低场磁共振

Modern MRI scanners have trended toward higher field strengths to maximize signal and resolution while minimizing scan time. However, high‐field devices remain expensive to install and operate, making them scarce outside of high‐income countries and major population centers. Low‐field strength scanners have drawn renewed academic, industry, and philanthropic interest due to advantages that could dramatically increase imaging access, including lower cost and portability. Nevertheless, low‐field MRI still faces inherent limitations in image quality that come with decreased signal. In this article, we review advantages and disadvantages of low‐field MRI scanners, describe hardware and software innovations that accentuate advantages and mitigate disadvantages, and consider clinical applications for a new generation of low‐field devices. In our review, we explore how these devices are being or could be used for high acuity brain imaging, outpatient neuroimaging, MRI‐guided procedures, pediatric imaging, and musculoskeletal imaging. Challenges for their successful clinical translation include selecting and validating appropriate use cases, integrating with standards of care in high resource settings, expanding options with actionable information in low resource settings, and facilitating health care providers and clinical practice in new ways. By embracing both the promise and challenges of low‐field MRI, clinicians and researchers have an opportunity to transform medical care for patients around the world.

For about 30 years, MRI set cruising speed at 1.5 T of magnetic field, with a gentle transition toward 3 T systems. In its first 10 years of existence, there was an open debate on the question of most relevant MRI field strengths considering the gain in T1 contrast, simpler cooling strategies, lower predisposition to generating image artifacts, and naturally cost reduction of small footprint low field systems. At the time, the inherent gain in sensitivity of high field, which would translate in more signal per unit time, quickly ended this debate. The promise of rapid exams or higher image resolution within a reasonable time won over other considerations and set the standards for MR value. Yet, many reasons bring low field MRI in a situation quite different from 40 years ago. From the achieved progress regarding all aspects of MRI technology, an MR scan at 1.5 T in the mid 1980s has very little in common with the equivalent scan in 2020. That clearly indicates that field strength alone is not what drives performance. It is also unlikely that the total number of machines worldwide will grow so to follow the increasing demand considering their overall cost (~$1M/T). The natural trend is to better control medical expenses worldwide, and reconsidering low-field MRI could lead to the democratization of dedicated, point-of-care devices to decongest high-field clinical scanners. In the present article, we aim to draw an extensive portrait of most recent MRI developments at low (1–199 mT) and ultra-low field (micro-Tesla range) outside of the commercial sphere, and we propose to discuss their potential relevance in future clinical applications. We will cover a broad spectrum from pre-polarized MRI using ultra-sensitive magnetic sensors up to permanent and resistive magnets in compact designs.

… optimized MRI detection coils (ie multi-turn coils using the maximum allowed conductor length) results in low-field MRI sensitivity approaching and even rivaling that of high-field MRI. …

This narrative review explores recent advancements and applications of modern low-field (≤ 1 Tesla) magnetic resonance imaging (MRI) in musculoskeletal radiology. Historically, high-field MRI systems (1.5 T and 3 T) have been the standard in clinical practice due to superior image resolution and signal-to-noise ratio. However, recent technological advancements in low-field MRI offer promising avenues for musculoskeletal imaging. General principles of low-field MRI systems are being introduced, highlighting their strengths and limitations compared to high-field counterparts. Emphasis is placed on advancements in hardware design, including novel magnet configurations, gradient systems, and radiofrequency coils, which have improved image quality and reduced susceptibility artifacts particularly in musculoskeletal imaging. Different clinical applications of modern low-field MRI in musculoskeletal radiology are being discussed. The diagnostic performance of low-field MRI in diagnosing various musculoskeletal pathologies, such as ligament and tendon injuries, osteoarthritis, and cartilage lesions, is being presented. Moreover, the discussion encompasses the cost-effectiveness and accessibility of low-field MRI systems, making them viable options for imaging centers with limited resources or specific patient populations. From a scientific standpoint, the amount of available data regarding musculoskeletal imaging at low-field strengths is limited and often several decades old. This review will give an insight to the existing literature and summarize our own experiences with a modern low-field MRI system over the last 3 years. In conclusion, the narrative review highlights the potential clinical utility, challenges, and future directions of modern low-field MRI, offering valuable insights for radiologists and healthcare professionals seeking to leverage these advancements in their practice.

In March 2022, the first ISMRM Workshop on Low‐Field MRI was held virtually. The goals of this workshop were to discuss recent low field MRI technology including hardware and software developments, novel methodology, new contrast mechanisms, as well as the clinical translation and dissemination of these systems. The virtual Workshop was attended by 368 registrants from 24 countries, and included 34 invited talks, 100 abstract presentations, 2 panel discussions, and 2 live scanner demonstrations. Here, we report on the scientific content of the Workshop and identify the key themes that emerged. The subject matter of the Workshop reflected the ongoing developments of low‐field MRI as an accessible imaging modality that may expand the usage of MRI through cost reduction, portability, and ease of installation. Many talks in this Workshop addressed the use of computational power, efficient acquisitions, and contemporary hardware to overcome the SNR limitations associated with low field strength. Participants discussed the selection of appropriate clinical applications that leverage the unique capabilities of low‐field MRI within traditional radiology practices, other point‐of‐care settings, and the broader community. The notion of “image quality” versus “information content” was also discussed, as images from low‐field portable systems that are purpose‐built for clinical decision‐making may not replicate the current standard of clinical imaging. Speakers also described technical challenges and infrastructure challenges related to portability and widespread dissemination, and speculated about future directions for the field to improve the technology and establish clinical value.

… Taken together, accessible and affordable low-field MRI has the potential for high impact at … as ultra-low-field MRI. The majority of clinical scanners correspond to HF-MRI, where the …

Stroke is a leading cause of death and disability worldwide. The reasons for increased stroke burden in developing countries are inadequately controlled risk factors resulting from poor public awareness and inadequate infrastructure. Computed tomography and MRI are common neuroimaging modalities used to assess stroke with diffusion‐weighted MRI, in particular, being the recommended choice for acute stroke imaging. However, access to these imaging modalities is primarily restricted to major cities and high‐income groups. In the case of stroke, the time‐window of treatment to limit the damage is of a few hours and needs a point‐of‐care diagnosis. A low‐cost MR system typically achieved at the ultra‐low‐ and very‐low‐field would meet the need for a geographically accessible and portable solution. We review studies focused on accessible stroke imaging and recent developments in MR methodologies, including hardware, to image at low fields. We hypothesize that in the absence of a formal, rapid stroke triaging system, the value of timely on‐site delivery of the scanner to the stroke patient can be significant. To this end, we discuss multiple recent hardware and methods developments in the low‐field regime. Our review suggests a compelling need to explore further the trade‐offs between high signal, contrast, and accessibility at low fields in low‐income communities.

Magnetic resonance imaging (MRI) allows important visualization of the brain and central nervous system anatomy and organization. However, unlike electroencephalography (EEG) or functional near infrared spectroscopy, which can be brought to a patient or study participant, MRI remains a hospital or center-based modality. Low magnetic field strength MRI systems, however, offer the potential to extend beyond these traditional hospital and imaging center boundaries. Here we describe the development of a modified cargo van that incorporates a removable low-field permanent magnet MRI system and demonstrate its proof-of-concept. Using phantom scans and in vivo T2-weighted neuroimaging data, we show no significant differences with respect to geometric distortion, signal-to-noise ratio, or tissue segmentation outcomes in data acquired in the mobile system compared to a similar static system in a laboratory setting. These encouraging results show, for the first time, MRI that can be performed at a participant’s home, community center, school, etc. Breaking traditional barriers of access, this mobile approach may enable imaging of patients and participants who have mobility challenges, live long distances from imaging centers, or are otherwise unable to travel to an imaging center or hospital.

… in low-field Magnetic Resonance Imaging (MRI). The development of low-cost MRI solutions … examples of fixed-field instruments operating at low-field. Then we discuss pros and cons of …

The paper describes the evolution of low-field MRI from the very early pioneering days in the late 70 s until today. It is not meant to give a comprehensive historical account of the development of MRI, but rather to highlight the different research environments then and now. In the early 90 s, when low-field systems below 1.5 T essentially vanished, there were just no reasonable means available to make up for the factor of roughly three in signal-to-noise-ratio (SNR) between 0.5 and 1.5 T. This has drastically changed. Improvements in hardware—closed Helium-free magnets, RF receiver systems and especially much faster gradients, much more flexible sampling schemes including parallel imaging and compressed sensing and especially the use of AI at all stages of the imaging process have made low-field MRI a clinically viable supplement to conventional MRI. Ultralow-field MRI with magnets around 0.05 T are also back and constitute a bold and courageous endeavor to bring MRI to communities, which have neither the means nor the infrastructure to sustain a current standard of care MRI.

Background Commercial low-field-strength MRI systems are generally not equipped with state-of-the-art MRI hardware, and are not suitable for demanding imaging techniques. An MRI system was developed that combines low field strength (0.55 T) with high-performance imaging technology. Purpose To evaluate applications of a high-performance low-field-strength MRI system, specifically MRI-guided cardiovascular catheterizations with metallic devices, diagnostic imaging in high-susceptibility regions, and efficient image acquisition strategies. Materials and Methods A commercial 1.5-T MRI system was modified to operate at 0.55 T while maintaining high-performance hardware, shielded gradients (45 mT/m; 200 T/m/sec), and advanced imaging methods. MRI was performed between January 2018 and April 2019. T1, T2, and T2* were measured at 0.55 T; relaxivity of exogenous contrast agents was measured; and clinical applications advantageous at low field were evaluated. Results There were 83 0.55-T MRI examinations performed in study participants (45 women; mean age, 34 years ± 13). On average, T1 was 32% shorter, T2 was 26% longer, and T2* was 40% longer at 0.55 T compared with 1.5 T. Nine metallic interventional devices were found to be intrinsically safe at 0.55 T (<1°C heating) and MRI-guided right heart catheterization was performed in seven study participants with commercial metallic guidewires. Compared with 1.5 T, reduced image distortion was shown in lungs, upper airway, cranial sinuses, and intestines because of improved field homogeneity. Oxygen inhalation generated lung signal enhancement of 19% ± 11 (standard deviation) at 0.55 T compared with 7.6% ± 6.3 at 1.5 T (P = .02; five participants) because of the increased T1 relaxivity of oxygen (4.7e-4 mmHg-1sec-1). Efficient spiral image acquisitions were amenable to low field strength and generated increased signal-to-noise ratio compared with Cartesian acquisitions (P < .02). Representative imaging of the brain, spine, abdomen, and heart generated good image quality with this system. Conclusion This initial study suggests that high-performance low-field-strength MRI offers advantages for MRI-guided catheterizations with metal devices, MRI in high-susceptibility regions, and efficient imaging. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Grist in this issue.

