Portable magnetic resonance imaging of patients indoors, outdoors and at home

Fast, high-quality, and unshielded 0.2 T low-field mobile MRI using minimal hardware resources

OBJECTIVE

To propose a deep learning-based low-field mobile MRI strategy for fast, high-quality, unshielded imaging using minimal hardware resources.

METHODS

Firstly, we analyze the correlation of EMI signals between the sensing coil and the MRI coil to preliminarily verify the feasibility of active EMI shielding using a single sensing coil. Then, a powerful deep learning EMI elimination model is proposed, which can accurately predict the EMI components in the MRI coil signals using EMI signals from at least one sensing coil. Further, deep learning models with different task objectives (super-resolution and denoising) are strategically stacked for multi-level post-processing to enable fast and high-quality low-field MRI. Finally, extensive phantom and brain experiments were conducted on a home-built 0.2 T mobile brain scanner for the evaluation of the proposed strategy.

RESULTS

20 healthy volunteers were recruited to participate in the experiment. The results show that the proposed strategy enables the 0.2 T scanner to generate images with sufficient anatomical information and diagnostic value under unshielded conditions using a single sensing coil. In particular, the EMI elimination outperforms the state-of-the-art deep learning methods and numerical computation methods. In addition, 2 × super-resolution (DDSRNet) and denoising (SwinIR) techniques enable further improvements in imaging speed and quality.

DISCUSSION

The proposed strategy enables low-field mobile MRI scanners to achieve fast, high-quality imaging under unshielded conditions using minimal hardware resources, which has great significance for the widespread deployment of low-field mobile MRI scanners.

Low-Field, Low-Cost, Point-of-Care Magnetic Resonance Imaging

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.

Open-source magnetic resonance imaging: Improving access, science, and education through global collaboration

Open-source practices and resources in magnetic resonance imaging (MRI) have increased substantially in recent years. This trend started with software and data being published open-source and, more recently, open-source hardware designs have become increasingly available. These developments towards a culture of sharing and establishing nonexclusive global collaborations have already improved the reproducibility and reusability of code and designs, while providing a more inclusive approach, especially for low-income settings. Community-driven standardization and documentation efforts are further strengthening and expanding these milestones. The future of open-source MRI is bright and we have just started to discover its full collaborative potential. In this review we will give an overview of open-source software and open-source hardware projects in human MRI research.

Whole-body magnetic resonance imaging at 0.05 Tesla

Despite a half-century of advancements, global magnetic resonance imaging (MRI) accessibility remains limited and uneven, hindering its full potential in health care. Initially, MRI development focused on low fields around 0.05 Tesla, but progress halted after the introduction of the 1.5 Tesla whole-body superconducting scanner in 1983. Using a permanent 0.05 Tesla magnet and deep learning for electromagnetic interference elimination, we developed a whole-body scanner that operates using a standard wall power outlet and without radiofrequency and magnetic shielding. We demonstrated its wide-ranging applicability for imaging various anatomical structures. Furthermore, we developed three-dimensional deep learning reconstruction to boost image quality by harnessing extensive high-field MRI data. These advances pave the way for affordable deep learning-powered ultra-low-field MRI scanners, addressing unmet clinical needs in diverse health care settings worldwide.

Water-Fat Separation for the Knee on a 50 mT Portable MRI Scanner

OBJECTIVE

The Dixon method is frequently employed in clinical and scientific research for fat suppression, because it has lower sensitivity to static magnetic field inhomogeneity compared to chemical shift selective saturation or its variants and maintains image signal-to-noise ratio (SNR). Recently, research on very-low-field (VLF < 100 mT) magnetic resonance imaging (MRI) has regained popularity. However, there is limited literature on water-fat separation in VLF MRI. Here, we present a modified two-point Dixon method specifically designed for VLF MRI.

METHODS

Most experiments were performed on a homemade 50 mT portable MRI scanner. The receiving coil adopted a homemade quadrature receiving coil. The data were acquired using spin-echo and gradient-echo sequences. We considered the T2* effect, and added priori information to existing two-point Dixon method. Then, the method used regional iterative phasor extraction (RIPE) to extract the error phasor. Finally, least squares solutions for water and fat were obtained and fat signal fraction was calculated.

RESULTS

For phantom evaluation, water-only and fat-only images were obtained and the local fat signal fractions were calculated, with two samples being 0.94 and 0.93, respectively. For knee imaging, cartilage, muscle and fat could be clearly distinguished. The water-only images were able to highlight areas such as cartilage that could not be easily distinguished without separation.

CONCLUSION

This work has demonstrated the feasibility of using a 50 mT MRI scanner for water-fat separation.

SIGNIFICANCE

To the best of our knowledge, this is the first reported result of water-fat separation at a 50 mT portable MRI scanner.

Bridging the gap: improving correspondence between low-field and high-field magnetic resonance images in young people

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

T1- and T2-weighted structural MR images were obtained from a low-field (64mT) Hyperfine and high-field (3T) Siemens system in N = 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

Single pairs of T1- and T2-weighted images acquired at low field showed high correspondence to high-field-strength images for estimates of total intracranial volume, surface area cortical volume, subcortical volume, and total brain volume (r range = 0.60-0.88). Correspondence was lower for cerebral white matter volume (r = 0.32, p = 0.007, q = 0.009) and non-significant for mean cortical thickness (r = -0.05, p = 0.664, q = 0.664). Processing images with SynthSR yielded significant improvements in correspondence for total brain volume, white matter volume, total surface area, subcortical volume, cortical volume, and total intracranial volume (r range = 0.85-0.97), with the exception of global mean cortical thickness (r = 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.

