NMR, MRI & biomedical quantum sensing

Abstract The forensic sodium rhodizonate test is used to detect lead in gunshot debris. Areas of interest are sprayed with a sodium rhodizonate solution followed by a tartrate buffer solution of pH 2.8, producing a deep red compound indicative of lead and other metals. A confirmatory hydrochloric acid spray produces a violet color specific to lead. The associated chemical transformations of the insoluble lead rhodizonate complexes have not been specified. Lead rhodizonate, lead rhodizonate tartrate, and lead rhodizonate chloride coordination polymers were synthesized from dilute solutions of sodium rhodizonate and lead nitrate in water, tartrate buffer, and HCl buffer. Nanoparticles of the precipitates were analyzed by three-dimensional electron diffraction (3D ED). A crystal, Pb24+(C6O6)2–Cl22–·3H2O, was characterized in the space group R3̅m (a = 8.0708(3) Å, c = 16.576(3) Å), with rhodizonate layers bidentately coordinated to three lead ions, offset by ca. 0.05 Å from the rhodizonate planes. Solid-state nuclear magnetic resonance revealed the presence of a single axial lead site in the nitrate precipitate, a distribution of lead sites in the lead rhodizonate tartrate precipitate, and three distinct sites in the lead rhodizonate chloride precipitate, suggesting other Pb compounds in the bulk coexisting with the 3D ED product.

Modeling the electric field gradient (EFG) in molecular dynamics (MD) simulations enables the prediction of nuclear magnetic resonance (NMR) relaxation rates thus allowing to unravel the underlying relaxation-active molecular motion. Here, we compare the linear-response Sternheimer model with a novel machine-learning (ML) approach to simulate 14 N quadrupolar T 1 relaxation in the ionic liquid 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF 4]). Improving the parametrization of the Sternheimer model at the low-symmetry nitrogen sites required an electron density-based charge-partitioning scheme. In contrast, the parameter-free ML descriptor directly captured the EFG dynamics, providing a flexible methodology for computing EFG tensors in complex ionic systems. In the extreme-narrowing regime, both models predicted relaxation rates in excellent agreement with experimental measurements. Finally, using a regularized inverse Laplace transform, we deconvolute the dynamics of the EFG tensor and of the cation inertia tensor, and identify molecular rotation as the dominant mode responsible for 14 N quadrupolar relaxation.

INTRODUCTION: Prediction of cartilage structural properties through MRI could allow earlier detection of joint pathologies, such as osteoarthritis. MATERIALS & METHODS: Bovine patellar cartilage samples (n = 12) were imaged using magnetic resonance fingerprinting, followed by histological examination of proteoglycan content and collagen fiber anisotropy. The relaxation time maps and raw signal data were then used for training Gaussian process regression (GPR) models to predict the histology results. RESULTS: Proteoglycan content was predicted by the GPR models with high accuracy (median r = 0.81, σ = 0.08 and NRMSE = 11.7%). Predictions performed using raw MRF data outperformed those done using qMRI maps. Collagen fiber anisotropy predictions found only weak correlation (median r = 0.40, σ = 0.25 & NRMSE = 26.4%) and no significant difference was seen between models trained on raw MRF or relaxation time maps. DISCUSSION: These findings indicate that noninvasive prediction of proteoglycan content in cartilage from MRF measurements using a 3 T clinical scanner is feasible, holding promise for future clinical applications. Collagen fiber anisotropy could not be reliably estimated with the current setup. GPR-based prediction models were found to outperform reference linear models using the same prediction data.

PURPOSE: The goal of this work is to introduce an automated method to assess the quality of under-sampled MRI reconstructions. THEORY AND METHODS: We propose a general framework for pixel-wise uncertainty quantification in accelerated MRI reconstructions, enabling automatic identification of unreliable regions without using ground-truth fully-sampled reference images. Our method integrates conformal quantile regression with learning-based image reconstruction methods to estimate statistically rigorous pixel-wise uncertainty intervals. We trained and evaluated our model on Cartesian undersampled brain and knee data obtained from the fastMRI dataset using acceleration factors ranging from 2 to 10. An end-to-end Variational Network was used for image reconstruction. RESULTS: Quantitative experiments demonstrate strong agreement between predicted uncertainty maps and true reconstruction error. Using our method, the corresponding Pearson correlation coefficient was higher than 90% at acceleration levels at and above four-fold; whereas it dropped to less than 70% when the uncertainty was computed using a simpler heuristic notion (magnitude of the residual). Qualitative examples further show the uncertainty maps based on quantile regression capture the magnitude and spatial distribution of reconstruction errors across acceleration factors, with regions of elevated uncertainty aligning with pathologies and artifacts. CONCLUSION: The proposed framework enables evaluation of reconstruction quality without access to fully-sampled ground-truth reference images. It represents a step toward adaptive MRI acquisition protocols that may be able to dynamically balance scan time and diagnostic reliability.

