Low-field Magnetic Resonance Imaging Research Articles

MRI denoising with a non-blind deep complex-valued convolutional neural network.

MR images with high signal-to-noise ratio (SNR) provide more diagnostic information. Various methods for MRI denoising have been developed, but the majority of them operate on the magnitude image and neglect the phase information. Therefore, the goal of this work is to design and implement a complex-valued convolutional neural network (CNN) for MRI denoising. A complex-valued CNN incorporating the noise level map (non-blind DnCNN) was trained with ground truth and simulated noise-corrupted image pairs. The proposed method was validated using both simulated and in vivo data collected from low-field scanners. Its denoising performance was quantitively and qualitatively evaluated, and it was compared with the real-valued CNN and several other algorithms. For the simulated noise-corrupted testing dataset, the complex-valued models had superior normalized root-mean-square error, peak SNR, structural similarity index, and phase ABSD. By incorporating the noise level map, the non-blind DnCNN showed better performance in dealing with spatially varying parallel imaging noise. For in vivo low-field data, the non-blind DnCNN significantly improved the SNR and visual quality of the image. The proposed non-blind DnCNN provides an efficient and effective approach for MRI denoising. This is the first application of non-blind DnCNN to medical imaging. The method holds the potential to enable improved low-field MRI, facilitating enhanced diagnostic imaging in under-resourced areas.

Using NMR linewidth broadening for magnetic characterization of micrometer-size particles in silicone matrix

Standard magnetization measurements on samples of small magnetic particles may generate conflicting results. We compare the mass magnetization of MgZn ferrite particles in a compressed bulk material and in dry powder and find that at low fields the values can differ by as much as 50%. We show here that embedding the particles in a silicone matrix and measuring the NMR linewidth in combination with simulations establishes a new method to evaluate the magnetization of the particles at different fields and temperatures. The NMR results agree with the direct magnetization measurements of the powder samples and the magnetization measurements of the particles embedded in silicone. This work is motivated, in part, by studies on using small magnetic particles as MRI temperature indicators, and we compare the effectiveness of these particles for low-field and high-field MRI thermometry.

Simplified shielded MEG-MRI multimodal system with scalar-mode optically pumped magnetometers as MEG sensors

Magnetoencephalography (MEG) conventionally operates within high-performance magnetic shields due to the extremely weak magnetic field signals from the measured objects and the narrow dynamic range of the magnetic sensors employed for detection. This limitation results in elevated equipment costs and restricted usage. Additionally, the information obtained from MEG is functional images, and to analyze from which part of the brain the signals are coming, it is necessary to capture morphological images separately. When MEG and morphological imaging devices are separate, despite their individual high measurement accuracies, discrepancies in positional information may arise. In response, we have developed a low-field magnetic resonance imaging system that incorporates scalar-mode optically pumped magnetometers with a wide dynamic range and exceptionally high measurement sensitivity as sensors for MEG. Operating at low magnetic fields eliminates the need for superconducting coils in magnetic resonance imaging and the high-performance magnetic shields essential for MEG, promising a substantial cost reduction compared to traditional approaches. We achieved a noise level of about 16.7pT/Hz1/2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${16.7}\\,\\hbox {pT/Hz}^{1/2}$$\\end{document} with a single channel magnetometer, and reached a noise level of 367fT/cm/Hz1/2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${367}\\,\\hbox {fT/cm/Hz}^{1/2}$$\\end{document} with a baseline of 1 cm through differential measurements. We employed this system to conduct sequential OPM-based magnetic field measurements and MRI imaging, successfully demonstrating the compatibility of high OPM sensitivity with clear MRI acquisition.

Low-Field Magnetic Resonance Imaging for the Detection of Medial Meniscal Lesions in Cranial Cruciate-Deficient Stifle Joints in Dogs.