Cardiac MR imaging is well established for assessment of cardiovascular structure and function, myocardial scar, quantitative flow, parametric mapping, and myocardial perfusion. Despite the clear evidence supporting the use of cardiac MRI for a wide range of indications, it is underutilized clinically. Recent developments in low‐field MRI technology, including modern data acquisition and image reconstruction methods, are enabling high‐quality low‐field imaging that may improve the cost–benefit ratio for cardiac MRI. Studies to‐date confirm that low‐field MRI offers high measurement concordance and consistent interpretation with clinical imaging for several routine sequences. Moreover, low‐field MRI may enable specific new clinical opportunities for cardiac imaging such as imaging near metal implants, MRI‐guided interventions, combined cardiopulmonary assessment, and imaging of patients with severe obesity. In this review, we discuss the recent progress in low‐field cardiac MRI with a focus on technical developments and early clinical validation studies.

Low magnetic field magnetic resonance imaging (MRI) ( B0$$ {B}_0 $$ < 1 T) is regaining interest in the magnetic resonance (MR) community as a complementary, more flexible, and cost‐effective approach to MRI diagnosis. Yet, the impaired signal‐to‐noise ratio (SNR) per square root of time, or SNR efficiency, leading in turn to prolonged acquisition times, still challenges its relevance at the clinical level. To address this, researchers investigate various hardware and software solutions to improve SNR efficiency at low field, including the leveraging of latest advances in computing hardware. However, there may not be a single recipe for improving SNR at low field, and it is key to embrace the challenges and limitations of each proposed solution. In other words, suitable solutions depend on the final objective or application envisioned for a low‐field scanner and, more importantly, on the characteristics of a specific low B0$$ {B}_0 $$ field. In this review, we aim to provide an overview on software solutions to improve SNR efficiency at low field. First, we cover techniques for efficient k‐space sampling and reconstruction. Then, we present post‐acquisition techniques that enhance MR images such as denoising and super‐resolution. In addition, we summarize recently introduced electromagnetic interference cancellation approaches showing great promises when operating in shielding‐free environments. Finally, we discuss the advantages and limitations of these approaches that could provide directions for future applications.

Advances in MRI technology have led to the development of low-field-strength (hereafter, "low-field") (0.55 T) MRI systems with lower weight, fewer shielding requirements, and lower cost than those of traditional (1.5-3 T) systems. The trade-offs of lower signal-to-noise ratio (SNR) at 0.55 T are partially offset by patient safety and potential comfort advantages (eg, lower specific absorption rate and a more cost-effective larger bore diameter) and physical advantages (eg, decreased T2* decay, shorter T1 relaxation times). Image reconstruction advances leveraging developing technologies (such as deep learning-based denoising) can be paired with traditional techniques (such as increasing the number of signal averages) to improve SNR. The overall image quality produced by low-field MRI systems, although perhaps somewhat inferior to 1.5-3 T MRI systems in terms of SNR, is nevertheless diagnostic for a broad variety of body imaging applications. Effective low-field body MRI requires (a) an understanding of the trade-offs resulting from lower field strengths, (b) an approach to modifying routine sequences to overcome SNR challenges, and (c) a workflow for carefully selecting appropriate patients. The authors describe the rationale, opportunities, and challenges of low-field body MRI; discuss important considerations for low-field imaging with common body MRI sequences; and delineate a variety of use cases for low-field body MRI. The authors also include lessons learned from their preliminary experience with a new low-field MRI system at a tertiary care center. Finally, they explore the future of low-field MRI, summarizing current limitations and potential future developments that may enhance the clinical adoption of this technology. ©RSNA, 2023 Supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center. See the invited commentary by Venkatesh in this issue.

Recent advances in magnetic resonance imaging (MRI) hardware and software have renewed interest in low-field MRI, challenging the long-held notion that such systems are inherently inferior to high-field counterparts. Traditionally dismissed due to lower signal-to-noise ratios and reduced image quality, low-field MRI was primarily relegated to cost-sensitive or resource-limited settings. However, modern low-field systems now integrate advanced reconstruction algorithms, refined imaging techniques, and improved hardware design, significantly narrowing the performance gap. In some scenarios, these systems offer distinct advantages, such as reduced susceptibility artifacts and improved safety of metallic implants. Their portability, lower operational costs, and reduced infrastructure demands make them especially valuable in point-of-care, remote, or intraoperative environments. This review examines the physical principles of low-field MRI, traces its technological evolution, and evaluates its current and emerging clinical applications. By highlighting both its strengths and limitations, we aim to clarify the growing role of low-field MRI in contemporary diagnostic imaging and underscore its potential in expanding global access to high-quality radiological care. Low-field and portable MRI systems offer a cost-effective, accessible, and safer imaging alternative that may expand diagnostic capabilities in underserved, point-of-care, and intraoperative settings, thereby improving global access to essential radiologic services. Advanced image reconstruction improves low-field MRI image quality and diagnostic utility. Reduced susceptibility artifacts enhance imaging near metallic hardware and air–tissue interfaces. Low-field systems enable cost-effective, portable imaging in constrained clinical environments. Advanced image reconstruction improves low-field MRI image quality and diagnostic utility. Reduced susceptibility artifacts enhance imaging near metallic hardware and air–tissue interfaces. Low-field systems enable cost-effective, portable imaging in constrained clinical environments.

… configuration low field MRI devices in the early 1990s; this enabled direct patient access and utilization of the MRI … and intraoperative MRI with special emphasis in low field surrounding. …

Low-field (LF) MRI research currently gains momentum from its potential to offer reduced costs and reduced footprints translating into wider accessibility. However, the impeded signal-to-noise ratio inherent to lower magnetic fields can have a significant impact on acquisition times that challenges LF clinical relevance. Undersampling is an effective way to speed up acquisitions in MRI, and recent work has shown encouraging results when combined with deep learning (DL). Yet, training DL models generally requires large databases that are not yet available at LF regimes. Here, we demonstrate the capability of Residual U-net combined with data augmentation to reconstruct magnitude and phase information of undersampled LF MRI scans at 0.1 T with a limited training dataset (n = 10). The model performance was first evaluated in a retrospective study for different acceleration rates and sampling patterns. Ultimately, the DL approach was validated on prospectively acquired, fivefold undersampled LF data. With varying performances associated to the adopted sampling scheme, our results show that the approach investigated can preserve the global structure and the details sharpness in the reconstructed magnitude and phase images. Overall, promising results could be obtained on acquired LF MR images that may bring this research closer to clinical implementation.

Background: Ultra-low-field magnetic resonance imaging (ULF-MRI) has emerged as an alternative with several portable clinical applications. This review aims to comprehensively explore its applications, potential limitations, technological advancements, and expert recommendations. Methods: A review of the literature was conducted across medical databases to identify relevant studies. Articles on clinical usage of ULF-MRI were included, and data regarding applications, limitations, and advancements were extracted. A total of 25 articles were included for qualitative analysis. Results: The review reveals ULF-MRI efficacy in intensive care settings and intraoperatively. Technological strides are evident through innovative reconstruction techniques and integration with machine learning approaches. Additional advantages include features such as portability, cost-effectiveness, reduced power requirements, and improved patient comfort. However, alongside these strengths, certain limitations of ULF-MRI were identified, including low signal-to-noise ratio, limited resolution and length of scanning sequences, as well as variety and absence of regulatory-approved contrast-enhanced imaging. Recommendations from experts emphasize optimizing imaging quality, including addressing signal-to-noise ratio (SNR) and resolution, decreasing the length of scan time, and expanding point-of-care magnetic resonance imaging availability. Conclusion: This review summarizes the potential of ULF-MRI. The technology’s adaptability in intensive care unit settings and its diverse clinical and surgical applications, while accounting for SNR and resolution limitations, highlight its significance, especially in resource-limited settings. Technological advancements, alongside expert recommendations, pave the way for refining and expanding ULF-MRI’s utility. However, adequate training is crucial for widespread utilization.

Magnetic resonance imaging is a key diagnostic tool in modern healthcare, yet it can be cost-prohibitive given the high installation, maintenance and operation costs of the machinery. There are approximately seven scanners per million inhabitants and over 90% are concentrated in high-income countries. We describe an ultra-low-field brain MRI scanner that operates using a standard AC power outlet and is low cost to build. Using a permanent 0.055 Tesla Samarium-cobalt magnet and deep learning for cancellation of electromagnetic interference, it requires neither magnetic nor radiofrequency shielding cages. The scanner is compact, mobile, and acoustically quiet during scanning. We implement four standard clinical neuroimaging protocols (T1- and T2-weighted, fluid-attenuated inversion recovery like, and diffusion-weighted imaging) on this system, and demonstrate preliminary feasibility in diagnosing brain tumor and stroke. Such technology has the potential to meet clinical needs at point of care or in low and middle income countries. A low cost MRI scanner may have the potential to meet clinical needs at point of care or in low and middle income countries. Here the authors describe a low cost 0.055 Tesla MRI scanner that operates using a standard AC power outlet, and demonstrate its preliminary feasibility in diagnosing brain tumor and stroke.

Ultra-low-field magnetic resonance imaging (ULF-MRI), operating below 0.2 Tesla, is gaining renewed interest as a re-emerging diagnostic modality in a field dominated by high- and ultra-high-field systems. Recent advances in magnet design, RF coils, pulse sequences, and AI-based reconstruction have significantly enhanced image quality, mitigating traditional limitations such as low signal- and contrast-to-noise ratio and reduced spatial resolution. ULF-MRI offers distinct advantages: reduced susceptibility artifacts, safer imaging in patients with metallic implants, low power consumption, and true portability for point-of-care use. This narrative review synthesizes the physical foundations, technological advances, and emerging clinical applications of ULF-MRI. A focused literature search across PubMed, Scopus, IEEE Xplore, and Google Scholar was conducted up to August 11, 2025, using combined keywords targeting hardware, software, and clinical domains. Inclusion emphasized scientific rigor and thematic relevance. A comparative analysis with other imaging modalities highlights the specific niche ULF-MRI occupies within the broader diagnostic landscape. Future directions and challenges for clinical translation are explored. In a world increasingly polarized between the push for ultra-high-field excellence and the need for accessible imaging, ULF-MRI embodies a modern “David versus Goliath” theme, offering a sustainable, democratizing force capable of expanding MRI access to anyone, anywhere.