The stabilization and functional integration of organic radicals within crystalline frameworks remain a significant challenge in materials chemistry, particularly for applications in optoelectronics and sensing. Herein, we report the design and synthesis of a radical-rich, polyrotaxanated covalent organic framework (VCB COF) featuring viologen-based radical cation dimers mechanically interlocked within cucurbit[8]uril (CB[8]) macrocycles. This supramolecular architecture gives rise to a double-walled framework that significantly enhances radical stability, photophysical properties, and photoresponsivity. Electron paramagnetic resonance (EPR) analysis reveals that VCB retains over 72% of its radical signal after 7 days of air exposure, compared to just 22% in its nonrotaxanated analogue, TpV COF. Photophysical studies reveal a 3-fold increase in photoluminescence intensity, while photoelectrochemical measurements demonstrate a stable photocurrent density of 1.3 μA cm-2 under 405 nm illumination with reversible on/off switching. Molecular dynamics simulations together with solid-state NMR support the localization of CB[8] at COF junctions and are consistent with the formation of a double-walled architecture stabilized by dipolar and long-range noncovalent interactions. This work establishes a generalizable strategy for integrating persistent radicals within COFs through macrocycle-assisted encapsulation and mechanical bonding, opening new avenues for the development of robust photoactive, redox-active, and spin-functional materials.

High Resolution Image Download MS PowerPoint Slide Ionic liquids are complex liquids characterized by high viscosity and high polarity. Their dynamics are of interest due to their use in many fields that require unconventional solvent environments. Here, we develop an MD-based forward-prediction framework to calculate NMR relaxation rates in the ionic liquid 1-butyl-3-methylimidazolium tetrafluoroborate, [bmim][BF 4], and investigate their temperature and magnetic-field dependence. We show that the combination of intra- and intermolecular dipolar couplings and chemical shift anisotropy interactions allows accounting for the experimentally observed nuclear spin lifetimes when considering multimodal relaxation processes. A regularized inverse Laplace transformation of MD-derived interactions reveals generally bimodal motional processes active in both inter- and intramolecular effects. The presence of two main motional processes may be somewhat surprising for intramolecular effects, especially in the symmetric [BF 4] − unit, but can be attributed to the restricted motion due to the local environment. We also highlight that the Bloembergen–Purcell–Pound (BPP) approach that is often used to characterize average motional correlation times is not reliable in most situations encountered with realistic systems. Consequently, we argue that achieving quantitative agreement between predicted and measured NMR relaxation rates offers a more robust route to extracting structural and dynamical information from complex liquids.

¹⁴N NMR is notoriously difficult to perform in liquids due to the very fast spin relaxation and the large quadrupolar couplings, which render many signals invisible. We show here how ¹⁴N nuclei of biomolecular constituents can be probed indirectly by reintroducing the scalar relaxation of the second kind contribution to the polarization lifetimes of J-coupled protons in double resonance spin-locking experiments. The enhanced ¹H relaxation rates in the rotating-frame allow for direct evaluation of nitrogen chemical shift and polarization lifetimes, from which one- and even two-bond ¹H-¹⁴N scalar couplings as well as ¹⁴N quadrupolar interactions can be determined. We demonstrate the versatility of this method by characterizing ¹H-¹⁴N spin pairs in several molecules of biological importance, showing proton relaxation enhancements beyond one order of magnitude. We further observe a pronounced effect from intermolecular hydrogen bonding. Our approach can be readily integrated into existing biomolecular NMR methodologies, as demonstrated here for ¹H-detected relaxation-editing experiments with water suppression. This method provides access to nitrogen's picosecond-modulated quadrupolar interaction via single-scan proton detection in systems that would otherwise yield almost no detectable direct ¹⁴N signal even after averaging over thousands of transients.

Purpose: Anatomical models with comprehensive tissue properties are needed for realistic MRI simulation, yet no validated method exists to generate such models automatically from patient scans. This work presents a pipeline for generating patient-specific numerical brain models suitable for physics-based MRI simulation. Methods: A simulation-ready head model was constructed from the IT’IS Duke body model by harmonizing tissue labels through an anatomy ontology to assign MRI relaxation and proton density values from the IT’IS tissue property database. A multi-metric deformable registration framework was applied to deform the Duke model into subject-specific geometry for 30 healthy adults (17 male, mean age 45.3 years, range 24.3–68.1 years) with 1.5 T scans from the IXI dataset. Agreement between simulated and acquired images was evaluated using SSIM, normalized cross-correlation (NCC), and normalized root mean square error (NRMSE). Results: Average SSIM was 0.734 (p = 1.3 × 10−6), NCC was 0.747 (p = 3.8 × 10−3), and NRMSE was 0.116 (p = 9.4 × 10−9). Conclusion: The proposed framework requires no segmentation of the target image and automatically produces patient-specific models with comprehensive atlas-derived tissue properties suitable for synthetic MRI generation and coil simulation.