The aim of this study was to substantiate the ability of noninvasive low-field magnetic resonance imaging (lfMRI) to accurately depict meniscal lesions, thereby establishing lfMRI as a viable option for the diagnosis of meniscal pathology. This was a prospective, controlled study. In each of 57 stifle joints, 5 predefined lfMRI sequences using a 0.25 T lfMRI were used. The scans were evaluated by an experienced surgeon (DECVS) and through external assessment by a radiologist (DECVDI). The patients were divided into two groups depending on their meniscal status after lfMRI. Dogs (Group 1; n = 33) without lfMRI evidence of medial meniscal pathology underwent TPLO, whereas concurrent craniomedial mini-arthrotomy was performed in patients with suspected meniscal lesions (Group 2; n = 24). Gait assessment was performed 6 weeks later and 6 months postoperatively. Cohen's kappa value of 0.8571 corresponds to a high level of agreement between the observers. A significant correlation was found between preoperative lameness severity and intraoperative meniscal damage (p < 0.04). The results showed a highly significant correlation between meniscal injuries on MRI and intraoperative findings (p < 0.0001). LfMRI appears to be a noninvasive, reliable imaging tool for the detection of medial meniscal lesions in cranial cruciate-deficient stifle joints, aiming to avoid (minimally) invasive diagnostics in healthy menisci.

Low-Field MRI for Dental Imaging in Pediatric Patients With Supernumerary and Ectopic Teeth: A Comparative Study of 0.55 T and Ultra-Low-Dose CT.

This study sought to elucidate the diagnostic performance of 0.55 T magnetic resonance imaging (MRI) for pediatric dental imaging, specifically in terms of the image quality (IQ) for detecting ectopic and/or supernumerary teeth, compared with routine ultra-low-dose computed tomography (ULD-CT) of the jaw. A total of 16 pediatric patients (mean age: 12.4 ± 2.6 years, range: 9-17 years) with ectopic and/or supernumerary teeth screened from January 2023 to January 2024 were enrolled in this prospective, single-center study. All patients underwent ULD-CT as the clinical reference standard and 0.55 T MRI as the study scan on the same day. A 0.6-mm isotropic 3-dimensional T1w FLASH sequence was developed with a dedicated field of view of the upper and lower jaws. ULD-CT was performed using a new single-source computed tomography (CT) scanner equipped with a tin filter (Sn100, slice thickness: 1 mm, quality reference mAs: 24). The IQ for the tooth axis, the tooth length, the tooth root, root resorptions, cysts, the periodontal ligament space, and the mandibular canal was evaluated twice by 3 senior readers using a 5-point Likert scale (LS) (LS score of 1: insufficient, 3: reduced IQ but sufficient for clinical use, and 5: perfect) and compared between both methods. Subsequently, the results were dichotomized into nonvalid (LS score of ≤2) and valid (LS score of ≥3) for clinical use. A total of 49 ectopic and/or supernumerary teeth in 16 pediatric patients were investigated using ULD-CT (CTDI: 0.43 ± 0.09 mGy) and 0.55 T MRI. The mean MRI acquisition time was 9:45 minutes. Motion artifacts were nonsignificantly different between 0.55 T MRI and ULD-CT (P = 0.126). The IQ for the tooth axis, the tooth root, root resorptions, and cysts was similar between the methods. The IQ for the periodontal ligament space and tooth length favored ULD-CT by 14% (confidence interval [CI]: 4.3%-24%) and 7.5% (CI: 1.8%-13%), respectively, whereas that for the mandibular canal favored 0.55 T MRI by -35% (CI: -54%-16%). Sufficient IQ was found especially for cystic lesions (CT: 100% sufficient, MRI: 95% sufficient), the tooth root (CT: 100%, MRI: 98%), root resorptions (CT: 94%; MRI: 85%), the tooth axis (CT: 100%; MRI: 98%), and the tooth length (CT: 99%; MRI: 91%). The findings indicate that 0.55 T MRI is a feasible, radiation-free technique for delineating ectopic and/or supernumerary teeth in pediatric patients. Nevertheless, to date, 0.55 T MRI has not yet been able to provide an optimal IQ for all anatomical tooth and jaw structures. In cases of advanced clinical indications that require optimal spatial resolution, high-resolution CT or cone-beam CT may still be necessary.

Analysis of Water Adsorption Capacity, Thermal Behavior, Antioxidant Activity, and Smoke Volume by Polyol Ester in Reconstituted Tobacco.

