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

Magnetic Resonance Imaging (MRI) is an essential tool for the early detection, risk stratification, prognosis, treatment selection, and monitoring of many diseases, including cancer, cardiovascular disease, metabolic, musculoskeletal, and brain disorders, among many others. Its ability to produce multi-contrast and multi-parametric images of soft tissues, coupled with its non-invasive and radiation-free nature, makes it a highly valuable tool in clinical practice. Over the past five decades, the technology behind MRI has undergone significant advancements, especially in terms of the magnetic field strengths used for imaging. Early MRI systems operated at low field strengths (0.15 T to 0.35 T) [1-3], and while they offered important diagnostic insights, they were limited by low signal-to-noise ratio (SNR) and image resolution. Over time, several advancements led to the development of systems operating at higher field strengths, such as 1.5 T and 3 T, which are now considered the clinical standard due to their superior SNR and image quality [4, 5]. Recent developments have even pushed field strengths to ultra-high levels ( 3 T), including 5 T, 7 T and beyond, further enhancing the spatial and temporal resolution of MRI [4, 6, 7]. However, high-field MRI has its challenges [8].