High Resolution Image Download MS PowerPoint Slide Today’s fast-progressing water scarcity demands new energy-efficient water desalination technologies. Polyelectrolyte hydrogels are promising materials for this application as they can absorb water from saline solutions and reject salts. This process relies on the electrostatic imbalance between the charged groups in the hydrogel and the surrounding solution. However, current state-of-the-art methods for assessing desalination performance are primarily based on ionic conductivity measurements, which lack phase specificity, atomic-level insight, or spatial resolution, limiting research into the underlying microscopic mechanisms. We investigated salt-ion interactions in sodium polyacrylate hydrogels using 23 Na nuclear magnetic resonance (NMR) spectroscopy throughout the swelling process, which provides detailed microscopic information on the different compartments. 23 Na magnetic resonance imaging (MRI) is shown to map Na + ion distributions across different environments, while polarization lifetime measurements are shown to probe ion dynamic regimes. Relaxation-edited imaging revealed the effects of the temperature and competing salts on ion behavior across phases. Additionally, multiple-quantum-filtered experiments selectively detected and characterized slow-tumbling Na + ions, showing the presence of two dynamically distinct sodium pools. These findings highlight the power of integrated 23 Na NMR and MRI techniques for the in situ analysis of ion distribution and dynamics in hydrogels, providing a detailed tool for tuning and optimizing hydrogel desalination strategies.

Abstract [1- 13 C]pyruvate is the most widely used probe for hyperpolarized metabolic magnetic resonance imaging, with profound applications in tumor and inflammation diagnosis as well as treatment monitoring. The most fundamental hurdle to broader application, however, remains the rapid relaxation of polarization and the associated signal loss. Here, we report a method to address this challenge. Studying the nuclear spin relaxation dispersion of [1- 13 C]pyruvate across magnetic fields from 8 μT to 9.4 T, as a function of additives, solvents, and preparation methods, allows us to achieve T 1 relaxation times of up to four minutes. Such a long T 1 could enable reliable quality control and nearly polarization loss-free transport, further boosting the power of hyperpolarized metabolic MRI. The power of the optimized protocol for T 1 elongation in vitro is showcased by more than doubling the sensitivity in a metabolic experiment.

Identifying reliable, nondestructive indicators of electrolyte degradation is essential for advancing the diagnostics and lifetime assessment of lithium-ion batteries. In this study, we performed multinuclear nuclear magnetic resonance spectroscopy at various time points during systematic degradation of battery electrolyte components undergoing thermal cycling. We highlight a strong correlation between electrolyte degradation state and the 7Li nuclear spin polarization lifetime, while other nuclei did not respond as strongly. A mechanistic rationale for this phenomenon is provided using molecular dynamics simulations. The remarkable sensitivity of 7Li nuclear spin lifetime to electrolyte health underscores its potential value as a nondestructive, field-independent probe suited for diagnosing defects and evaluating the overall health of lithium-ion batteries.

Imaging of the human body at ultra-high fields (static magnetic fieldB0≥ 7Tesla) is challenging due to the radio-frequency field inhomogeneities in the human body tissues caused by the short wavelength. These effects could be partially mitigated using an array of antennas and by parallel transmission allowing for control of the radio-frequency field distribution in the region of interest. All commonly-used radio-frequency arrays for ultra-high field MRI consist of resonant elements: dipoles, TEM-resonators, loops and individual slots. All these elements rely on excitation in the reactive near-field region, in the sense that they are resonant devices that produce a field pattern with a fixed relative phase distribution between the currents in different parts of the structure along the commensurable conductor elements. In our work, we propose to use a previously demonstrated non-resonant leaky-wave approach to control the phase of currents in radio-frequency coil conductors to increase theB1+field in the center or in the region of interest. Using this approach, we developed a radio-frequency coil based on a leaky-wave antenna approach with optimized surface current distribution resulting in strongerB1+in the desired region compared to e.g., a fractionated dipole.