A series of ester derivatives with hydroxyl groups were created by esterification of 1,2-propanediol and glycerol with o-toluoyl chloride via the catalyst 4-dimethylaminopyridine (DMAP). All the compounds were verified using nuclear magnetic resonance (1H NMR, 13C NMR), infrared spectroscopy, and high resolution mass spectrometry. Moisture absorption and desorption experiments were carried out on the compounds' pure systems and their systems in reconstituted tobacco, while their hygroscopicity and moisturizing properties were evaluated using low-field nuclear magnetic resonance imaging. Two compounds with better absorption and moisture retention were selected: 4 (1-hydroxypropan-2-yl 2-methylbenzoate) and 6 (2,3-dihydroxypropyl 2-methylbenzoate), respectively. Moreover, the thermal stability of compounds 4 and 6 were investigated using thermogravimetric (TG) analysis. The results of TG-DTG showed that the maximum mass loss rate of compound 4 appeared at 251.67 °C, with a mass loss rate of 87.61%. At the peak temperature of 291.83 °C, 6 showed the highest decomposition rate with 49.23% mass loss rate. To explore the antioxidant properties, we constructed three different antioxidant systems. Furthermore, compound 6 showed excellent effects, with scavenging rates reaching 69.28%, 71.92%, and 75.93%, respectively. Smoke volume results indicated that the compounds optimally replaced propanediol and glycerol at a rate of 40%. The results provide a theoretical reference for the development of new moisturizing agents based on polyol esters in the tobacco industry.

Zero-echo-time sequences in highly inhomogeneous fields.

Zero-echo-time (ZTE) sequences have proven a powerful tool for MRI of ultrashort tissues, but they fail to produce useful images in the presence of strong field inhomogeneities (14 000 ppm). Here we seek a method to correct reconstruction artifacts from non-Cartesian acquisitions in highly inhomogeneous , where the standard double-shot gradient-echo approach to field mapping fails. We present a technique based on magnetic field maps obtained from two geometric distortion-free point-wise (SPRITE) acquisitions. To this end, we employ three scanners with varying field homogeneities. These maps are used for model-based image reconstruction with iterative algebraic techniques (ART). For comparison, the same prior information is fed also to widely used Conjugate Phase (CP) algorithms. Distortions and artifacts coming from severe inhomogeneities, at the level of the encoding gradient, are largely reverted by our method, as opposed to CP reconstructions. This holds even close to the limit where intra-voxel bandwidths (determined by inhomogeneities, up to 1.2 kHz) are comparable to the encoding inter-voxel bandwidth (determined by the gradient fields, 625 Hz in this work). We have benchmarked the performance of a new method for ZTE imaging in highly inhomogeneous magnetic fields. For example, this can be exploited for dental imaging in affordable low-field MRI systems, and can be expanded for arbitrary pulse sequences and extreme magnet geometries, as in, for example, single-sided MRI.

First in-vivo magic angle directional imaging using dedicated low-field MRI.

To report the first in-vivo results from exploiting the magic angle effect, using a dedicated low-field MRI scanner that can be rotated about two axes. The magic angle directional imaging (MADI) method is used to depict collagen microstructures with 3D collagen tractography of knee ligaments and the meniscus. A novel low-field MRI system was developed, based on a transverse field open magnet, where the magnet can be rotated about two orthogonal axes. Sets of volume scans at various orientations were obtained in healthy volunteers. The experiments focused on the anterior cruciate ligament (ACL) and the meniscus of the knee. The images were co-registered, anatomical regions of interest (ROIs) were selected and the collagen fiber orientations in each voxel were estimated from the observed image intensity variations. The 3D collagen tractography was superimposed on conventional volume images. The MADI method was successfully employed for the first time producing in-vivo results comparable to those previously reported for excised animal specimens using conventional MRI. Tractography plots were generated for the ACL and the menisci. These results are consistent with the known microstructure of collagen fibers in these tissues. Images obtained using low-field MRI with 1 mm3 resolution were of sufficient quality for the MADI method, which was shown to produce high quality in-vivo information of collagen microstructures. This was achieved using a cost effective and sustainable low-field magnet making the technique potentially accessible and scalable, potentially changing the way we image injuries or disease in joints.

