Keywords: Low-Field MRI, Low-Field MRI, breast cancer

Motivation: Field Cycling Imaging (FCI) has never been used in clinics and its capability in medical diagnosis has not yet demonstrated.

Goal(s): Our goal was to demonstrate the capabilities of FCI as an imaging modality to diagnose breast cancer by measuring the T₁ variations at low field from 2.3 to 200 mT. 

Approach: Ten patients were imaged with our recent FCI prototype scanner and images were compared with standard clinical imaging and histology. 

Results: FCI provides relevant biomarkers of molecular dynamics that detect tumours and discriminate invasive from non-invasive tumours. In addition, FCI is insensitive to breast density  and provides accurate tumour delineation. 

Impact: FCI, which uses variant low field strengths, could complement clinical imaging without contrast agents non-invasively and  could improve the estimation of tumour size and resection margins, even for dense breasts, including DCIS which is often under/over-estimated in clinical imaging.

Keywords: Low-Field MRI, Low-Field MRI, SPEN, Field Inhomogeneity, less distortion, low SAR

Motivation: Geometric distortions in echo-planar acquisitions pose challenges for correction in portable low-field MRI due to significant field inhomogeneities.

Goal(s): Our goal was to apply spatiotemporal encoding (SPEN) MRI at a 110 mT portable low-field system, aiming for nearly distortion-free echo-planar images.

Approach: We leveraged the low SAR in low-field MR to optimize the SPEN technique for substantial gains in sensitivity. SPEN-based 2D imaging ,3D imaging and DWI were compared with EPI-based imaging and EPI TopUp correction results.

Results: Approximately distortion-free SPEN acquisitions including robust 2D, 3D imaging and DWI demonstrated the potential clinical values of SPEN in the portable low field systems.

Impact: SPEN MRI provides a unique and robust fast echo planar acquisition approach to obtain nearly distortion-free images at low-cost portable low field systems, thereby expanding the prospects for rapid imaging, navigation, and functional imaging in portable low-field MRI.

Keywords: Low-Field MRI, Health Care Economics, MRI value

Motivation: While the USA boasts one of the highest numbers of MRIs per million inhabitants, the impact of social determinants of health on accessibility remains uncertain. This issue becomes particularly relevant as Low Field MRI could level the playing field and mitigate existing inequities.

Goal(s): Primary goal was to investigate the relationship between MRI availability and poverty rate in the US.

Approach: We tested the correlation between poverty rate and both the quantity and geographical distribution of MRIs. 

Results: The number of MRI units exhibited an exponential decline (R²=0.9823) with the poverty rate, with geographical location and other pertinent socioeconomic factors playing a role.  

Impact: LF MRI has attributes that make it particularly suitable for implementation in low-middle income countries (LMICs). Nevertheless, features like affordability and portability can also potentially be pivotal in addressing healthcare disparities within the US.

Keywords: Low-Field MRI, Low-Field MRI, EMI reduction

Motivation: Electromagnetic interference (EMI) reduction is essential to utilize low-field point-of-care MRI devices in different environments with different noise conditions.

Goal(s): Improving EMI reduction by subject grounding

Approach: Noise scans and brain images were acquired with and without subject grounding. This is done with normal imaging conditions and when adding broadband noise or single frequency EMI.  

Results: The SNR of the images was improved by a factor of ~4 when grounding the subject and adding broadband EMI to the experiment. A factor ~2 improvement in SNR was observed for the single frequency EMI and a factor of 1.5 improvement for the normal imaging conditions.

Impact: Subject grounding effectively reduced EMI interference. It may be relevant to investigate whether this setup would be able to reduce EMI from medical equipment, or general environmental EM noise in typically challenging POC settings (ICU, emergency room, in remote locations)

Keywords: Low-Field MRI, Low-Field MRI

Motivation: The current electromagnetic interference (EMI) noise removal approaches for low-field portable magnetic resonance imaging (MRI) only focus on single receive coil EMI removal, which ignores noise relationship among coil elements of RF coil arrays.

Goal(s): Our goal was to remove EMI noise in receive coils not only related to EMI detectors, but also among receive coil elements.

Approach: A signal correlation matrix was constructed from signals acquired by EMI coils and receive coils, and decorrelation matrix was calculated for EMI noise removal.

Results: Phantom results and pilot in vivo human brain images showed that the proposed method have better EMI noise removal rate.

Impact: The proposed EMI noise removal method for unshielded low-field MRI can better improve signal-to-noise ratio (SNR) compared to state-of-the-art methods, which enable the EMI noise removal for array coils in low-field portable MRI application.

Keywords: Low-Field MRI, Low-Field MRI

Motivation: To develop low-cost and patient-friendly MRI scanners to address global healthcare disparities.

Goal(s): To demonstrate cervical spine (C-spine) MRI on a low-cost and RF shielding-free 0.05T MRI scanner.

Approach: Typical imaging protocols were implemented on a newly developed 0.05T MRI scanner. The scanner is compact, RF shielding-free, and acoustically quiet during scanning. Further, a deep learning electromagnetic interference (EMI) elimination method and a data-driven reconstruction strategy were designed.

Results: The deep learning EMI elimination method effectively removed EMI noise, and the data driven reconstruction method suppressed image noise and artifacts while increasing spatial resolution, leading to significantly improved image quality.

Impact: We demonstrate high-quality C-spine MRI on a low-cost and shielding-free 0.05T MRI scanner through exploiting computing power and extensive high-field MRI data. These developments will lead to a new generation of affordable, patient-centric, and computing-powered MRI scanners.

Keywords: Low-Field MRI, Brain Connectivity, Connectomics

Motivation: Neuroscience MRI research, including assessment of structural connectomics, has been largely limited to high-resource settings.

Goal(s): To democratise assessment of brain connectivity by demonstrating the first ever diffusion-weighted imaging (DWI)-based connectomics at 64 mT.

Approach: 15-direction DWI data were acquired at 64 mT. Whole-brain tractograms were recovered after deep learning based denoising and constrained spherical deconvolution.  Whole-brain adjacency matrices and graph-theory parameters were extracted, and their test-retest agreement and variability assessed. For one subject, results were compared to high-field MRI.

Results: Global graph-theory parameters (e.g., small-worldness) showed high test-retest agreement. However, inter-hemispheric connectivity was overestimated at 64 mT compared to high-field results.

Impact: Our unique combinations of low-field (64 mT) diffusion-weighted imaging, denoising, spherical deconvolution and connectomics opens up new research opportunities, allowing the assessment of structural connectivity and network neuroscience studies of under-served populations where this has never previously been possible.

Keywords: Hybrid & Novel Systems Technology, Low-Field MRI

Motivation: To overcome the limitations of signal co-registration in MEG, we constructed MEG/MRI hybrid system.

Goal(s): To demonstrate feasibility of this system. High-sensitivity OPMs in a reasonable magnetic shield with MRI system.

Approach: Phantom experiments. Phantom includes triangle coil and MEG measures its magnetic field. MRI scanned this phantom with 3D-SE sequence.

Results: We recognized signal peak generated by 100 nAm current dipole moment in amplitude spectrum density visually. We also confirmed the structure of the phantom in 3D MR images.

Impact: We made MEG/MRI hybrid system for the purpose of accurate signal co-registration between them. MEG employed scalar-mode OPM, and its noise level was 367 fT/rHz. We scanned phantom using 7-mT MRI scanner and confirmed its structure.