BACKGROUND: Magnetic Resonance Fingerprinting (MRF) enables rapid quantitative parameter mapping from which synthetic clinical contrast images can be derived using deep learning (DL). PURPOSE: This study evaluates the reliability and interchangeability of MRF-derived synthetic knee MRI relative to conventional MRI in patients with osteoarthritis. STUDY TYPE: Prospective single-center comparative study. SUBJECTS: Between March 2022 and 2023, 78 participants (54 females, mean age 57.2, range 33-78 years) with knee osteoarthritis. FIELD STRENGTH/SEQUENCE: 3.0 T; proton density weighted (PDw) imaging, T2-weighted fat-saturated (T2w fs) imaging, and MRF. ASSESSMENT: U-Nets were trained to produce synthetic contrasts from MRF data. Three musculoskeletal radiologists performed MRI OsteoArthritis Knee Score (MOAKS) assessments and image quality evaluation using a Likert scale (1-5). STATISTICAL TESTS: The inter-rater and inter-method reliability were evaluated using prevalence-and-bias-adjusted kappa (PABAK), and percentages of exact matches. Image quality scores were compared using the Wilcoxon test. The limit of statistical significance was set at p < 0.05 and no multiple comparisons corrections were applied. RESULTS: The inter-rater reliability for synthetic MR images varied between 0.980-0.994 (CI 0.975-0.997; exact matches 77.%-89.2%) and for conventional MR images between 0.979 and 0.994 (CI 0.973-0.997; exact matches 75.3%-89.5%). Inter-method reliability between synthetic and conventional MR images was near-perfect: mean PABAK-values and exact matches were 0.927 and 77.9% for cartilage, 0.915 and 89.7% for bone marrow lesions, 0.922 and 65.2% for osteophytes, 0.950 and 72.9% for meniscus pathology, 0.934 and 66.6% for effusion, and 0.857 and 92.5% for Baker's cyst. Average Likert scores were significantly better for conventional than synthetic images: 4.5 vs. 3.9 for PDw and 4.1 vs. 3.2 for T2w fs. DATA CONCLUSION: MRF-derived DL-based synthetic clinical contrasts provide excellent inter-rater reliability and interchangeability against conventional MR sequences in knee OA; however, image quality needs further development. EVIDENCE LEVEL: 2. STAGE OF TECHNICAL EFFICACY: 2.

Enhanced Intersystem Crossing (EISC) is an important mechanism that allows for formally forbidden population transfer from the singlet to triplet manifold in chromophore-radical (C-R) systems. We use first order perturbation theory to estimate the likelihood of EISC in various organic C-R molecules. The first order mixing coefficient κ between the states involved in EISC depends on the difference in pairwise exchange interactions between photoexcited chromophore electrons and the radical. Exchange coupling constants were calculated with the Heisenberg-Dirac-Van Vleck Hamiltonian using excited state wave functions and energies obtained from the CASSCF/QD-NEVPT2 calculations. The predictions derived using this framework are in a good agreement with the available experimental data on EISC observed with transient absorption spectroscopy.

Electron spin plays a critical role in determining the structure, dynamics, and reactivities of molecular excited states, including multiexciton processes such as singlet fission. These systems exhibit triplet pair states whose excited state dynamics can be widely tuned through molecular engineering. For example, the electronic coupling between covalently linked chromophores can be readily modulated using chemical bridges to control proximity, quantum interference, or resonance effects. However, less is known about how spin coupling interactions are impacted by chromophore architecture, and how this influences triplet pair recombination dynamics. Here, we investigate the role of bridge connectivity and chromophore identity in modulating interchromophore exchange and dipolar coupling interactions for a series of pentacene and tetracene dimers bridged by alternant hydrocarbons (phenylene, naphthalene, anthracene). Using both time-resolved electron paramagnetic resonance and transient absorption spectroscopy, we find that the boundedness and recombination pathways of the triplet pair spins are highly sensitive to molecular architecture and chromophore-specific magnetic dipolar interactions. Notably, nominally ferromagnetic and antiferromagnetic eigenstates result in distinct spin state orderings, consistent with predictions from quantum interference-based graphical models. These findings establish new design principles for tuning spin dynamics in iSF materials, with implications for photonic and quantum information applications.

High Resolution Image Download MS PowerPoint Slide Ytterbium doped cesium lead halide materials exhibit a property known as quantum cutting which allows for greater than 100% photoluminescent quantum yields (PLQYs). The local atomic structure of the defects responsible for these properties and the effectiveness of the doping for producing the desired PLQYs is not readily discerned using techniques requiring long-range order. In this work we prepared 2.5, 5, 10, and 20% Yb 3+ doped CsPbCl 3 powders using mechanosynthesis under distinct stoichiometric ratio conditions and characterized the defect incorporation and its effects on local atomic disorder using solid-state nuclear magnetic resonance (SSNMR) spectroscopy. We then correlate our observations to the observed PLQYs for each of the prepared samples. All samples prepared were found to be in an orthorhombic phase and no lattice shrinking was observed upon increased Yb 3+ doping. An increase in doping concentrations was accompanied by a decrease in 133 Cs NMR spin–lattice relaxation times T 1 consistent with a paramagnetic relaxation enhancement effect induced by Yb 3+ incorporation into the perovskite lattice. Through a comparison of synthesis methods, PLQY and NMR T 1 parameters we found that incorporated defects favorable for PLQY in mechanosynthesized samples are more likely to form in the presence of excess lead and excess chloride ions. The maximum PLQY values obtained for each set of samples correlated with T 1 parameters in the range of 13 to 35 s. In addition, we found that the observed PLQY in 5% doped samples was optimized after 1 to 2 h of interval grinding in stainless steel jars. Further grinding beyond 2 h led to a reduction in particle size below 1 μm as well as a reduction in PLQY and spin relaxation times.