Low field magnetic resonance imaging of palmar osteochondral fragmentation of the distal phalanx

SummaryBackgroundThe presence of palmar osteochondral fragments of the distal phalanx is a newly recognised cause of foot lameness with only a few cases having been reported.ObjectivesTo describe the low field magnetic resonance imaging (MRI) appearance of palmar articular fragmentation of the distal phalanx and concurrent abnormalities of the distal interphalangeal joint (DIPJ); to investigate the prevalence of these fragments in a population of sports and pleasure horses and their potential clinical significance.Study designRetrospective descriptive case series.MethodsThis is a retrospective descriptive case series in which all Sports and Pleasure horses undergoing standing low‐field MRI of the feet between 2015 and 2023 were reviewed. Appearance, location and number of palmar articular fragments were recorded. When a palmar fragment was present, abnormal findings of the DIPJ were graded.ResultsA total of 855 horses underwent MRI examination of one or both front feet. Palmar osteochondral fragmentation of the distal phalanx was identified in four horses (0.4% prevalence), three Lusitanos and one Warmblood. The age range was 4–17 years old. All fragments were located along the palmarolateral margin of the glenoid of the distal phalanx in front feet. Concurrent imaging abnormalities of the joint were present in all four horses.Main limitationsLack of histopathology.ConclusionsThe prevalence of palmar articular fragments of the distal phalanx was low in this population of horses. Palmar osteochondral fragmentation must be considered when evaluating lameness localised to the foot, since it was identified only in lame limbs and associated with other imaging abnormalities of the DIPJ.

Recovering MRI magnetic field strength properties using machine learning based on image compression quality scores

AbstractOver the years, significant hardware advancements have led to higher magnetic field MRI (Magnetic Resonance Imaging) acquisitions, providing improved diagnostic image quality at a higher cost. While higher image quality is desired, the investment required for high-field MRI imaging systems remains a significant consideration. Furthermore, historical patient records may still contain low-field magnetic strength MRI data. In this work, we present how image compression quality scores of MRI images could be used to recover their magnetic field strength properties. This work presents an interesting inverse problem scenario where output properties are used to recover an input property of an image. We implemented supervised machine learning models that utilize compressed image quality scores - based on Mean Squared Error (MSE), Structural Similarity Index (SSIM), Visual Information Fidelity (VIF), and Earth Movers Distance (EM) metrics - as inputs. These models classify images into specific classes of magnetic field strength at which they were acquired (Low, Medium, and High) based solely on the quality scores of their compressed copies. The trained machine learning models (Support Vector Machine (SVM), Logistic Regression, K-nearest Neighbours (KNN), Decision Tree and Random Forest) achieved nearly 100% performance efficiency based on accuracy and F-1 score for all considered quality measures. This comprehensive evaluation offers insights into how compression techniques and quality factors impact image fidelity across various MRI magnetic field strengths. The findings of this study may prove valuable in recovering missing meta-tag information in historical image DICOM records and Context-Based Retrieval Systems (CBRS). The approach of using quality scores as features in training machine learning models, as demonstrated in this work, can be extended to other image groups where input parameters create statistically significant distinctions or are challenging to extract from images.

Low-field MRI study of the 1H distribution and migration in porous sediments during natural gas conversion from methane hydrate

Gas hydrates, crystalline compounds formed from water and guest molecules like methane, are gaining attention as a promising clean energy source. While research has extensively focused on the extraction and transport of natural gas hydrates from offshore marine sediments, the dynamic processes in the porous sediments remain under explored. This study employs low-field Magnetic Resonance Imaging (MRI) to investigate the in-situ formation and dissociation of methane hydrate within a partially saturated stack of glass beads. By integrating advanced MRI techniques, including 1D dhk SPRITE, bulk T1 distribution, and 2D spatial T2 imaging, this research provides a comprehensive analysis of fluid distribution and migration during phase transitions in the porous sediments. Three key findings emerged from the study. First, SE-SPI measurements successfully quantified hydrate and residual water saturation, aligning with previous X-ray CT and high-field MRI studies and validating low-field MRI for hydrate research. Secondly, this study introduced a volumetric approach to T1 and T2 measurements, distinguishing between free and bound water, with characteristic relaxation times for each, confirming the technique's capability to differentiate types of residual water. Finally, spatially resolved T2 relaxation time distributions revealed vertical fluid migration within the pore spaces, identifying an equilibrium zone where inflow and outflow rates were balanced. Overall, this study underscores the effectiveness of low-field MRI as a noninvasive tool for analyzing hydrate-bearing sediments. The findings lay the groundwork for further exploration of hydrate in porous sediments, enhancing our understanding of gas production dynamics in hydrate-rich environments.

