Recent innovations in Magnetic Resonance Imaging (MRI) hardware and software have reignited interest in low-field (<1T) and ultra-low-field MRI (<0.1T). 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.
@article{arxiv.2501.17211,
title = {MR imaging in the low-field: Leveraging the power of machine learning},
author = {Andreas Kofler and Dongyue Si and David Schote and Rene M Botnar and Christoph Kolbitsch and Claudia Prieto},
journal= {arXiv preprint arXiv:2501.17211},
year = {2025}
}
Comments
To appear as a book chapter in T. K\"ustner et al, "Machine Learning in MRI: From Methods to Clinical Translation"