Concentrated hydrogen-bonded electrolytes (CoHBEs) are structured, electrochemically stable, less-volatile alternatives to aqueous and dilute nonaqueous electrolytes, however, with high viscosities that limit molecular diffusion. This work provides an understanding of the proton conduction mechanism in CoHBEs based on mixtures of acids and azoles and establishes a link between the structurally dictated transport properties and the proton-coupled electron transfer (PCET) reaction rates that can be leveraged for enhancing electrochemical reactions. Diffusion and relaxation NMR studies suggest a breaking of the viscosity–conductivity tradeoff, where at high azole concentrations (>45 mol%), Grotthuss transport is more likely with lowered proton transfer energy barriers between the azole and the acid according to the machine learning (ML) accelerated ab initio path integral MD (AI-PIMD) simulations. Proton conduction pathways are found to be switchable between the hydrogen bonding networks of the acid and the azole, with imidazole chain forming structures better facilitating Grotthuss hopping. Supported by small-angle neutron scattering studies, the chains are found to have six member molecules on average with maximum of 3 to 4 imidazole/imidazoliums at 50 to 60 mol%. Despite their high viscosities, the measured PCET rates for quinones and phenazines measured in the protic CoHBEs present relatively high electron transfer rate constants (k 0 ~ 10 − 4 cm/s), validated by rotating disc electrode and scanning electrochemical microscopy measurements. The results demonstrate that strategic tuning of hydrogen-bond donor–acceptor interactions enables the decoupling of proton transport and viscosity, thereby impacting PCET reactions.

Accurate modeling of radiofrequency (RF) coils is essential for reliable electromagnetic (EM) field simulations in magnetic resonance imaging (MRI). This study evaluates the numerical accuracy of higher-order surface basis functions for RF coil simulation within the surface-volume integral equation (SVIE) framework. Graglia-Wilton-Peterson (GWP) basis functions and quadratic curvilinear triangular discretization elements replace the conventional Rao-Wilton-Glisson (RWG) basis functions and flat triangular elements used to approximate the coil currents. SVIE simulations show that the solver based on GWP achieves up to two orders of magnitude lower error in the electric field distribution within the coil load for coils without open edges, while no significant performance improvements were observed for coils with open edges. These results demonstrate that higher-order formulations are needed for accurate RF coil modeling in certain scenarios.

H NMR spectroscopies. Scanning electron microscopy and energy dispersive X-ray spectroscopy demonstrate the reversibility of these changes, with crystal morphology and surface composition nearly returning to near-pristine single crystals of BP4VA after annealing. The recoverability of single crystals enables a promising platform for the detection of volatile organic compounds.

Ultralow-field nuclear magnetic resonance has the power to see chemistry through metallic enclosures—enabling nondestructive quantitative tracking of the state and composition of liquid-state electrolytes in batteries.

ABSTRACT Optically active tetrathiafulvalene (TTF) banded spherulite films comprising helicoidal fibrils exhibit an order of magnitude higher hole mobility compared to their unbanded counterparts. These mobilities of ∼10 −5 cm 2 V −1 s −1 are, however, low among organic semiconductors. We enhance the electrical conductivity of TTF banded spherulite films over seven orders of magnitude by iodine doping. Pale yellow films became dark red as TTF + formed in the presence of iodine vapor, with the local doping rate dependent on the crystal face exposed at the film surface. A six‐order‐of‐magnitude increase in conductivity is observed during the first two min of doping, reaching a maximum value of 0.7 ± 0.6 S cm −1 after 10 min. TTF 7 I 5 needles nucleated at the film surface and grew downwards during iodine vapor exposure, consuming β‐TTF helicoidal fibrils. The four principal linear optical anisotropies, linear and circular birefringence and dichroism, are mapped, and all four properties decreased over 10 min of iodine vapor exposure due to conversion to TTF 7 I 5. For short doping times, however, circular birefringence and dichroism remained unchanged. These results demonstrate that the conductivity of TTF banded spherulite films can be increased without disrupting the microstructure that gives rise to optical activity, expanding their utility in chiroptoelectronics.

