MR imaging in the low-field: Leveraging the power of machine learning

TL;DR

This paper explores how machine learning techniques can improve low-field MRI by enhancing image quality and overcoming inherent limitations, thereby expanding its clinical utility and accessibility.

Contribution

It provides an overview of ML applications in low-field MRI, including reconstruction, denoising, and super-resolution, highlighting potential for broader healthcare use.

Findings

ML improves image reconstruction quality

Deep learning enhances denoising capabilities

Super-resolution techniques increase spatial resolution

Abstract

Recent innovations in Magnetic Resonance Imaging (MRI) hardware and software have reignited interest in low-field ($<1\,\mathrm{T}$) and ultra-low-field MRI ($<0.1\,\mathrm{T}$). These technologies offer advantages such as lower power consumption, reduced specific absorption rate, reduced field-inhomogeneities, and cost-effectiveness, presenting a promising alternative for resource-limited and point-of-care settings. However, low-field MRI faces inherent challenges like reduced signal-to-noise ratio and therefore, potentially lower spatial resolution or longer scan times. This chapter examines the challenges and opportunities of low-field and ultra-low-field MRI, with a focus on the role of machine learning (ML) in overcoming these limitations. We provide an overview of deep neural networks and their application in enhancing low-field and ultra-low-field MRI performance. Specific ML-based solutions, including advanced image reconstruction, denoising, and super-resolution algorithms, are discussed. The chapter concludes by exploring how integrating ML with low-field MRI could expand its clinical applications and improve accessibility, potentially revolutionizing its use in diverse healthcare settings.