Ultra low‐field (ULF) MRI is an accessible neuroimaging modality that can bridge healthcare disparities and advance population‐level brain health research. However, the inherently low signal‐to‐noise ratio of ULF‐MRI often necessitates reductions in spatial resolution and, combined with the field‐dependency of MRI contrast, challenges the accurate extraction of clinically relevant brain morphology. We evaluate the current state of ULF‐MRI brain volumetry utilizing techniques for enhancing spatial resolution and leveraging recent advancements in brain segmentation. This is based on the agreement between ULF and corresponding high‐field (HF) MRI brain volumes, and test–retest repeatability for multiple ULF scans. In this study, we find that accurate brain volumes can be measured from ULF‐MRIs when combining orthogonal imaging directions for T2‐weighted images to form a higher resolution image volume. We also demonstrate that not all orthogonal imaging directions contribute equally to volumetric accuracy and provide a recommended scan protocol given the constraints of the current technology.

Recent development of ultra‐low‐field (ULF) MRI presents opportunities for low‐power, shielding‐free, and portable clinical applications at a fraction of the cost. However, its performance remains limited by poor image quality. Here, a computational approach is formulated to advance ULF MR brain imaging through deep learning of large‐scale publicly available 3T brain data.

Breast cancer screening is essential for reducing mortality, yet current modalities face significant barriers, including high costs, limited accessibly, and reliance on ionizing radiation, which leads many women to forego regular screenings. Magnetic resonance imaging (MRI) offers a radiation-free alternative, but its adoption for screening is constrained by cost, availability, and the need for IV contrast administration. In this study, we demonstrate the feasibility of ultra-low field (ULF) unilateral breast MRI for screening applications. ULF MRI was performed on 11 healthy women in a prone position. These participants were healthy women without a history of breast cancer. Three breast radiologists could reliably delineate breast outlines and distinguish fibroglandular tissue (FGT) from adipose tissue. Tissue patterns (fatty, scattered, heterogeneous, and extreme FGT) were consistently identified. In two additional patients with prior breast cancer, ULF MRI effectively eliminated magnetic susceptibility artifacts from surgical biopsy clips and in one of these patients revealed post-surgical changes following lumpectomy. Additionally, in another patient, a > 3 cm cyst, previously confirmed on standard clinical ultrasound, was feasible to visualize with ULF MRI. These findings establish the technical feasibility of ULF breast MRI. While preliminary, they motivate further technical development and evaluation to clarify its capabilities and limitations.

… such measures correspond to high-field MRI. Here we scanned 23 … Finally, we showcase the potential of ultra-low-field MRI for … and analysis approaches for ultra-low-field neuroimaging. …

The lower infrastructure requirements of portable ultra‐low field MRI (ULF‐MRI) systems have enabled their use in diverse settings such as intensive care units and remote medical facilities. The UNITY Project is an international neuroimaging network harnessing this technology, deploying portable ULF‐MRI systems globally to expand access to MRI for studies into brain development. Given the wide range of environments where ULF‐MRI systems may operate, there are external factors that might influence image quality. This work aims to introduce the quality control (QC) framework used by the UNITY Project to investigate how robust the systems are and how QC metrics compare between sites and over time. We present a QC framework using a commercially available phantom, scanned with 64 mT portable MRI systems at 17 sites across 12 countries on four continents. Using automated, open‐source analysis tools, we quantify signal‐to‐noise, image contrast, and geometric distortions. Our results demonstrated that the image quality is robust to the varying operational environment, for example, electromagnetic noise interference and temperature. The Larmor frequency was significantly correlated to room temperature, as was image noise and contrast. Image distortions were less than 2.5 mm, with high robustness over time. Similar to studies at higher field, we found that changes in pulse sequence parameters from software updates had an impact on QC metrics. This study demonstrates that portable ULF‐MRI systems can be deployed in a variety of environments for multi‐center neuroimaging studies and produce robust results.

High-field (HF) MRI is a standard diagnostic tool for brain cancer, but its high cost and technical demands limit accessibility in low- and middle-income countries. Recent advancements in ultra-low field (ULF) MRI technology, including the development of portable scanners, offer a promising solution to these challenges. This study evaluates the diagnostic capabilities of ULF-MRI in detecting brain cancer and compares radiological evaluation using ULF- with HF-MRI. Consecutive patients with suspected or confirmed brain tumors undergoing routine 3T HF-MRI at the University Hospital Bonn were recruited for this study and underwent ULF-MRI. Eligible patients were at least 18 years old and had MRI-abnormalities in the HF-MRI. The 0.064 Tesla Swoop® portable MR Imaging System was utilized. HF-MRI and ULF-MRI scans were independently evaluated by two experienced neuroradiologists and results were compared. Thirteen patients were recruited, of whom 11 (85%) were diagnosed with brain tumors. In 11/11 (100%) patients with brain tumors, ULF-MRI identified tumor lesions corresponding to the findings of HF-MRI. In 7/11 (63.6%) identification of all tumor lesions could be achieved. Three of four further relevant imaging findings in HF-MRI (e.g. acute hydrocephalus or concomitant ischemia) were also found in in ULF-MRI. This single-center study demonstrates that ULF-MRI is a practical tool in neuro-oncology, which may particularly be helpful in resource-limited settings. Further research is required to define the role of ULF-MRI alongside existing imaging modalities for brain cancer diagnosis and management.

… The chapter presents an overview of the opportunities, challenges and pulse sequence considerations of imaging at low and ultra-low field strengths (defined as less than 1 T in this …

… In the initial assessment of the usefulness of incorporating a mobile ultra low-field iMRI scanner into our surgical routine, we did not establish a list of criteria that had to be met to select …

… Ultra-low-field MRI uses microtesla fields for signal encoding … -MRI instrumentation based on a commercial whole-head MEG device is described. The combination of ultra-low-field MRI …

High‐field magnetic resonance imaging (MRI) is a standard in the diagnosis of epilepsy. However, high costs and technical barriers have limited adoption in low‐ and middle‐income countries. Even in high‐income nations, many individuals with epilepsy face delays in undergoing MRI. Recent advancements in ultra‐low‐field (ULF) MRI technology, particularly the development of portable scanners, offer a promising solution to the limited accessibility of MRI. In this study, we present and evaluate the imaging capability of ULF MRI in detecting structural abnormalities typically associated with epilepsy and compare it to high‐field MRI at 3 T.

Ultra Low-field Magnetic Resonance Imaging (MRI) scanners can potentially make a substantial impact in the field of medical imaging and radiology due to their cost-effectiveness, potential for portability and utility in an environment where the resource is in shortage. However, low- field MRI encounters challenges such as a low signal-to-noise ratio which results in lower-quality images. In this study, we introduce a novel image translation technique that relies on an adversarial diffusion-based deep learning approach to generate high-field MRI images from ultra low-field MR images. We have integrated a non-diffusive attention-guided module to enhance areas recognized as critical high-level features using self-attention maps from the diffusion process. To evaluate our approach, we use paired datasets consisting of different MRI sequences from both 64mT ultra low-field and 3T high-field scanners. We compare the performance of our method against state-of-the-art GAN and diffusion-based models, demonstrating its superior performance both quantitatively and qualitatively.

Introduction: Neuroimaging is a prerequisite for treatment of stroke patients, but it is not available all over the globe. Portable ultra-low field (pULF) MRI has the potential to improve access to neuroimaging and thus stroke care worldwide. In a pilot study, we were the first to utilise pULF-MRI in a European tertiary stroke centre and to evaluate its diagnostic value compared to high-field (HF) MRI. Patients and methods: Consecutive patients admitted for suspected ischaemic stroke underwent pULF-MRI using the 0.064 Tesla Swoop® portable MR imaging system in addition to standard imaging. HF-MRI and pULF-MRI scans were blindly assessed to compare the diagnostic accuracy and imaging-based therapeutic decisions based on pULF-MRI to HF-MRI. Results: Seventeen patients underwent pULF-MRI, 12 of whom had ischaemic lesions on HF-MRI. Ischaemic lesions were detected on pULF-MRI in 8/12 cases. The four infarcts not identified on pULF-MRI were all smaller than 6 mm in diameter. In all cases, a virtual treatment decision based on pULF-MRI by a blinded team matched the actual clinical decisions. Conclusion: This single-centre study demonstrates that pULF-MRI is a promising tool in acute stroke care, providing reliable imaging for treatment decision and follow-up monitoring. pULF-MRI may support acute stroke care if HF-MRI is unavailable and may be particularly helpful in resource-limited settings. Limitations of pULF-MRI include long acquisition times and the lack of vessel imaging and haemorrhage-sensitive sequences. Graphical abstract

The detection of transient peri‐ictal magnetic resonance imaging (MRI) abnormalities has been variable after epileptic seizures. The most common reason for this variability is that abnormalities may disappear if the interval between seizure and scan acquisition is prolonged using conventional high‐field systems. Here, we deployed a portable ultra‐low‐field MRI system in the presurgical evaluation at the bedside of individuals with epilepsy. We hypothesized that this novel technology enables rapid postictal scans and reliably shows focal peri‐ictal MRI abnormalities in the seizure onset zone. A .064‐T Swoop Portable MR Imaging System was used. Postictally, an axial diffusion‐weighted sequence was acquired. The interictal MRI consisted of the diffusion‐weighted and three‐dimensional T1‐weighted sequences. Postictal–interictal difference maps of diffusion‐weighted volumes were calculated. Three individuals were included. Two individuals with focal aware seizures scanned 29 s and 19 min after the seizure, respectively, showed focal restrictions in diffusivity in the seizure onset zone, and a third individual scanned 5 h 45 min after a focal to bilateral tonic–clonic seizure showed global restrictions of diffusivity. Portable ultra‐low‐field MRI opens a new line of inquiry with the aim to establish postictal seizure imaging as part of the presurgical evaluation of people with epilepsy.

We aim to explore the feasibility of head and neck time‐of‐flight (TOF) magnetic resonance angiography (MRA) at ultra‐low‐field (ULF). TOF MRA was conducted on a highly simplified 0.05 T MRI scanner with no radiofrequency (RF) and magnetic shielding. A flow‐compensated three‐dimensional (3D) gradient echo (GRE) sequence with a tilt‐optimized nonsaturated excitation RF pulse, and a flow‐compensated multislice two‐dimensional (2D) GRE sequence, were implemented for cerebral artery and vein imaging, respectively. For carotid artery and jugular vein imaging, flow‐compensated 2D GRE sequences were utilized with venous and arterial blood presaturation, respectively. MRA was performed on young healthy subjects. Vessel‐to‐background contrast was experimentally observed with strong blood inflow effect and background tissue suppression. The large primary cerebral arteries and veins, carotid arteries, jugular veins, and artery bifurcations could be identified in both raw GRE images and maximum intensity projections. The primary brain and neck arteries were found to be reproducible among multiple examination sessions. These preliminary experimental results demonstrated the possibility of artery TOF MRA on low‐cost 0.05 T scanners for the first time, despite the extremely low MR signal. We expect to improve the quality of ULF TOF MRA in the near future through sequence development and optimization, ongoing advances in ULF hardware and image formation, and the use of vascular T1 contrast agents.