Improving portable low-field MRI image quality through image-to-image translation using paired low- and high-field images

Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-effective, sustainable with lower carbon emissions than superconducting high-field MRI scanners. However, the images produced have relatively poor image quality, lower signal-to-noise ratio, and limited spatial resolution. This study develops and investigates an image-to-image translation deep learning model, LoHiResGAN, to enhance the quality of low-field (64mT) MRI scans and generate synthetic high-field (3T) MRI scans. We employed a paired dataset comprising T1- and T2-weighted MRI sequences from the 64mT and 3T and compared the performance of the LoHiResGAN model with other state-of-the-art models, including GANs, CycleGAN, U-Net, and cGAN. Our proposed method demonstrates superior performance in terms of image quality metrics, such as normalized root-mean-squared error, structural similarity index measure, peak signal-to-noise ratio, and perception-based image quality evaluator. Additionally, we evaluated the accuracy of brain morphometry measurements for 33 brain regions across the original 3T, 64mT, and synthetic 3T images. The results indicate that the synthetic 3T images created using our proposed LoHiResGAN model significantly improve the image quality of low-field MRI data compared to other methods (GANs, CycleGAN, U-Net, cGAN) and provide more consistent brain morphometry measurements across various brain regions in reference to 3T. Synthetic images generated by our method demonstrated high quality both quantitatively and qualitatively. However, additional research, involving diverse datasets and clinical validation, is necessary to fully understand its applicability for clinical diagnostics, especially in settings where high-field MRI scanners are less accessible.

Low-field MRI: A report on the 2022 ISMRM workshop

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.

Tackling SNR at low-field: a review of hardware approaches for point-of-care systems

OBJECTIVE

To review the major hardware components of low-field point-of-care MRI systems which affect the overall sensitivity.

METHODS

Designs for the following components are reviewed and analyzed: magnet, RF coils, transmit/receive switches, preamplifiers, data acquisition system, and methods for grounding and mitigating electromagnetic interference.

RESULTS

High homogeneity magnets can be produced in a variety of different designs including C- and H-shaped as well as Halbach arrays. Using Litz wire for RF coil designs enables unloaded Q values of ~ 400 to be reached, with body loss representing about 35% of the total system resistance. There are a number of different schemes to tackle issues arising from the low coil bandwidth with respect to the imaging bandwidth. Finally, the effects of good RF shielding, proper electrical grounding, and effective electromagnetic interference reduction can lead to substantial increases in image signal-to-noise ratio.

DISCUSSION

There are many different magnet and RF coil designs in the literature, and to enable meaningful comparisons and optimizations to be performed it would be very helpful to determine a standardized set of sensitivity measures, irrespective of design.

NMR Magnetometer Based on Dynamic Nuclear-Polarization for Low-Strength Magnetic Field Measurement

Nuclear magnetic resonance (NMR) magnetometers are considered due to their ability to map magnetic fields with high precision and calibrate other magnetic field measurement devices. However, the low signal-to-noise ratio of low-strength magnetic fields limits the precision when measuring magnetic fields below 40 mT. Therefore, we developed a new NMR magnetometer that combines the dynamic nuclear polarization (DNP) technique with pulsed NMR. The dynamic pre-polarization technique enhances the SNR under a low magnetic field. Pulsed NMR was used in conjunction with DNP to improve measurement accuracy and speed. The efficacy of this approach was validated through simulation and analysis of the measurement process. Next, a complete set of equipment was constructed, and we successfully measured magnetic fields of 30 mT and 8 mT with an accuracy of only 0.5 Hz (11 nT) at 30 mT (0.4 ppm) and 1 Hz (22 nT) at 8mT (3 ppm).

MaRCoS, an open-source electronic control system for low-field MRI

Every magnetic resonance imaging (MRI) device requires an electronic control system that handles pulse sequences and signal detection and processing. Here we provide details on the architecture and performance of MaRCoS, a MAgnetic Resonance COntrol System developed by an open international community of low-field MRI researchers. MaRCoS is inexpensive and can handle cycle-accurate sequences without hard length limitations, rapid bursts of events, and arbitrary waveforms. It has also been readily adapted to meet the requirements of the various academic and private institutions participating in its development. We describe the MaRCoS hardware, firmware and software that enable all of the above, including a Python-based graphical user interface for pulse sequence implementation, data processing and image reconstruction.

Specific absorption rate (SAR) simulations for low-field (< 0.1 T) MRI systems

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 B₁⁺ 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.

Benchmarking the performance of a low-cost magnetic resonance control system at multiple sites in the open MaRCoS community

PURPOSE

To describe the current properties and capabilities of an open-source hardware and software package that is being developed by many sites internationally with the aim of providing an inexpensive yet flexible platform for low-cost MRI.

METHODS

This article describes three different setups from 50 to 360 mT in different settings, all of which used the MaRCoS console for acquiring data, and different types of software interface (custom-built GUI or Pulseq overlay) to acquire it.

RESULTS

Images are presented both from phantoms and in vivo from healthy volunteers to demonstrate the image quality that can be obtained from the MaRCoS hardware/software interfaced to different low-field magnets.

CONCLUSIONS

The results presented here show that a number of different sequences commonly used in the clinic can be programmed into an open-source system relatively quickly and easily, and can produce good quality images even at this early stage of development. Both the hardware and software will continue to develop, and it is an aim of this article to encourage other groups to join this international consortium.