Compressed sensing MRI: a review of the clinical literature

Abstract

MRI is one of the most dynamic and safe imaging techniques available in the clinic today. However, MRI acquisitions tend to be slow, limiting patient throughput and limiting potential indications for use while driving up costs. Compressed sensing (CS) is a method for accelerating MRI acquisition by acquiring less data through undersampling of k-space. This has the potential to mitigate the time-intensiveness of MRI. The limited body of research evaluating the effects of CS on MR images has been mostly positive with regards to its potential as a clinical tool. Studies have successfully accelerated MRI with this technology, with varying degrees of success. However, more must be performed before its diagnostic efficacy and benefits are clear. Studies involving a greater number radiologists and images must be completed, rating CS based on its diagnostic efficacy. Also, standardized methods for determining optimal imaging parameters must be developed.

DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

“…Comprehensive reviews on classic CS-MRI methods and clinical applications can be found elsewhere, e.g., [1], [31].…”

“…Although there are promising studies applying fast CS-MRI in clinical environments [31], [32], [33], most routine clinical MRI scanning is still based on standard fully-sampled Cartesian sequences or is accelerated only using parallel imaging. The main challenges are: (1) satisfying the incoherence criteria required by CS-MRI [1]; (2) the widely applied sparsifying transforms might be too simple to capture complex image details associated with subtle differences of biological tissues, e.g., TV based sparsifying transform penalises local variation in the reconstructed images but can introduce staircase artefacts and the wavelet transform enforces point singularities and isotropic features but orthogonal wavelets may lead to blocky artefacts [34], [35], [36]; (3) nonlinear optimisation solvers usually involve iterative computation that may result in relatively long reconstruction time [1]; (4) inappropriate hyperparameters predicted in current CS-MRI methods can cause over-regularisation that will yield overly smooth and unnatural looking reconstructions or images with residual undersampling artefacts [1].…”

Sparse Reconstruction Techniques in Magnetic Resonance Imaging

“…While L1 SPIRiT and k-t SPARSE-SENSE/GRASP have been used as examples of compressed sensing techniques in this section, there are many more compressed sensing type reconstruction algorithms that have been presented in the literature and reviewed elsewhere 92,93 .…”

“…The performance of the technique is then demonstrated in a small set of proof-of-concept reconstructions using in vivo data, typically collected from healthy volunteers and sometimes a small number of patients. However, the impact of the various sparse reconstruction techniques in clinical practice with regard to diagnostic accuracy has been less extensively studied 93 . The following section describes some applications where sparse reconstructions hold clinical promise.…”

Compressed sensing for body MRI

“…Incoherence is a key component that aims to break the usual regularity in sampling patterns and enables the use of sparsity-based reconstructions. The introduction of compressed sensing to MRI has initiated a large body of research across multiple clinical applications, ranging from cardiovascular imaging to body imaging to neuroimaging and spectroscopic imaging (17). Moreover, appropriate combinations of compressed sensing and parallel imaging have been shown to enable further increases in imaging speed beyond what is possible with either method alone (8,18–22).…”