Radiological examination of the brain is a critical determinant of stroke care pathways. Accessible neuroimaging is essential to detect the presence of intracerebral hemorrhage (ICH). Conventional magnetic resonance imaging (MRI) operates at high magnetic field strength (1.5–3 T), which requires an access-controlled environment, rendering MRI often inaccessible. We demonstrate the use of a low-field MRI (0.064 T) for ICH evaluation. Patients were imaged using conventional neuroimaging (non-contrast computerized tomography (CT) or 1.5/3 T MRI) and portable MRI (pMRI) at Yale New Haven Hospital from July 2018 to November 2020. Two board-certified neuroradiologists evaluated a total of 144 pMRI examinations (56 ICH, 48 acute ischemic stroke, 40 healthy controls) and one ICH imaging core lab researcher reviewed the cases of disagreement. Raters correctly detected ICH in 45 of 56 cases (80.4% sensitivity, 95%CI: [0.68–0.90]). Blood-negative cases were correctly identified in 85 of 88 cases (96.6% specificity, 95%CI: [0.90–0.99]). Manually segmented hematoma volumes and ABC/2 estimated volumes on pMRI correlate with conventional imaging volumes (ICC = 0.955, p = 1.69e-30 and ICC = 0.875, p = 1.66e-8, respectively). Hematoma volumes measured on pMRI correlate with NIH stroke scale (NIHSS) and clinical outcome (mRS) at discharge for manual and ABC/2 volumes. Low-field pMRI may be useful in bringing advanced MRI technology to resource-limited settings. Conventional magnetic resonance imaging (MRI) operates at a high magnetic field strength and requires a strict access-controlled environment, making MRI often inaccessible. Here, the authors present a portable low-field MRI device that detects intracerebral hemorrhage with high accuracy.

Portable, low-field magnetic resonance imaging (LF-MRI) of the brain may facilitate point-of-care assessment of patients with Alzheimer’s disease (AD) in settings where conventional MRI cannot. However, image quality is limited by a lower signal-to-noise ratio. Here, we optimize LF-MRI acquisition and develop a freely available machine learning pipeline to quantify brain morphometry and white matter hyperintensities (WMH). We validate the pipeline and apply it to outpatients presenting with mild cognitive impairment or dementia due to AD. We find hippocampal volumes from ≤ 3 mm isotropic LF-MRI scans have agreement with conventional MRI and are more accurate than anisotropic counterparts. We also show WMH volume has agreement between manual segmentation and the automated pipeline. The increased availability and reduced cost of LF-MRI, in combination with our machine learning pipeline, has the potential to increase access to neuroimaging for dementia. Portable, low-field MRI of the brain may facilitate assessment of patients with Alzheimer’s disease. Here, the authors present and validate an end-to-end pipeline to quantify brain morphometry using LF-MRI in patients with dementia.

Importance Neuroimaging is a key step in the clinical evaluation of brain injury. Conventional magnetic resonance imaging (MRI) systems operate at high-strength magnetic fields (1.5-3 T) that require strict, access-controlled environments. Limited access to timely neuroimaging remains a key structural barrier to effectively monitor the occurrence and progression of neurological injury in intensive care settings. Recent advances in low-field MRI technology have allowed for the acquisition of clinically meaningful imaging outside of radiology suites and in the presence of ferromagnetic materials at the bedside. Objective To perform an assessment of brain injury in critically ill patients in intensive care unit settings, using a portable, low-field MRI device at the bedside. Design, Setting, and Participants This was a prospective, single-center cohort study of 50 patients admitted to the neuroscience or coronavirus disease 2019 (COVID-19) intensive care units at Yale New Haven Hospital in New Haven, Connecticut, from October 30, 2019, to May 20, 2020. Patients were eligible if they presented with neurological injury or alteration, no contraindications for conventional MRI, and a body habitus not exceeding the scanner's 30-cm vertical opening. Diagnosis of COVID-19 was determined by positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction nasopharyngeal swab result. Exposures Portable MRI in an intensive care unit room. Main Outcomes and Measures Demographic, clinical, radiological, and treatment data were collected and analyzed. Brain imaging findings are described. Results Point-of-care MRI examinations were performed on 50 patients (16 women [32%]; mean [SD] age, 59 [12] years [range, 20-89 years]). Patients presented with ischemic stroke (n = 9), hemorrhagic stroke (n = 12), subarachnoid hemorrhage (n = 2), traumatic brain injury (n = 3), brain tumor (n = 4), and COVID-19 with altered mental status (n = 20). Examinations were acquired at a median of 5 (range, 0-37) days after intensive care unit admission. Diagnostic-grade T1-weighted, T2-weighted, T2 fluid-attenuated inversion recovery, and diffusion-weighted imaging sequences were obtained for 37, 48, 45, and 32 patients, respectively. Neuroimaging findings were detected in 29 of 30 patients who did not have COVID-19 (97%), and 8 of 20 patients with COVID-19 (40%) demonstrated abnormalities. There were no adverse events or complications during deployment of the portable MRI or scanning in an intensive care unit room. Conclusions and Relevance This single-center series of patients with critical illness in an intensive care setting demonstrated the feasibility of low-field, portable MRI. These findings demonstrate the potential role of portable MRI to obtain neuroimaging in complex clinical care settings.

Background: In recent years, there has been an increasing effort to take advantage of the potential use of low magnetic induction devices with less than 1 T, referred to as Low-Field MRI (LF MRI). LF MRI systems were used, especially in the early days of magnetic resonance technology. Over time, magnetic induction values of 1.5 and 3 T have become the standard for clinical devices, mainly because LF MRI systems were suffering from significantly lower quality of the images, e.g., signal–noise ratio. In recent years, due to advances in image processing with artificial intelligence, there has been an increasing effort to take advantage of the potential use of LF MRI with induction of less than 1 T. This overview article focuses on the analysis of the evidence concerning the diagnostic efficacy of modern LF MRI systems and the clinical comparison of LF MRI with 1.5 T systems in imaging the nervous system, musculoskeletal system, and organs of the chest, abdomen, and pelvis. Methodology: A systematic literature review of MEDLINE, PubMed, Scopus, Web of Science, and CENTRAL databases for the period 2018–2023 was performed according to the recommended PRISMA protocol. Data were analysed to identify studies comparing the accuracy, reliability and diagnostic performance of LF MRI technology compared to available 1.5 T MRI. RESULTS: A total of 1275 publications were retrieved from the selected databases. Only two articles meeting all predefined inclusion criteria were selected for detailed assessment. Conclusions: A limited number of robust studies on the accuracy and diagnostic performance of LF MRI compared with 1.5 T MRI was available. The current evidence is not sufficient to draw any definitive insights. More scientific research is needed to make informed conclusions regarding the effectiveness of LF MRI technology.

… onboard MRI to the onboard CT-based standard for image-guided RT is warranted to … onboard MR images acquired with a hybrid low-field MRI-RT system and OB-CT images acquired …

Background: Portable low-field-strength magnetic resonance imaging (MRI) systems represent a promising alternative to traditional high-field-strength systems with the potential to make MR technology available at scale in low-resource settings. However, lower image quality and resolution may limit the research and clinical potential of these devices. We tested two super-resolution methods to enhance image quality in a low-field MR system and compared their correspondence with images acquired from a high-field system in a sample of young people. Methods: = 70 individuals (mean age = 20.39 years, range 9-26 years). We tested two super-resolution approaches to improve image correspondence between images acquired at high- and low-field: (1) processing via a convolutional neural network ('SynthSR'), and (2) multi-orientation image averaging. We extracted brain region volumes, cortical thickness, and cortical surface area estimates. We used Pearson correlations to test the correspondence between these measures, and Steiger Z tests to compare the difference in correspondence between standard imaging and super-resolution approaches. Results: = 0.14). An alternative multi-orientation image averaging approach improved correspondence for cerebral white matter and total brain volume. Processing with SynthSR also significantly improved correspondence across widespread regions for estimates of cortical volume, surface area and subcortical volume, as well as within isolated prefrontal and temporal regions for estimates of cortical thickness. Conclusion: Applying super-resolution approaches to low-field imaging improves regional brain volume and surface area accuracy in young people. Finer-scale brain measurements, such as cortical thickness, remain challenging with the limited resolution of low-field systems.

Brain imaging is essential to the clinical management of patients with ischemic stroke. Timely and accessible neuroimaging, however, can be limited in clinical stroke pathways. Here, portable magnetic resonance imaging (pMRI) acquired at very low magnetic field strength (0.064 T) is used to obtain actionable bedside neuroimaging for 50 confirmed patients with ischemic stroke. Low-field pMRI detected infarcts in 45 (90%) patients across cortical, subcortical, and cerebellar structures. Lesions as small as 4 mm were captured. Infarcts appeared as hyperintense regions on T2-weighted, fluid-attenuated inversion recovery and diffusion-weighted imaging sequences. Stroke volume measurements were consistent across pMRI sequences and between low-field pMRI and conventional high-field MRI studies. Low-field pMRI stroke volumes significantly correlated with stroke severity and functional outcome at discharge. These results validate the use of low-field pMRI to obtain clinically useful imaging of stroke, setting the stage for use in resource-limited environments.