Multi-target biosensing platforms require immobilization of sensing probes on surfaces with nanoscale precision. A critical challenge is preventing unwanted molecular transfer (cross-pollination) between different probe patterns, which compromises measurement accuracy. This study investigates cross-pollination in multiplexed biosensing arrays fabricated using thermal scanning probe lithography (tSPL), a method that offers sub-10 nm spatial resolution. By systematically optimizing both the tSPL patterning parameters and chemical functionalization protocols, particularly biomolecule incubation times, this work demonstrates the ability to reduce cross-pollination to undetectable levels (0%). These experimental results establish a clear relationship between processing conditions and cross-contamination rates, providing a practical framework for reliable nanoscale patterning of multiple biomolecular probes on a single surface. These findings enable the development of high-fidelity, multi-target biosensing platforms with enhanced specificity, and therefore sensitivity, for applications in medical diagnostics, environmental monitoring, and biological research.

Femoroacetabular impingement (FAI) is caused by abnormal contact between the femur and acetabulum, resulting in pain, limited motion, and early osteoarthritis. Existing imaging techniques for diagnosing FAI face considerable challenges. Radiomics involves the quantitative extraction and analysis of imaging features using advanced algorithms, often combined with machine learning (ML), to enhance diagnostic and prognostic precision. When integrated with ML, radiomics can identify patterns beyond conventional imaging measurements, potentially enabling automated, precise, and reproducible assessment of hip morphology and pathology. Early studies demonstrate its potential to differentiate between normal, symptomatic, and asymptomatic cam-type hips. However, challenges persist, including the standardization of imaging protocols, feature selection, access to large datasets, and the explainability of models. This review summarizes the state of the art in radiomics for FAI and highlights its future applications.

We demonstrate that thermally stable perpendicular magnetic tunnel junctions (pMTJs), widely used in spin-transfer torque magnetic random-access memory, can be actuated with nanosecond pulses to exhibit tunable stochastic behavior. This actuated-stochastic tunnel junction (A-sMTJ) concept produces random telegraph noise, with control over fluctuation rate and probability bias. The device response is shown to be consistent with a Poisson process, with fluctuation rates tunable over more than two orders of magnitude, with average state dwell times varying from 29 ns to greater than 2.3 microseconds. These results establish A-sMTJs as a versatile platform for integrating deterministic, stochastic, and in-memory functionality on a single chip, advancing the development of probabilistic, neuromorphic, and unconventional computing systems.

PURPOSE: To introduce mtrk, a new open-source tool based on modern software-engineering principles that simplifies pulse-sequence design, implementation, and dissemination. METHODS: The mtrk framework is vendor-agnostic and relies on a compact and human-readable descriptive language. Users can design pulse sequences using either a Python-based programming interface or an intuitive graphical interface. The graphical interface also allows for visualizing pulse-sequence diagrams. A driver sequence was developed to run mtrk sequences on MR scanners. A spin-echo sequence was designed with mtrk and converted to Pulseq for comparison. Both versions were compared to an equivalent vendor sequence in phantom and in vivo experiments. RESULTS: Images from the mtrk and Pulseq versions were nearly identical and showed over 90% similarity compared to the vendor sequence, despite minor unavoidable design differences. Phantom images matched corresponding synthetic images simulated using the same pulse sequences. CONCLUSION: The mtrk framework simplifies the development of pulse sequences by providing an intuitive descriptive language and compatibility with the Pulseq format. Users can design and simulate pulse sequences using the graphical interface without any programming experience.

electrical tissue properties | phantom fabrication | phantom guidelines MR-based Electrical Properties (EPs) Tomography (MR-EPT) denotes non-invasive electrical conductivity (σ e) and permittivity (ε r) mapping methods using MR measurements.The lack of standardized tissue-mimicking phantoms hinders reproducibility studies and method comparisons.For example, the NIST/ISMRM MRI system phantom has contributed significantly to standardization efforts in relaxometry, underscoring the importance of having similar benchmarks for MR-EPT.The guidelines presented herein outline the importance of MR-EPT phantom design and construction, focusing on structure, composition, and reliability.