Enhancement of Image Quality in Low-Field Knee MR Imaging Using Deep Learning.

The purpose of this study is to investigate the potential of deep learning (DL) techniques to enhance the image quality of low-field knee MR images, with the ultimate goal of approximating the standards ofhigh-field knee MR imaging. We analyzed knee MR images collected from 45 patients with knee disorders and six normal subjects using a 3T MR scannerand those collected from 25 patients with knee disorders using a 0.4T MR scanner. Two DL models were developed: a fat-suppression contrast-generation model and a super-resolution model. These DL models were trained using 3T knee MR imaging data and applied to 0.4T knee MR imaging data. Visual assessments of anatomical structures and image noise and abnormality detection with diagnostic confidence levels on the original 0.4T MR images and those afterDL enhancement were conducted by two board-certified radiologists. Statistical analyses were performed using McNemar's test and the Wilcoxon signed-rank test. DL-enhanced MR images significantly improved the depiction of anatomical structures and reduced image noise compared to the original MR images. The number of abnormal findings detected and the diagnostic confidence levels were higher in the DL-enhanced MR images, indicating the potential for more accurate diagnoses. DL techniques effectively enhance the image quality of low-field knee MR images by leveraging 3T MR imaging data. This enhancement significantly improves image quality and diagnostic confidence levels, making low-field MR images much more reliable for detecting abnormalities. This advancement offers a useful alternative for clinical settings, especially in resource-limited environments, without compromising diagnostic accuracy.

Development of an EGSnrc multi-leaf collimator component module and treatment head model for a low-field MRI linear accelerator.

Monte Carlo (MC) modeling of MR-guided radiotherapy (MRgRT) treatment machines enables the characterization of photon/electron interactions in the presence of a magnetic field. The EGSnrc MC code system is a well-established system for radiation dose calculations. The multi-leaf collimator (MLC) component modules presently available within the EGSnrc MC code system do not include a model of the double-focused MLC available on a low-field (0.35T) MRI linear accelerator (MR linac). Here we developed and validated a new component module (CM) for the low-field MRgRT MLC using the EGSnrc/BEAMnrc/DOSXYZnrc code system. We performed detailed modeling of the treatment head and validated the model using measurements and calculations from the vendor-specific treatment planning system (TPS). The detailed geometry of the low-field MR linac MLC and other treatment head structures were modeled using BEAMnrc. Comparisons of DOSXYZnrc simulated dose against measurements and the low-field MR linac TPS for a variety of AAPM TG-53 task group report suggested square and shaped fields, as well as a step-and-shoot intensity-modulated radiotherapy (IMRT) plan, are presented. Our model agrees with both measured and TPS calculated data on average within 2%/2mm (dose/DTA) criterion for square field profiles. Output factors agreed within 1% for field sizes down to 2.49×2.49cm2 and within 2% of TPS data for the smallest field size of 0.83×0.83cm2. Shaped field and IMRT MC calculations agreed with measured and TPS data such that the gamma pass rates (3%/2mm) were 99.5% and (3%/3mm) 96.2%, respectively. We developed and validated an MLC CM (SYNCVRMLC) for the low-field MR linac using the EGSnrc MC code systems. This new CM will facilitate MC computation of fluence and dose distributions using BEAMnrc/DOSXYZnrc for patients treated on the low-field MR linac.

MRI at low field: A review of software solutions for improving SNR.

Low magnetic field magnetic resonance imaging (MRI) ( <1T) 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 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.