Low-field magnetic resonance imaging (MRI) has recently experienced a renaissance that is largely attributable to the numerous technological advancements made in MRI, including optimized pulse sequences, parallel receive and compressed sensing, improved calibrations and reconstruction algorithms, and the adoption of machine learning for image postprocessing. This new attention on low-field MRI originates from a lack of accessibility to traditional MRI and the need for affordable imaging. Low-field MRI provides a viable option due to its lack of reliance on radio-frequency shielding rooms, expensive liquid helium, and cryogen quench pipes. Moreover, its relatively small size and weight allow for easy and affordable installation in most settings. Rather than replacing conventional MRI, low-field MRI will provide new opportunities for imaging both in developing and developed countries. This article discusses the history of low-field MRI, low-field MRI hardware and software, current devices on the market, advantages and disadvantages, and low-field MRI's global potential. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 26 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

… The flip angle for best contrast between CSF and disc was calculated to be 18 at 0.1 T, but our goal was not only the highest possible contrast, but also an still acceptable signal-to-noise …

… for lesions of the lateral meniscus for images obtained at 0.1 T. Penrod et al. [20] reported a … Accuracy of diagnoses from magnetic resonance imaging of the knee. J Bone Joint Surg Am …

… The vertical field of 0.1 T is provided by an electro-magnet with an air gap of 15 cm equiped … of views demonstrate that high resolution MRI of limb extremities can be achieved at 0.1 T. …

Objective To simulate the magnetic and electric fields produced by RF coil geometries commonly used at low field. Based on these simulations, the specific absorption rate (SAR) efficiency can be derived to ensure safe operation even when using short RF pulses and high duty cycles. Methods Electromagnetic simulations were performed at four different field strengths between 0.05 and 0.1 T, corresponding to the lower and upper limits of current point-of-care (POC) neuroimaging systems. Transmit magnetic and electric fields, as well as transmit efficiency and SAR efficiency were simulated. The effects of a close-fitting shield on the EM fields were also assessed. SAR calculations were performed as a function of RF pulse length in turbo-spin echo (TSE) sequences. Results Simulations of RF coil characteristics and B_1^+ transmit efficiencies agreed well with corresponding experimentally determined parameters. Overall, the SAR efficiency was, as expected, higher at the lower frequencies studied, and many orders of magnitude greater than at conventional clinical field strengths. The tight-fitting transmit coil results in the highest SAR in the nose and skull, which are not thermally sensitive tissues. The calculated SAR efficiencies showed that only when 180° refocusing pulses of duration ~ 10 ms are used for TSE sequences does SAR need to be carefully considered. Conclusion This work presents a comprehensive overview of the transmit and SAR efficiencies for RF coils used for POC MRI neuroimaging. While SAR is not a problem for conventional sequences, the values derived here should be useful for RF intensive sequences such as T _1ρ, and also demonstrate that if very short RF pulses are required then SAR calculations should be performed.

… field MRI (2-4), together with the low-cost implementation of MRI … -field MRI performs poorly in fields less than about 0.1 T and … The loss of sensitivity in low-field NMR and MRI can be …

… Since 1994 patients referred to our department for evaluation for suspected perianal fistulae have been examined with lowfield MRI (0.1 T). The evaluations were carried out in the …

… Since that time, however, MRI has been routinely reserved to … First of all, most installed whole-body MRI imagers are not … small MRI system that used a low magnetic field of 0.1 T and …

… Ten healthy volunteers were studied with a 0.1 T clinical MR imager. T 1 ρ values were determined by first measuring the tissue signal intensities with different locking pulse durations (…

The aim of the present investigation was to determine spin lock (SL) relaxation parameters for the normal brain tissues and thus, to provide basis for optimizing the imaging contrast at …

Low-field magnetic resonance imaging (MRI) is an attractive route toward affordable, mobile, and energy-efficient scanners, but its intrinsically reduced signal-to-noise ratio (SNR) places stringent requirements on radiofrequency (RF) coil efficiency and impedance matching. This work presents the design, electromagnetic numerical simulations, and experimental validation of a solenoidal RF coil that uses a simple mechanical impedance matching strategy based on a movable non-resonant inductive coupling loop. A 12-turn copper-tube solenoid was implemented in an open vertical-field magnet operating at 0.1 T without a Faraday cage and compared with a reference coil matched by a lumped L-shape network. Numerical simulations and experimental measurements show that translating the non-resonant loop along the solenoid axis strongly modulates the reflection coefficient S11, enabling fine control of the coupling between the <inline-formula> <tex-math notation="LaTeX">$50~\Omega $ </tex-math></inline-formula> transmit/receive chain and the resonant solenoid. At the optimal loop position, the mechanically matched configuration results in higher mean <inline-formula> <tex-math notation="LaTeX">$\vert $ </tex-math></inline-formula>H<inline-formula> <tex-math notation="LaTeX">$\vert $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\vert $ </tex-math></inline-formula>B<inline-formula> <tex-math notation="LaTeX">${}_{1}^{+}\vert $ </tex-math></inline-formula> fields inside a uniform phantom compared to the conventional L-matched design, with similar field homogeneity. Phantom imaging and nutation experiments confirm that the loop position optimization improves RF efficiency and leads to increased mean SNR across slices, whereas the L-matched coil shows a higher signal but 30% higher noise and 47% broader linewidth. The proposed non-resonant inductive coupling therefore provides a low-cost, low-loss, and easily adjustable approach to impedance matching in low-field MRI, well suited to mobile point-of-care systems.

… Three glomus tumors of the fingers were detected using a dedicated hand and wrist low field (0.1 T) MR imager equipped with … MRI findings correlate well with surgery and biopsy. …

Recent years have seen a resurgence of interest in inexpensive low magnetic field (< 0.3 T) MRI systems mainly due to advances in magnet, coil and gradient set designs. Most of these advances have focused on improving hardware and signal acquisition strategies, and far less on the use of advanced image reconstruction methods to improve attainable image quality at low field. We describe here the use of our end-to-end deep neural network approach (AUTOMAP) to improve the image quality of highly noise-corrupted low-field MRI data. We compare the performance of this approach to two additional state-of-the-art denoising pipelines. We find that AUTOMAP improves image reconstruction of data acquired on two very different low-field MRI systems: human brain data acquired at 6.5 mT, and plant root data acquired at 47 mT, demonstrating SNR gains above Fourier reconstruction by factors of 1.5- to 4.5-fold, and 3-fold, respectively. In these applications, AUTOMAP outperformed two different contemporary image-based denoising algorithms, and suppressed noise-like spike artifacts in the reconstructed images. The impact of domain-specific training corpora on the reconstruction performance is discussed. The AUTOMAP approach to image reconstruction will enable significant image quality improvements at low-field, especially in highly noise-corrupted environments.

<italic>Objective:</italic> We present a model-based image reconstruction approach based on unrolled neural networks which corrects for image distortion and noise in low-field (<inline-formula><tex-math notation="LaTeX">$B_{0} \sim$</tex-math></inline-formula> 50 mT) MRI. <italic>Methods:</italic> Utilising knowledge about the underlying physics, a novel network architecture (SH-Net) is introduced which involves the estimation of spherical harmonic coefficients to guarantee a spatially smooth field map estimate. The SH-Net is integrated in an end-to-end trainable model which jointly estimates the <inline-formula><tex-math notation="LaTeX">$B_{0}$</tex-math></inline-formula>-field map as well as the image. Experiments were conducted on retrospectively simulated low-field data of human knees. <italic>Results:</italic> We compare our model to different model-based approaches at distinct noise levels and various <inline-formula><tex-math notation="LaTeX">$B_{0}$</tex-math></inline-formula>-field distributions. Our results show that our physics-informed neural network approach outperforms the purely model-based methods by improving the PSNR up to 11.7% and the RMSE up to 86.3%. <italic>Conclusion:</italic> Our end-to-end trained model-based approach outperforms existing methods in reconstructing image and <inline-formula><tex-math notation="LaTeX">$B_{0}$</tex-math></inline-formula>-field maps in the low-field regime. <italic>Significance:</italic> low-field MRI is becoming increasingly more popular as it enables access to MR in challenging situations such as intensive care units or resource poor areas. Our method allows for fast and accurate image reconstruction in such low-field imaging with <inline-formula><tex-math notation="LaTeX">$B_{0}$</tex-math></inline-formula>-inhomogeneity compensation under a wide range of various environmental conditions.

Low‐field magnetic resonance imaging (LF‐MRI) has emerged as a transformative technology, offering portable and cost‐effective solutions for medical imaging in resource‐limited settings. However, LF‐MRI systems face inherent challenges, including low signal‐to‐noise ratio (SNR) and reduced spatial resolution, which can compromise diagnostic accuracy. To systematically address these fundamental limitations, which involve complex, nonlinear image transformations, deep learning (DL) presents a powerful solution due to its proven capability in learning such mappings from data. This review explores the pivotal role of DL techniques in overcoming these limitations, focusing on two critical applications: denoising and super‐resolution. Recent advancements in DL architectures—such as U‐Net, generative adversarial networks (GANs), and diffusion models (DMs)—have demonstrated remarkable success in enhancing LF‐MRI image quality. For denoising, supervised models like U‐Net and denoising auto‐encoders (DAEs) effectively suppress noise while preserving anatomical details, while unsupervised approaches (e.g., Cycle‐GANs) leverage unpaired datasets to bridge the gap between low‐field (LF) and high‐field (HF) MRI. In super‐resolution, DL models like 3D U‐Net and residual channel attention networks (RCANs) reconstruct high‐resolution images from LF inputs, enabling finer detail visualization for clinical diagnostics. Key findings highlight the superiority of DL over conventional iterative methods in adaptability, robustness, and real‐time performance. However, challenges persist, including data dependency, computational costs, and limited interpretability. Innovations such as diffusion‐driven neural representations and dual‐acquisition 3D super‐resolution further push the boundaries of LF‐MRI quality. This review underscores DL‘s potential to democratize MRI access, particularly in low‐resource regions, while outlining future directions: improving generalization, reducing training data requirements, and integrating postprocessing pipelines. By directly tackling the key barriers of image quality, DL‐enhanced LF‐MRI is poised to make a significant impact in clinical scenarios where accessibility and speed are paramount, such as point‐of‐care diagnostics and emergency clinic, thereby helping bridge global healthcare disparities.

Low-field MRI scanners are significantly less expensive than their high-field counterparts, which gives them the potential to make MRI technology more accessible all around the world. In general, images acquired using low-field MRI scanners tend to be of a relatively low resolution, as signal-to-noise ratios are lower. The aim of this work is to improve the resolution of these images. To this end, we present a deep learning-based approach to transform low-resolution low-field MR images into high-resolution ones. A convolutional neural network was trained to carry out single image super-resolution reconstruction using pairs of noisy low-resolution images and their noise-free high-resolution counterparts, which were obtained from the publicly available NYU fastMRI database. This network was subsequently applied to noisy images acquired using a low-field MRI scanner. The trained convolutional network yielded sharp super-resolution images in which most of the high-frequency components were recovered. In conclusion, we showed that a deep learning-based approach has great potential when it comes to increasing the resolution of low-field MR images.

