Compressed Sensing in Sodium Magnetic Resonance Imaging: Techniques, Applications, and Future Prospects

Abstract

Sodium ( 23 Na) yields the second strongest nuclear magnetic resonance (NMR) signal in biological tissues and plays a vital role in cell physiology. Sodium magnetic resonance imaging (MRI) can provide insights into cell integrity and tissue viability relative to pathologies without significant anatomical alternations, and thus it is considered to be a potential surrogate biomarker that provides complementary information for standard hydrogen ( 1 H) MRI in a noninvasive and quantitative manner. However, sodium MRI suffers from a relatively low signal-to-noise ratio and long acquisition times due to its relatively low NMR sensitivity. Compressed sensing-based (CS-based) methods have been shown to accelerate sodium imaging and/or improve sodium image quality significantly. In this manuscript, the basic concepts of CS and how CS might be applied to improve sodium MRI are described, and the historical milestones of CS-based sodium MRI are briefly presented. Representative advanced techniques and evaluation methods are discussed in detail, followed by an expose of clinical applications in multiple anatomical regions and diseases as well as thoughts and suggestions on potential future research prospects of CS in sodium MRI.

Improved Magnetic Resonance Image Reconstruction using Compressed Sensing and Adaptive Multi Extreme Particle Swarm Optimization Algorithm

“…The RIP having an order of K for A is expressed through the restricted isometry constant 𝛿 𝑘 , where 0 < 𝛿 𝑘 < 1. The RIP condition for order K asserts that the matrix A behaves almost like an isometry on all K-sparse vectors, meaning it approximately preserves their norm and pairwise distances enabling the recovery of the Website: www.ijeer.forexjournal.co.in Improved Magnetic Resonance Image Reconstruction using Compressed Sensing and Adaptive Multi Extreme Particle Swarm Optimization Algorithm original sparse signal from a reduced set of measurements [21]. Mathematically, for any K-sparse vector x, the RIP is characterized by the lower and upper bound conditions as shown in Equation 5.…”

ICRICS: Iterative Compensation Recovery for Image Compressive Sensing

“…Q.P. Chen et al surveyed the technologies, applications and future prospects of compressive sensing for magnetic resonance imaging [9]. A. Bustin et al reviewed the means of magnetic resonance imaging, including low-rank reconstruction, sparse dictionary learning and deep learning [10].…”

2D sodium MRI of the human calf using half‐sinc excitation pulses and compressed sensing

“…This can be compensated by reducing the number of signal averages (NSAs), at a cost to SNR. Recently, compressed sensing (CS) has been shown to improve SNR in sodium imaging, [16][17][18] but only a small number of studies have applied it in the leg. 19,20 The focus of this study was to develop a high-resolution 2D UTE- 23 Na MRI sequence with CS reconstruction for rapid and accurate TSC quantification in skeletal muscle and skin.…”

“…This can be compensated by reducing the number of signal averages (NSAs), at a cost to SNR. Recently, compressed sensing (CS) has been shown to improve SNR in sodium imaging, 16 , 17 , 18 but only a small number of studies have applied it in the leg. 19 , 20 …”