.…”

An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset

“…In recent years, we have witnessed the tremendous success of deep learning in solving a variety of inverse problems, but the interpretation and generalization of these deeplearning-based methods still remain the main concerns. As an improvement over generic black-box-type deep neural networks (DNNs), several classes of learnable optimization algorithms (LOAs) inspired neural networks, known as unrolling networks, which unfold iterative algorithms to multi-phase networks and have demonstrated promising solution accuracy and efficiency empirically [43][44][45][46][47][48][49][50]. However, many of them are only specious imitations of the iterative algorithms and hence lack the backbone of the variational model and any convergence guarantee.…”

Deep Learning Algorithm-Based Ultrasound Image Information in Diagnosis and Treatment of Pernicious Placenta Previa

“…Dictionary learning is mainly based on the dictionary and coefficient solution of matrix decomposition. Deep dictionary learning combines the advantages of deep learning and dictionary learning, constructs a deep structure through multilevel dictionary learning, and constitutes a deep dictionary learning model to learn sample data to obtain a dictionary [ 15 ]. The current application of deep learning techniques in medical ultrasound image analysis mainly involves three tasks: classification, detection, and segmentation of various anatomical structures such as the breast, prostate, liver, heart, and fetus.…”