Deep learning has proven successful in a variety of medical image processing applications, including denoising and removing artifacts. This is of particular interest for low-field Magnetic Resonance Imaging (MRI), which is promising for its affordability, compact footprint, and reduced shielding requirements, but inherently suffers from low signal-to-noise ratio. In this work, we propose a method of simulating scanner-specific images from publicly available, 1.5T and 3T database of MR images, using a signal encoding matrix incorporating explicitly modeled imaging gradients and fields. We apply a stacked, U-Net architecture to reduce noise from the system and remove artifacts due to the inhomogeneous B0 field, nonlinear gradients, undersampling of k-space and image reconstruction to enhance low-field MR images. The final network is applied as a post-processing step following image reconstruction to phantom and human images acquired on a 60-67mT MR scanner and demonstrates promising qualitative and quantitative improvements to overall image quality.

ABSTRACT Three‐dimensional reconstruction of cortical surfaces from MRI for subsequent morphometric analysis is fundamental for understanding brain structure. While high‐field Magnetic Resonance Imaging (HF‐MRI) is the standard in research and clinical settings, its relatively limited availability hinders widespread use. Low‐field MRI (LF‐MRI), particularly portable systems, offers a cost‐effective and accessible alternative. However, existing cortical surface analysis tools, such as FreeSurfer, are optimized for high‐resolution HF‐MRI and struggle with the lower signal‐to‐noise ratio (SNR) and resolution of LF‐MRI. In this work, we present a machine learning method for 3D reconstruction and analysis of portable LF‐MRI scans over a range of contrasts and resolutions. Our method works “out of the box” and does not require retraining. It leverages a 3D U‐Net trained on synthetic LF‐MRI data to predict signed distance functions of the cortical surfaces, followed by geometric processing to ensure topologically accurate reconstructions. We evaluate our approach using paired HF‐/LF‐MRI scans of the same 15 subjects and 50 subjects from the ULF‐EnC dataset. The results show that our method robustly recovers surfaces across LF‐MRI acquisitions, with accuracy depending on MRI contrast mechanism (T1 vs. T2), slice anisotropy (axial vs. isotropic), and resolution. A 3 mm isotropic T2‐weighted scan acquired in under 4 min, which is comparable in duration to typical HF‐MRI acquisitions, yields strong agreement with HF‐derived surfaces: surface area correlates at r=0.96, cortical parcellations reach a Dice coefficient of 0.98, and gray matter volume achieves r=0.93. Cortical thickness remains more challenging but achieves correlations up to r=0.70, reflecting the difficulties of achieving sub‐mm precision with ~3 × 3 × 3 mm voxels. Our results also show that recon‐any performs robustly across other sequences and contrasts, though thickness estimates are particularly sensitive and degrade substantially with anisotropic or low‐resolution scans. We also validate our method on challenging postmortem LF‐MRI scans, further illustrating its robustness. Our method represents a significant step toward making cortical surface analysis feasible for portable LF‐MRI systems. The tool is publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/ReconAny.

Our goal is to develop and validate a practical protocol that guides users in identifying and suppressing electromagnetic noise in low‐field MRI systems, enabling operation near the thermal noise limit.

OBJECTIVE Ultra-Low-Field Magnetic Resonance Imaging (ULF MRI) offers low cost and portability but suffers from electromagnetic interference (EMI) in unshielded environments. This study developed a deep learning-based active EMI suppression method to overcome these limitations. METHODS Using a 68mT ULF MRI system, human body coupling was identified as a primary EMI pathway. We proposed EMIC-Net, a U-Net architecture incorporating Transformer and hybrid attention mechanisms, to learn the data-driven nonlinear mapping from sensing coil signals to radio-frequency (RF) receiver coil interference. Acquired data underwent phase and gain compensation prior to model training. The model's efficacy was validated through in vivo human brain imaging, comparing its performance with EDITER and standard CNN methods, and by assessing the impact of varying EMI coil numbers and training data volumes. RESULTS EMIC-Net effectively suppressed complex dynamic EMI. It restored image SNR from 2.35 dB to 17.63 dB, with significant PSNR and SSIM improvements. Image quality neared shielded acquisitions and surpassed comparative methods. Three EMI coils provided optimal balance, and the model showed data efficiency, requiring a small dataset for effective training. CONCLUSION The EMIC-Net method accurately predicts and efficiently removes EMI in unshielded ULF MRI, offering superior performance and practicality. SIGNIFICANCE This research promotes portable, low-cost ULF MRI for primary healthcare and bedside diagnosis. It also offers insights for mitigating complex EMI issues in other RF sensing domains.

Objective: Passive shielding is usually applied to block electro-magnetic interference (EMI) for portable very-low field MRI scanners, but it goes against the mobility of the scanners. Here, the reference channel-based active EMI suppression (AES) system was proposed to discard them. Methods: Different from the existing studies, this work started with analyzing the interference transmission paths and discovered that the human body coupling was the main path. Then, the “ring-” shaped EMI receiving coil designed here together with the electrocardiograph (ECG) electrode patch were used to suppress the human-body-coupled interferences. For the first time, the periphery data of the k-spaces of the RF coil and the EMI detectors were utilized to calculate the transfer factors in multiple frequency bands by using the least square method. The reference EMI signals were transferred to the interferences in the MR signal by multiplying the transfer factors in each frequency band, and the denoised MR signal could be obtained by subtracting the transferred reference EMI signals. Results: The prototype of the AES system was applied to a 50 mT unshielded portable MRI scanner. The in-vivo experiments indicated that the interference suppression rate of the AES system equipped with the “ring-” shaped EMI receiving coil could reach 96.8%. Meanwhile, the SNR of the images after interference suppression by the AES system equipped with both types of detectors was 97.2% of that of the images scanned inside the shielded room. Conclusion: The proposed AES system ensures that the portable MRI scanner works normally in unshielded environment. Significance: Our study provides a solution to make portable MRI scanners truly movable.

Point‐of‐care MRI requires operation outside of Faraday shielded rooms normally used to block image‐degrading electromagnetic interference (EMI). To address this, we introduce the EDITER method (External Dynamic InTerference Estimation and Removal), an external sensor‐based method to retrospectively remove image artifacts from time‐varying external interference sources.

… in ultra-low field (ULF) magnetic resonance imaging (MRI) in an … suppression method to eliminate these artifacts in MRI images. … The adaptive suppression method is first implemented to …

One of the main challenges for point-of-care (POC) MRI systems is electromagnetic interference (EMI), since such systems are intended for use outside conventional Faraday-shielded rooms. Many methods have been proposed based on EMI detection via sensors external to the MRI system, followed by different types of signal processing to reduce artifacts in the image. Although these methods can be very effective, they do increase the complexity of the overall system, and introduce more potential failure points for systems designed for challenging environments. In this work we introduce a new method that does not require external sensors, but rather uses the "MR-silent" mode of an RF coil to detect the EMI, followed by simple subtraction from the signal from the "MR-active" mode. This method can be performed post-acquisition if there are two receive channels available, or as demonstrated here can operate with a single-channel receive detection system with the addition of a simple passive 180° power splitter/combiner into the receive chain. Proof-of-concept in vivo results show that a reduction in the standard deviation of the EMI up to ∼ 97 % is possible, with average values ∼ 90 %.

ABSTRACT Electromagnetic interference (EMI) is a significant challenge for low‐field MRI systems operating without conventional Faraday‐shielded rooms. This interference degrades image quality and limits deployment in space‐constrained or electromagnetically noisy environments. Traditional EMI mitigation approaches include external shields, subject grounding via electrodes, or active noise cancellation requiring synchronized receive channels. These methods either limit portability, introduce patient discomfort, or demand advanced hardware. In this work, we start from the hypothesis that EMI primarily couples capacitively from the body to the RF coil. We investigated two methods of blocking capacitive coupling while preserving inductive MRI signal detection: First, we employed capacitive segmentation of the RF coil and studied its effect on EMI coupling. Second, we present FENCE(Flexible Electromagnetic Noise reduCtion Endo‐shield), a novel approach blocking capacitive coupling using flexible PCB shields placed inside the RF coil. FENCE can be retrofitted to existing RF coils without significant mechanical modifications. Finite element (FE) simulations were used to estimate the expected shielding performance and the impact on RF coil losses prior to practical implementation. Testing in various scenarios then demonstrated that the combination of FENCE with segmented solenoid coils is effective against both environmental noise sources and controlled EMI. In phantom experiments, FENCE significantly improved imaging performance and reduced EMI levels to near‐baseline levels with 9% reduction in coil quality factor (Q factor), showing good agreement with the predictions from the FE simulations. In vivo head imaging confirmed these results across diverse electromagnetic environments significantly improving imaging performance while showing an 18% decrease in Q factor. FENCE provides a simple method for EMI mitigation in low‐field MRI, enhancing image quality while maintaining system portability and accessibility. This approach could help to expand the deployment of low‐field MRI systems in low‐cost point‐of‐care applications where conventional shielding is impractical.

… Firstly, we analyze the correlation of EMI signals between the sensing coil and the MRI coil … Another adaptive interference suppression method uses the spatial correlation of EMI at …

At present, MRI scans are typically performed inside fully enclosed radiofrequency (RF) shielding rooms, posing stringent installation requirements and causing patient discomfort. We aim to eliminate electromagnetic interference (EMI) for MRI with no or incomplete RF shielding. In this study, a method of active sensing and deep learning EMI prediction is presented to model, predict, and remove EMI signal components from acquired MRI signals. Specifically, during each MRI scan, separate EMI‐sensing coils placed in various locations are utilized to simultaneously sample external and internal EMI signals within two windows (for both conventional MRI signal acquisition and EMI characterization acquisition). A convolution neural network model is trained using the EMI characterization data to relate EMI signals detected by EMI‐sensing coils to EMI signals in the MRI receive coil. This model is then used to retrospectively predict and remove EMI signal components detected by the MRI receive coil during the MRI signal acquisition window. This strategy was implemented on a low‐cost ultralow‐field 0.055 T permanent magnet MRI scanner without RF shielding. It produced final image signal‐to‐noise ratios that were comparable with those obtained using a fully enclosed RF shielding cage, and outperformed existing analytical EMI elimination methods (i.e., spectral domain transfer function and external dynamic interference estimation and removal [EDITER] methods). A preliminary experiment also demonstrated its applicability on a 1.5 T superconducting magnet MRI scanner with incomplete RF shielding. Altogether, the results demonstrated that the proposed method was highly effective in predicting and removing various EMI signals from both external environments and internal scanner electronics at both 0.055 T (2.3 MHz) and 1.5 T (63.9 MHz). The proposed strategy enables shielding‐free MRI. The concept is relatively simple and is potentially applicable to other RF signal detection scenarios in the presence of external and/or internal EMI.

