It is critical to acknowledge that, while low-field applications have found important roles in terms of quality, speed, and performance, higher-field-strength scanners will remain the diagnostic standard for the foreseeable future.
Abstract
Magnetic resonance imaging (MRI) at low magnetic field strengths (under 1 T) has seen renewed interest, driven by technological advances that enhance accessibility, affordability, and versatility. Improvements in design, gradient performance, and artificial intelligence (AI)-assisted reconstruction enable diagnostic-quality neuroimaging at well under 1 T, disrupting the once-peerless dominance of high-field imaging. Modern low-field systems offer advantages in patient comfort, siting flexibility, and/or portability. Reduced infrastructure and safety constraints enable scanning in diverse settings, including outpatient, emergency, and intensive care environments. Lower susceptibility improves imaging near air-bone interfaces, metallic implants, and the skull base, while markedly lowering specific absorption rate (SAR) and acoustic noise. Clinical neurological applications extend across the brain, spine, and skull base and include pediatric or fetal imaging, where improved tolerance and reduced motion artifacts are particularly useful. Lower signal-to-noise ratio (SNR) and limited spatial resolution remain challenges. AI-enhanced reconstruction, optimized pulse sequences, and higher relaxivity of contrast agents help mitigate these drawbacks. Portable low-field MRI also enables point-of-care neuroimaging and intraoperative use, offering real-time insights to expedite diagnosis. However, it is critical to acknowledge that, while low-field applications have found important roles in terms of quality, speed, and performance, higher-field-strength scanners will remain the diagnostic standard for the foreseeable future.
The recent developments in ultra-low-field brain MRI are reviewed, which enable imaging in open environments and demonstrate initial clinical applicability in point-of-care settings, and future developments are envisioned to address the current limitations of image quality and contrast in ultra-low-field brain MRI systems.
Ed X. Wu, Yujiao Zhao, Yilong Liu et al.· Stroke· 1 citation
The current clinical evidence base is reviewed, including the role of LF-MRI in supporting stroke-type classification and tissue confirmation, in wake-up and unknown-onset stroke for tissue-based triage, and in post-therapeutic settings to enable serial assessment after thrombolysis or thrombectomy.
A. Sorby-Adams, N. Pintér, Keith W. Muir et al.· Stroke· 0 citations
This work presents an open-source, optimized solenoid head coil tailored for the 50 mT open-source scanner (OSII ONE v2.1), set the basis for a fully reliable and reproducible component for the open-source OSII ONE MRI scanner.
Umberto Zanovello, Julia Pfitzer, Ariane Ernst et al.· 1 citation
Ultra‐low‐field (ULF) MRI offers a promising path to accessible neuroimaging, with potential to address global healthcare disparities and advance population‐level brain health research. However, the inherently low signal‐to‐noise ratio (SNR), reduced spatial resolution, and altered tissue contrasts relative to conventional high‐field (HF) scans are significant barriers to ULF analysis and interpretation. While deep learning (DL) approaches have been proposed to enhance ULF image quality, many rely on synthetic training data due to the lack of available subject‐matched ULF and HF scans, introducing potential “domain shift” errors when applied to real acquisitions. Here, we present a DL framework trained on real ULF and HF‐MRIs to address these limitations and improve ULF‐derived brain volume analysis.
Peter Hsu, Elisa Marchetto, Patricia M. Johnson et al.· Magnetic Resonance in Medici...· 0 citations
Ultra-high field (UHF) MRI (≥ 7T) is increasingly used to study the cerebro-cerebellar networks, offering substantial gains in signal-to-noise ratio and blood oxygen level dependent (BOLD) sensitivity that enable submillimeter functional and structural imaging. These advantages are particularly relevant for the cerebellum, whose tightly folded cortex and small deep nuclei have historically been difficult to resolve in vivo. In this targeted review, we synthesize recent progress in UHF-MRI applied to cerebro-cerebellar network mapping and critically examine the methodological challenges that accompany these developments. We highlight how UHF-MRI has enabled more precise delineation of cerebellar functional territories, improved visualization of the dentate nucleus and its connectivity, and facilitated integration of cerebellar nodes into whole-brain network models. We also discuss the technical challenges, including RF-field inhomogeneity, susceptibility-induced distortions, and the trade-off between spatial resolution and signal strength. These issues are compounded in the cerebellum due to its anatomical location and fine-scale organization, increasing the risk of spatial biases and misinterpretation if not carefully addressed. Beyond acquisition, we identify key gaps in analysis methodologies, including the need for cerebellum-specific preprocessing pipelines, segmentation tools that preserve mesoscale anatomy, and frameworks that treat the cerebellum and cerebrum as a unified system. We also discuss emerging opportunities, such as neurostimulation, integration with postmortem imaging and deep learning-based methods that may help bridge microstructural and in vivo findings. Overall, we argue that while UHF-MRI holds transformative potential for cerebellar systems neuroscience, translational progress will depend on rigorous methodological standardization and awareness of its limitations.
Amina Zohor Zidane Burgess, J. Boonstra, Yoshifumi Mori et al.· Cerebellum· 0 citations
The emerging concept of "Green Radiology" aims to mitigate the environmental impact of medical imaging while maintaining high standards of patient care. Among various modalities, MRI is particularly resource-intensive. This review focuses on the clinical feasibility and significance of minimizing gadolinium-based contrast agent (GBCA) administration in neuroimaging, specifically for the longitudinal follow-up of brain tumors. Key points of this review are as follows: (1) Rationales for "Green" MRI: Beyond patient safety concerns such as adverse reactions and gadolinium retention in the brain and bone, GBCAs pose significant environmental risks due to their persistence in global water systems and potential eco-toxicity. (2) Feasibility in extra-axial tumors: For many extra-axial lesions, including meningiomas, schwannomas, and macro-PitNETs (≥ 10 mm), non-contrast MRI sequences-leveraging the inherent contrast of cerebrospinal fluid-provide sufficient diagnostic information for routine surveillance. (3) Strategies for intra-axial tumors: While GBCAs remain the gold standard for high-grade gliomas and metastases, a "non-contrast-first" strategy may be viable for tumors with indolent progression. In these cases, GBCAs can be reserved for instances where interval changes are first identified on unenhanced sequences. (4) Advanced contrast-free alternatives: Non-invasive techniques such as arterial spin labeling (ASL), chemical exchange saturation transfer (CEST) imaging, and advanced diffusion-weighted imaging (DWI) offer powerful metabolic and microstructural insights without the need for chemical agents. (5) Future perspectives: Deep learning-based technologies, including "virtual contrast" synthesis and dosage reduction algorithms, hold immense potential to harmonize diagnostic excellence with environmental sustainability. Transitioning toward "Green Neuroradiology" through the judicious use of GBCAs and the adoption of advanced non-contrast sequences is a practical and necessary step for sustainable radiological practice. This review highlights the concept of "Green MRI" for sustainable neuroimaging. We discuss the clinical and environmental rationales for minimizing the use of GBCAs, particularly in the follow-up of extra-axial tumors. Furthermore, we explore how advanced sequences and deep learning provide viable, contrast-free alternatives for future radiological practice.
Kazuhiro Tsuchiya, M. Gomyo, Shichiroh Katase et al.· Japanese Journal of Radiolog...· 0 citations