Physicochemical and digestive properties of curcumin stabilized by quaternized chitosan/sodium alginate nanoemulsion coatings

Curcumin (Cur) is a bioactive substance, but its instability limits its use in the food system. Thus, a new polysaccharide-based coating is developed by combining quaternized chitosan (QCS) with sodium alginate (SA) in this study to improve the bioaccessibility and stability of Cur. The microstructure of the emulsion was characterized by fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and low-field pulsed nuclear magnetic resonance imaging (LF-NMR). The thermal, ionic strength, and pH stability of emulsions were measured. Results indicate that QCS/SA (1:1)-Cur is more stable than other three nanoemulsion systems and the encapsulation rate of which reaches 90.62 %. It also shows higher stability in gastric fluid, excellent bioaccessibility and FFAs in intestinal fluid, as well as significant antibacterial property, antioxidant activity and water dispersibility. This study introduces a new method for obtaining stable, functional Cur-coated systems for food applications.

Low-field MRI lung opacity severity associated with decreased DLCO in post-acute Covid-19 patients

ObjectivesTo evaluate the clinical significance of low-field MRI lung opacity severity. MethodsRetrospective cross-sectional analysis of post-acute Covid-19 patients imaged with low-field MRI from 9/2020 through 9/2022, and within 1 month of pulmonary function tests (PFTs), 6-min walk test (6mWT), and symptom inventory (SI), and/or within 3 months of St. George Respiratory Questionnaire (SGRQ) was performed.Univariate and correlative analyses were performed with Wilcoxon, Chi-square, and Spearman tests. The association between disease and demographic factors and MR opacity severity, PFTs, 6mWT, SI, and SGRQ, and association between MR opacity severity with functional and patient-reported outcomes (PROs), was evaluated with mixed model analysis of variance, covariance and generalized estimating equations. Two-sided 5 % significance level was used, with Bonferroni multiple comparison correction. Results81 MRI exams in 62 post-acute Covid-19 patients (median age 57, IQR 41–64; 25 women) were included. Exams were a median of 8 months from initial illness. Univariate analysis showed lung opacity severity was associated with decreased %DLCO (ρ = −0.55, P = .0125), and lung opacity severity quartile was associated with decreased %DLCO, predicted TLC, FVC, and increased FEV1/FVC.Multivariable analysis adjusting for sex, initial disease severity, and interval from Covid-19 diagnosis showed MR lung opacity severity was associated with decreased %DLCO (P < .001). Lung opacity severity was not associated with PROs. ConclusionLow-field MRI lung opacity severity correlated with decreased %DLCO in post-acute Covid-19 patients, but was not associated with PROs.

Improved metal suppression using new generation low-field MRI: a biophantom feasibility study.

Novel 0.55 MRI scanners have the potential to reduce metal artifacts around orthopedic implants. The purpose of this study was to compare metal artifact size and depiction of anatomy between 0.55T and 3.0T MRI in a biophantom. Steel and titanium screws were implanted in 12 porcine knee specimens and imaging at 0.55T and 3T MRI was performed using the following sequences: turbo spin-echo (TSE), TSE with view angle tilting (VAT), and slice encoding for metal artifact correction (SEMAC) with proton-density (PD) and T2-weighted short-tau inversion-recovery (T2w-STIR) contrasts. Artifacts were measured, and visualization of anatomy (cartilage, bone, growth plates, cruciate ligaments) was assessed and compared between groups. Metal artifacts were significantly smaller at 0.55T. The smallest artifact sizes were achieved with SEMAC at 0.55T for both PD and T2w-STIR sequences; corresponding relative size reductions vs. 3.0T were 78.7% and 79.4% (stainless steel) and 45.3% and 1.4% (titanium). Depiction of anatomical structures was superior at 0.55T. Substantial reduction of artifact size resulting in superior depiction of anatomical structures is possible on novel 0.55T MRI systems. Further clinical studies are required to elucidate patient-relevant advantages.

Multidimensional RF pulse design with consideration of concomitant field effects.