… low-field MRI systems in resource-constrained scenarios. Aiming at the above problems, this paper proposes an active EMI suppression … active noise reduction in low-field MRI systems. …

Portable low‐field (<0.1T) MRI is increasingly used for point‐of‐care imaging, but electromagnetic interference (EMI) presents a significant challenge, especially in unshielded environments. EMI can degrade image quality and compromise diagnostic utility. This study investigates whether subject grounding can effectively reduce EMI and improve image quality, comparing different grounding strategies.

Background Magnetic resonance imaging (MRI) is a safe non-invasive and nonionizing medical imaging modality that is used to visualize the structure of human anatomy. Conventional (high-field) MRI scanners are very expensive to purchase, operate and maintain, which limit their use in many developing countries. This study is part of a project that aims at addressing these challenges and is carried out by teams from Mbarara University of Science and Technology (MUST) in Uganda, Leiden University Medical Center (LUMC) in the Netherlands, Delft University of Technology (TU Delft) in the Netherlands and Pennsylvania State University (PSU) in the USA. These are working on developing affordable, portable and low-field MRI scanners to diagnose children in developing countries with hydrocephalus. The challenges faced by the teams are that the low-field MRI scanners currently under development are characterized by low Signal-to-Noise Ratio (SNR), and long scan times. Methods We propose an algorithm called adaptive-size dictionary learning algorithm (AS-DLMRI) that integrates information-theoretic criteria (ITC) and Dictionary learning approaches. The result of the integration is an adaptive-size dictionary that is optimal for any input signal. AS-DLMRI may help to reduce the scan time and improve the SNR of the generated images, thereby improving the image quality. Results We compared our proposed algorithm AS-DLMRI with adaptive patch-based algorithm known as DLMRI and non-adaptive CSMRI technique known as LDP. DLMRI and LDP have been used as the baseline algorithms in other related studies. The results of AS-DLMRI are consistently slightly better in terms of PSNR, SNR and HFEN than for DLMRI, and are significantly better than for LDP. Moreover, AS-DLMRI is faster than DLMRI. Conclusion Using a dictionary size that is appropriate to the input data could reduce the computational complexity, and also the construction quality since only dictionary atoms that are relevant to the task are included in the dictionary and are used during the reconstruction. However, AS-DLMRI did not completely remove noise during the experiments with the noisy phantom. Our next step in our research is to integrate our proposed algorithm with an image denoising function.

Ultra‐low‐field (ULF) MRI provides a cost‐effective, portable imaging option but has relatively low SNR and long acquisition times compared to standard clinical scans. This study presents a time‐conditioned zero‐shot self‐supervised learning image reconstruction framework (ULF‐ZS‐SSL) to accelerate 3D‐acquired single‐coil ULF MRI without relying on external training data. In addition, for faster computation, a transfer‐learning (TL) variant (ULF‐ZS‐SSL‐TL) was implemented by pretraining on a small fully‐sampled ULF brain dataset and fine‐tuning on the target subject in a zero‐shot manner. This image reconstruction method combines a physics‐based data‐consistency step with a 3D residual network prior and sinusoidal time‐step embeddings to improve convergence speed. Data were acquired on a 47 mT Halbach‐based scanner using 3D turbo spin‐echo sequences with T 1 ‐, T 2 ‐, and inversion‐recovery–T 1 ‐weighted contrasts. Additional T 1 ‐weighted wrist scans were acquired to evaluate cross‐anatomy generalization. Both true and retrospectively undersampled data were compared with total variation (TV) and model‐based deep learning (MoDL). The ULF‐ZS‐SSL method produced high‐quality reconstructions across all tested contrasts, outperforming zero‐filled and TV reconstructions, particularly at higher acceleration factors. Time‐step conditioning improved convergence speed, while ULF‐ZS‐SSL‐TL further accelerated the image reconstruction three‐fold, enabling full 3D reconstructions in about 3 min. Pretraining on brain data also worked well for wrist reconstructions, indicating cross‐anatomy generalization. The ULF‐ZS‐SSL framework enables accurate, training‐free reconstruction of undersampled single‐coil ULF MRI data, as does the ULF‐ZS‐SSL‐TL approach using minimal training data. The combination of physics‐based unrolling, time‐step conditioning, and transfer‐learning supports rapid and robust application in portable or resource‐limited ULF MRI systems.

Objective To correct for image distortions produced by standard Fourier reconstruction techniques on low field permanent magnet MRI systems with strong $${B}_{0}$$ B 0 inhomogeneity and gradient field nonlinearities. Materials and methods Conventional image distortion correction algorithms require accurate $${\Delta B}_{0}$$ Δ B 0 maps which are not possible to acquire directly when the $${B}_{0}$$ B 0 inhomogeneities also produce significant image distortions. Here we use a readout gradient time-shift in a TSE sequence to encode the $${B}_{0}$$ B 0 field inhomogeneities in the k-space signals. Using a non-shifted and a shifted acquisition as input, $$\Delta {B}_{0}$$ Δ B 0 maps and images were reconstructed in an iterative manner. In each iteration, $$\Delta {B}_{0}$$ Δ B 0 maps were reconstructed from the phase difference using Tikhonov regularization, while images were reconstructed using either conjugate phase reconstruction (CPR) or model-based (MB) image reconstruction, taking the reconstructed field map into account. MB reconstructions were, furthermore, combined with compressed sensing (CS) to show the flexibility of this approach towards undersampling. These methods were compared to the standard fast Fourier transform (FFT) image reconstruction approach in simulations and measurements. Distortions due to gradient nonlinearities were corrected in CPR and MB using simulated gradient maps. Results Simulation results show that for moderate field inhomogeneities and gradient nonlinearities, $$\Delta {B}_{0}$$ Δ B 0 maps and images reconstructed using iterative CPR result in comparable quality to that for iterative MB reconstructions. However, for stronger inhomogeneities, iterative MB reconstruction outperforms iterative CPR in terms of signal intensity correction. Combining MB with CS, similar image and $$\Delta {B}_{0}$$ Δ B 0 map quality can be obtained without a scan time penalty. These findings were confirmed by experimental results. Discussion In case of $${B}_{0}$$ B 0 inhomogeneities in the order of kHz, iterative MB reconstructions can help to improve both image quality and $$\Delta {B}_{0}$$ Δ B 0 map estimation.

… Therefore, in this study, CS reconstruction with selfcalibrated k-space … reconstruction was used for the undersampled datasets; a reduction factor of 2.5 was used for the undersampled …

Research in MRI technology has traditionally expanded diagnostic benefit by developing acquisition techniques and instrumentation to enable MRI scanners to "see more." This typically focuses on improving MRI's sensitivity and spatiotemporal resolution, or expanding its range of biological contrasts and targets. In complement to the clear benefits achieved in this direction, extending the reach of MRI by reducing its cost, siting, and operational burdens also directly benefits healthcare by increasing the number of patients with access to MRI examinations and tilting its cost-benefit equation to allow more frequent and varied use. The introduction of low-cost, and/or truly portable scanners, could also enable new point-of-care and monitoring applications not feasible for today's scanners in centralized settings. While cost and accessibility have always been considered, we have seen tremendous advances in the speed and spatial-temporal capabilities of general-purpose MRI scanners and quantum leaps in patient comfort (such as magnet length and bore diameter), but only modest success in the reduction of cost and siting constraints. The introduction of specialty scanners (eg, extremity, brain-only, or breast-only scanners) have not been commercially successful enough to tilt the balance away from the prevailing model: a general-purpose scanner in a centralized healthcare location. Portable MRI scanners equivalent to their counterparts in ultrasound or even computed tomography have not emerged and MR monitoring devices exist only in research laboratories. Nonetheless, recent advances in hardware and computational technology as well as burgeoning markets for MRI in the developing world has created a resurgence of interest in the topic of low-cost and accessible MRI. This review examines the technical forces and trade-offs that might facilitate a large step forward in the push to "jail-break" MRI from its centralized location in healthcare and allow it to reach larger patient populations and achieve new uses. Level of Evidence: 5 Technical Efficacy Stage: 6 J. Magn. Reson. Imaging 2019.

Portable low-cost MRI systems have the potential to enable point-of-care and timely MRI diagnosis, and to make this imaging modality available to routine scans and to underdeveloped areas. With simplicity, no maintenance, no power consumption, and low cost, permanent magnets or arrays are attractive to use as a source of magnetic field to realize portability and low cost for a scanners. However, when taking the Fourier imaging approach and using linear gradient fields, homogeneous fields are required, thus either a bulky magnet is needed, or the imaging volume is too small to image an organ if the magnet is scaled down. Recently, with the progress on image reconstruction based on non-linear gradient field, field patterns without spatial-linearity can be used as spatial encoding magnetic fields to encode MRI signals for imaging. As a result, the requirements for the homogeneity of the field can be relaxed, which allows permanent magnets(arrays) with reduced sizes, reduced weight to image bigger volumes covering organs such as a head. It offers chances to construct a truly portable low-cost MRI scanner. For this exciting potential application, permanent magnets(arrays) have attracted increased attention. A magnet(array) is strongly associated with the imaging volume, reconstruction methods, and RF excitation and coils, etc. through field patterns and homogeneity. This paper offers a review on permanent magnets(arrays) of different kinds, especially those can be used for spatial encoding towards the development of portable and low-cost MRI systems. It is aimed to familiarize the readers with relevant knowledge, literature, and the latest updates of the development on permanent magnets for MRI. Perspectives on and challenges of using permanent magnets to supply a patterned magnetic field, without spatial-linearity nor high homogeneity, for reconstruction in a portable setup are discussed.