The radiative characteristics of the radiofrequency receive coils dictate the signal-to-noise ratio (SNR) of magnetic resonance images. Despite the crucial importance of RF coils, the practical coil design process has remained a largely empirical one. This work introduces a novel optimization framework for rational coil design, which relies on a fully automated pipeline that combines rapid electromagnetic simulations, shape optimization and coil meshing. The objective function iteratively maximizes SNR performance in a target region of interest with respect to the ultimate intrinsic SNR, which is the theoretically highest SNR independent from any particular coil design. The forward simulation employs a fast electromagnetic solver based on coupled surface and volume integral equations. The coils are represented as B-spline curves with an associated width, and automatically meshed for EM simulation. We implemented a new method to tune and decouple coils at each iteration without manual user intervention. The algorithm optimizes the size and position of a given number of coils with a combination of grid search and a line search. We demonstrated the framework by designing receive arrays of increasing complexity that yield optimal SNR for different target regions inside a numerical head model. SNR simulation time ranged from 15 s for a 3-coil configuration to 32 s for a 12-coil array, constrained to a helmet-like surface, including tuning and decoupling. The optimized 12-coil geometry yielded 9% higher average SNR performance in the brain at 3 T. This work represents the first automated coil optimization framework that uses full-wave electromagnetic simulations and ultimate performance benchmarks. This novel approach enables the systematic design of coils for magnetic resonance imaging with significantly improved SNR performance, potentially transforming coil development from empirical design to physics-driven optimization.

Background: Femoroacetabular impingement (FAI) is a complex hip disorder characterized by abnormal contact between the femoral head and acetabulum, often leading to joint damage, chronic pain, and early-onset osteoarthritis. Despite MRI being the imaging modality of choice, diagnosis remains challenging due to subjective interpretation, lack of standardized imaging criteria, and difficulty differentiating symptomatic from asymptomatic cases. This study aimed to develop and externally validate radiomics-based machine learning (ML) models capable of classifying healthy, asymptomatic, and symptomatic FAI cases with high diagnostic accuracy and generalizability. Methods: A total of 82 hip MRI datasets (31 symptomatic, 31 asymptomatic, 20 healthy) from a single center were used for training and cross-validation. Radiomic features were extracted from four segmented anatomical regions (femur, acetabulum, gluteus medius, gluteus maximus). A four-step feature selection pipeline was implemented, followed by training 16 ML classifiers. External validation was conducted on a separate multi-center cohort of 185 symptomatic FAI cases acquired with heterogeneous MRI protocols. Results: The best-performing models achieved a cross-validation accuracy of up to 90.9% in distinguishing among healthy, asymptomatic, and symptomatic hips. External validation on the independent multi-center cohort demonstrated 100% accuracy in identifying symptomatic FAI cases. Since this metric reflects performance on symptomatic cases only, it should be interpreted as a detection rate (true positive rate) rather than overall multi-class accuracy. Gini index-based feature selection consistently outperformed F-statistic-based methods across all the models. Conclusions: This is the first study to systematically integrate radiomics and multiple ML models for FAI classification for these three phenotypes, trained on a single-center dataset and externally validated on multi-institutional MRI data. The demonstrated robustness and generalizability of radiomic features support their use in clinical workflows and future large-scale studies targeting standardized, data-driven FAI diagnosis.

OBJECTIVE: Global Maxwell Tomography (GMT) is a noninvasive inverse optimization method for the estimation of electrical properties (EP) from magnetic resonance (MR) measurements. GMT uses the volume integral equation (VIE) in the forward problem and assumes that the sample has negligible effect on the coil currents. Consequently, GMT calculates the coil's incident fields with an initial EP distribution and keeps them constant for all optimization iterations. This can lead to erroneous reconstructions. This work introduces a novel version of GMT that replaces VIE with the volume-surface integral equation (VSIE), which recalculates the coil currents at every iteration based on updated EP estimates before computing the associated fields. METHODS: We simulated an 8-channel transceiver coil array for 7 T brain imaging and reconstructed the EP of a realistic head model using VSIE-based GMT. We built the coil, collected experimental MR measurements, and reconstructed EP of a two-compartment phantom. RESULTS: In simulations, VSIE-based GMT outperformed VIE-based GMT by at least 12% for both EP. In experiments, the relative difference with respect to probe-measured EP values in the inner (outer) compartment was 13% (26%) and 17% (33%) for the permittivity and conductivity, respectively. CONCLUSION: The use of VSIE over VIE enhances GMT's performance by accounting for the effect of the EP on the coil currents. SIGNIFICANCE: VSIE-based GMT does not rely on an initial EP estimate, rendering it more suitable for experimental reconstructions compared to the VIE-based GMT.