To develop a small-tip multidimensional RF pulse design procedure that incorporates linear time-invariant gradient imperfections and concomitant field effects. This could be particularly important for contemporary low-field MRI systems with high-performance gradients. We developed an extension of the small-tip excitation k-space formalism, where concomitant fields were approximated as a Bloch-Siegert shift in the rotating frame. This was evaluated using realistic simulations of 2D selective excitation at various field strengths (0.2T, 0.55T, 1.5T, 3T, and 7T) with single and parallel transmit. Simulated excitation profiles from the original and extended k-space formalisms were compared. Experimental validations were performed at 0.55T with a single-channel transmit. The extended formalism provides improved 2D excitation profiles in all scenarios simulated, compared against the original formalism. The proposed method corrects the concomitant field effects on 2D selective excitations for B0 > 0.2T when the magnitude of the B0 is far larger than that of nonrotating concomitant fields. Simulation and phantom experiments at 0.55T match well for both original and proposed methods, with the proposed method providing sharper and more accurate excitation profiles at off-isocenter distances up to 15 cm. The impact of the proposed method is greatest in scenarios where concomitant fields are substantial, such as low field strengths and off-isocenter. Concomitant fields can be modeled as a Bloch-Siegert shift in the rotating frame during multidimensional RF pulse design, resulting in improved excitation profiles with sharp edges. This is important to consider for off-isocenter excitations and imaging at low field strengths with strong gradients.

Joint [Formula: see text] and Image Reconstruction in Low-Field MRI by Physics-Informed Deep-Learning.

We present a model-based image reconstruction approach based on unrolled neural networks which corrects for image distortion and noise in low-field ( B0 ∼ 50 mT) MRI. 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 B0-field map as well as the image. Experiments were conducted on retrospectively simulated low-field data of human knees. We compare our model to different model-based approaches at distinct noise levels and various B0-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%. Our end-to-end trained model-based approach outperforms existing methods in reconstructing image and B0-field maps in the low-field regime. 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 B0-inhomogeneity compensation under a wide range of various environmental conditions.

Deep Learning-Based Synthetic Computed Tomography for Low-Field Brain Magnetic Resonance-Guided Radiation Therapy

PurposeMR-guided Radiation Therapy (MRgRT) enables online adaptation to address intra- and inter-fractional changes. To address the need of high-fidelity synthetic CT (synCT) required for dose calculation, we developed a conditional generative adversarial network (cGAN) for synCT generation from low-field MRI in the brain. Methods and MaterialsSimulation MR-CT pairs from twelve glioma patients imaged with a head and neck surface coil and treated on a 0.35T MR-linac were prospectively included to train the model consisting of a 9-block residual network generator and a PatchGAN discriminator. Four-fold cross validation was implemented. SynCT was quantitatively evaluated against real CT using mean absolute error (MAE), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). Dose was calculated on synCT applying original treatment plan. Dosimetric performance was evaluated by dose-volume histogram (DVH) metric comparison and local three-dimensional gamma analysis. To demonstrate utilization in treatment adaptation, longitudinal synCTs were generated for qualitative evaluation, and one offline adaptation case underwent two comparative plan evaluations. Secondary validation was conducted with 9 patients on a different MR-linac using a high-resolution brain coil. ResultsOur model generated high-quality synCTs with MAE, PSNR and SSIM of 70.9±10.4 HU, 28.4±1.5 d.B. and 0.87±0.02 within the field-of-view, respectively. Underrepresented post-surgical anomalies challenged model performance. Nevertheless, excellent dosimetric agreement was observed with the mean difference between real and synCT DVH metrics of -0.07±0.29 Gy for target D95 and within [-0.14, 0.02] Gy for organs at risk. Significant differences were only observed in the right lens D0.01cc with negligible overall difference (<0.13 Gy). Mean gamma analysis pass rates were 92.2%±3.0%, 99.2%±0.7% and 99.9%±0.1% at 1%/1mm, 2%/2mm and 3%/3mm, respectively. Secondary validation yielded no significant differences in synCT performance for whole brain MAE, PSNR, and SSIM with comparable dosimetric results. ConclusionsOur cGAN model generated high-fidelity brain synCTs from low-field MRI with excellent dosimetric performance. Secondary validation suggests great promise of implementing synCTs to facilitate robust dose calculation for online adaptive brain MRgRT.