Lightweight and compact permanent magnet arrays (PMAs) are suitable for portable dedicated magnetic resonance imaging (MRI). It is worth exploring different PMA design possibilities and optimization methods with an adequate balance between weight, size, and performance, in addition to Halbach arrays and C-shaped/H-shaped magnets which are widely used. In this paper, the design and optimization of a sparse high-performance inward-outward ring-pair PMA consisting of magnet cuboids is presented for portable imaging of the brain. The design is lightweight (151kg) and compact (inner bore diameter: 270mm, outer diameter: 616mm, length: 480mm, 5-Gauss range: 1840×1840×2340mm3). The optimization framework is based on the genetic algorithm with a consideration of both field properties and simulated image quality. The resulting PMA design has an average field strength of 101.5 mT and a field pattern with a built-in linear readout gradient. Subtracting the best fit to the linear gradient target resulted in a residual deviation from the target field of 0.76mT and an average linear regression coefficient of 0.85 to the linear gradient. The required radiofrequency bandwidth is 6.9% within a field of view (FoV) with a diameter of 200mm and a length of 125mm. It has a magnetic field generation efficiency of 0.67mT/kg, which is high among the sparse PMAs that were designed for an FoV with a diameter of 200mm. The field can be used to supply gradients in one direction working with gradient coils in the other two directions, or can be rotated to encode signals for imaging with axial slice selection. The encoding capability of the designed PMA was examined through the simulated reconstructed images. The force experienced by each magnet in the design was calculated, and the feasibility of a physical implementation was confirmed. The design can offer an increased field strength, and thus, an increased signal-to-noise ratio. It has a longitudinal field direction that allows the application of technologies developed for solenoidal magnets. This proposed design can be a promising alternative to supplying the main and gradient fields in combination for dedicated portable MRI. Lastly, the design is resulted from a fast genetic algorithm-based optimization in which fast magnetic field calculation was applied and high design flexibility was feasible. Within optimization iterations, image quality metrics were used for the encoding field of a magnet configuration to guide the design of the magnet array.

Starting from general results of magnetostatics, we give fundamental considerations on the design and characterization of permanent magnets for NMR based on harmonic analysis and symmetry. We then propose a simple geometry that takes advantage of some of these considerations and discuss the practical aspects of the assembly of a real magnet based on this geometry, involving the characterization of its elements, the optimization of the layout and the correction of residual inhomogeneities due to material and geometry imperfections. We report with this low-cost, light-weight magnet (100 euros and 1.8 kg including the aluminum frame) a field of 120 mT (5.1 MHz proton) with a 10 ppm natural homogeneity over a sphere of 1.5 mm in diameter.

In this paper, we present the design and optimization of a ring-pair permanent magnet array for head imaging in a low-field portable magnetic resonance imaging (MRI) system. The proposed array generates a longitudinal main static magnetic field ( ${B}_{0}$ field) with an average field strength of 169.7 mT and a homogeneity of 24 786 ppm in a field of view with a diameter of 200 mm and a thickness of 50 mm. It is a significant increase in field homogeneity by 79.7% compared to a traditional ring-pair structure of the same dimension and mass while still maintaining a similar field strength. The optimization was implemented by applying a genetic algorithm and by proposing an efficient current model for the forward calculation of the magnetic field. The effectiveness of the optimization is validated by realistic simulations using COMSOL Multiphysics. Compared to a Halbach array, where the field is transversal and the existing coil designs cannot be applied directly, the proposed array generates stronger fields. Its magnetic field is longitudinal, which allows the direct application of the advancements in RF coil designs in a conventional MRI system to the imaging system using the proposed magnet array.

Mobile medical imaging devices are invaluable for clinical diagnostic purposes both in and outside healthcare institutions. Among the various imaging modalities, only a few are readily portable. Magnetic resonance imaging (MRI), the gold standard for numerous healthcare conditions, does not traditionally belong to this group. Recently, low-field MRI technology companies have demonstrated the first decisive steps towards portability within medical facilities and vehicles. However, these scanners’ weight and dimensions are incompatible with more demanding use cases such as in remote and developing regions, sports facilities and events, medical and military camps, or home healthcare. Here we present in vivo images taken with a light, small footprint, low-field extremity MRI scanner outside the controlled environment provided by medical facilities. To demonstrate the true portability of the system and benchmark its performance in various relevant scenarios, we have acquired images of a volunteer’s knee in: (i) an MRI physics laboratory; (ii) an office room; (iii) outside a campus building, connected to a nearby power outlet; (iv) in open air, powered from a small fuel-based generator; and (v) at the volunteer’s home. All images have been acquired within clinically viable times, and signal-to-noise ratios and tissue contrast suffice for 2D and 3D reconstructions with diagnostic value. Furthermore, the volunteer carries a fixation metallic implant screwed to the femur, which leads to strong artifacts in standard clinical systems but appears sharp in our low-field acquisitions. Altogether, this work opens a path towards highly accessible MRI under circumstances previously unrealistic.

We propose a magnet featuring a standard 52 mm bore that creates a longitudinal magnetic field. The magnet is made out of commercial magnetized NdFeB cubes, costing less than 100 €. This device is a low-cost solution to build a portable magnet generating a field of 100 mT with an intrinsic homogeneity as good as 40 ppm over a 5 mm 3 volume. Furthermore, the bore can accomodate a standard narrow bore shim stack and NMR probe in order to shim the field and conduct low-field NMR experiments. We established an assembly process including characterization of the magnetic cubes, cube sorting and optimization of the assembly according to the needs of the application. Aspects of the assembly method are discussed, including characterizing the magnet cubes, sorting them and arranging them in an optimal fashion.

Access to scanners for magnetic resonance imaging (MRI) is typically limited by cost and by infrastructure requirements. Here, we report the design and testing of a portable prototype scanner for brain MRI that uses a compact and lightweight permanent rare-earth magnet with a built-in readout field gradient. The 122-kg low-field (80 mT) magnet has a Halbach cylinder design that results in a minimal stray field and requires neither cryogenics nor external power. The built-in magnetic field gradient reduces the reliance on high-power gradient drivers, lowering the overall requirements for power and cooling, and reducing acoustic noise. Imperfections in the encoding fields are mitigated with a generalized iterative image reconstruction technique that leverages previous characterization of the field patterns. In healthy adult volunteers, the scanner can generate T1-weighted, T2-weighted and proton density-weighted brain images with a spatial resolution of 2.2 × 1.3 × 6.8 mm3. Future versions of the scanner could improve the accessibility of brain MRI at the point of care, particularly for critically ill patients. A portable prototype scanner for brain MRI that uses a compact and lightweight permanent rare-earth magnet with a built-in readout field gradient generates clinically relevant images of the brain, as shown in adult volunteers.

Abstract Since the discovery of magnetic resonance imaging (MRI) as an imaging modality, it has evolved immensely and is still doing so. Most imaging modalities have made bedside or emergency imaging possible due to their portability. This aspect is yet to be fully evaluated and established in the case of MRI as its high-field strength requires specialized infrastructure and its time-consuming nature makes its portability questionable. The goal of this review is to access the efficiency and feasibility of low-field portable MRI (pMRI) systems in a wide array of health care applications. Articles from indexed journals, on PubMed, Springer, Elsevier, etc. databases, relevant to this study were searched and reviewed. This review provides an atypical design that could be used in making a pMRI unit that could find its potential in diagnosing a wide variety of pathologies with an added advantage of imaging critical patients in the intensive care unit or patients in isolation due to its portability, imaging patients with implants or prosthesis effectively due to its low field, pediatric imaging due to its high speed, for guided interventions, imaging obese and claustrophobic patients due to its open nature, in dental imaging, extremity scanning, etc. With its vast spectrum of applications in the health care system, the future of low-field pMRI units seems to be bright.

Ferromagnetic structures, particularly the anti-eddy plate, in a bi-planar permanent-magnet-type low-field (0.05 T) magnetic resonance imaging (MRI) brain scanner can distort the gradient field in the target region. This study aims to provide a new gradient coil design method that reduces ferromagnetic influences on gradient field linearity. Thus, a simplified model of electromagnetic (EM) structures of the permanent-magnet-type MRI scanners was established. By using precise analytical proof, the anti-eddy plate was reduced to a homogeneous magnetic plate. The overall effects of the EM structures, which can be represented by bi-planar magnetic plates, were evaluated. In sequence, the image magnetic dipole was first introduced to show the effects of anti-eddy plates were added to the conventional equivalent magnetic dipole (EMD) approach. A novel equivalent image magnetic dipole (EIMD) method was proposed to build the gradient coil pattern. The effect of ferromagnetic materials was predicted throughout the gradient coil design phase using the proposed method, and a high-linear gradient field was generated under real working conditions. The computational and experimental results showed that the gradient coil was linear when ferromagnetic structures were present. The effectiveness of the proposed method was demonstrated by comparing T1-weighted images of the conventional method to those of the proposed method. The proposed method reduced image distortion caused by nearby EM structures in bi-planar permanent-magnet-type low-field MRI systems and provided an effective and concise solution for gradient coil designs.

Low-cost and portable magnetic resonance imaging (MRI) may make this imaging modality more accessible. Permanent-magnet-array is an option to supply a static magnetic field (B-field) with portability, low cost, and no power consumption. However, it has low field strength. Moreover, it does not have linear gradients, thus the signals and the images are not linked by the Fourier transformation as they are in a conventional system. The B-field generated by an array and called spatial-encoding-magnetic-field (SEM), is spatially non-linear and always on. Such an SEM, in terms of the field strength, direction, homogeneity, pattern and its field pattern variation, is related to the image quality. This relation is crucial because it can be used to guide the magnet and system design for high image quality and portability. However, it has not been systematically studied. In this paper, the characteristics of the SEMs from different magnet array designs are identified. Due to the non-linearity of the SEMs, local structural similarity (SSIM) index is proposed to evaluate the region-dependent image quality, and local k-space is applied to analyze the region-dependent effects of these SEMs on image reconstruction. Moreover, point spread function is applied to analyze the overall effect of the SEMs on the quality of reconstructed images. Besides the intrinsic effects of the SEMs, those of the external factors, e.g. the receive coil sensitivity, are analyzed. This study identifies the unique characteristics of the SEMs in a permanent-magnet-array-based MRI system, and offers methods to analyze the unique relation between the image quality and the field. It can not only guide the magnet designs but also trigger more design ideas, e.g., the design of the mechanical movement of the magnet array, and that of the static magnetic field shimming coils, paving the way towards a low-field MRI system with practical portability.

Neuroimaging is an inevitable component of the assessment of neurological emergencies. Magnetic resonance imaging (MRI) is the preferred imaging modality for detecting neurological pathologies and provides higher sensitivity than other modalities. However, difficulties such as intra-hospital transport, long exam times, and availability in strict access-controlled suites limit its utility in emergency departments and intensive care units (ICUs). The evolution of novel imaging technologies over the past decades has led to the development of portable MRI (pMRI) machines that can be deployed at point-of-care. This article reviews pMRI technologies and their clinical implications in acute neurological conditions. Benefits of pMRI include timely and accurate detection of major acute neurological pathologies such as stroke and intracranial hemorrhage. Additionally, pMRI can be potentially used to monitor the progression of neurological complications by facilitating serial measurements at the bedside.

To design a low‐cost, portable permanent magnet‐based MRI system capable of obtaining in vivo MR images within a reasonable scan time.