Accurate modeling of the dynamic structures of ribonucleic acid (RNA) molecules is essential for understanding their biological roles. However, such modeling remains challenging due to limitations in current force fields. This study critically evaluates three RNA force fields, HB-CUFIX, AMBER-χOL3, and AMBER-ROC, comparing their performance against experimental nuclear magnetic resonance and small-angle x-ray scattering data for single-stranded oligonucleotides. Using enhanced sampling techniques, specifically Unified Free Energy Dynamics, we exhaustively sampled the conformational space of tetramer and hexamer RNA sequences, achieving a detailed and thermodynamically converged view of their structural dynamics. Our findings reveal that HB-CUFIX outperforms AMBER-χOL3 and AMBER-ROC, providing near-experimental accuracy in capturing sequence-dependent structural preferences. In particular, HB-CUFIX accurately predicts low energy states for the AAAA and CCCC sequences, favoring A-form helical conformations, while the UUUU sequence adopts an extended, heterogeneous structure. The mixed GACC sequence displays a predominantly A-form helix with flexible terminal residues. These results highlight the significant role of sequence in dictating RNA conformational spaces, which are driven by base stacking interactions and covalent geometry. We also emphasize the importance of enhanced sampling, particularly methods that can handle large numbers of collective variables, in evaluating RNA force fields, as traditional brute-force Molecular dynamics fails to capture the conformational diversity of flexible RNAs. Our study provides a reliable tool for RNA structure prediction and dynamic analysis, supporting future advancements in RNA-targeted research and therapeutic design.

Radiation damping is a well-known phenomenon in the context of NMR spectroscopy. Several strategies exist for minimizing the effects of radiation damping, which have mostly been focused on limiting the effects of one resonance (frequently water). When samples with many resonances are examined at high concentration, such approaches often cannot be used. One category of systems where the broadband nature of radiation damping leads to complications are deep eutectic solvents (DESs). DESs are considered innovative solvent systems that have several advantages such as tunability and environmental friendliness, and enabling important applications ranging from catalysis to drug delivery. It is of interest to examine the intermolecular effects via NMR spectroscopy in these systems. Here we show that broadband radiation effects are very strong in these systems and identify simple strategies specifically for 2D NOESY NMR spectroscopy to regain the ability to quantify intermolecular interactions in these systems.

High Resolution Image Download MS PowerPoint Slide Ferrimagnetic insulators with ultralow damping are of great interest for their potential applications in energy-efficient computing devices. Here, we report the direct electrical detection of magnetic auto-oscillations in unpatterned ultralow damping ferrimagnetic insulator epitaxial Li 0.5 Al 0.5 Fe 2 O 4 thin films, driven by a current in a proximal Pt nanowire. Auto-oscillations occur for only one current polarity, consistent with the spin-Hall effect inducing the oscillation state. Micromagnetic modeling shows good agreement with the experimental frequency and field dispersions, showing only one dominant oscillation mode, in contrast to the multiple modes typically observed in transition-metal nanowire-type spin-Hall nanooscillators. This study illustrates a new material system for neuromorphic computing and magnonics, a simple material platform with the direct-current generation of high-frequency (∼10 GHz) signals and their electrical detection.

OBJECTIVES: Magnetization-prepared rapid gradient-echo (MP-RAGE) sequences are routinely acquired for brain exams, providing high conspicuity for enhancing lesions. Vessels, however, also appear bright, which can complicate the detection of small lesions. T1RESS (T1 relaxation-enhanced steady-state) sequences have been proposed as an alternative to MP-RAGE, offering improved lesion conspicuity and suppression of blood vessels. This work aims to evaluate the performance of radial T1RESS variants for motion-robust contrast-enhanced brain MRI. MATERIALS AND METHODS: Radial stack-of-stars sampling was implemented for steady-state free-precession-based rapid T1RESS acquisition with saturation recovery preparation. Three variants were developed using a balanced steady-state free-precession readout (bT1RESS), an unbalanced fast imaging steady precession (FISP) readout (uT1RESS-FISP), and an unbalanced reversed FISP readout (uT1RESS-PSIF). Image contrast was evaluated in numerical simulations and phantom experiments. The motion robustness of radial T1RESS was demonstrated with a motion phantom. Four patients and six healthy volunteers were scanned at 3 T and 0.55 T. Extensions were developed combining T1RESS with GRASP for dynamic imaging, with GRAPPA for accelerated scans, and with Dixon for fat/water separation. RESULTS: In simulations and phantom scans, uT1RESS-FISP provided higher signal intensity for regions with lower T1 values (<500 ms) compared with MP-RAGE. In motion experiments, radial uT1RESS-FISP showed fewer artifacts than MP-RAGE and Cartesian uT1RESS-FISP. In patients, both unbalanced uT1RESS variants provided higher lesion conspicuity than MP-RAGE. Blood vessels appeared bright with MP-RAGE, gray with uT1RESS-FISP, and dark with uT1RESS-PSIF. At 0.55 T, bT1RESS provided high signal-to-noise ratio T1-weighted images without banding artifacts. Lastly, dynamic T1RESS images with a temporal resolution of 10.14 seconds/frame were generated using the GRASP algorithm. CONCLUSIONS: Radial T1RESS sequences offer improved lesion conspicuity and motion robustness and enable dynamic imaging for contrast-enhanced brain MRI. Both uT1RESS variants showed higher tumor-to-brain contrast than MP-RAGE and may find application as alternative techniques for imaging uncooperative patients with small brain lesions.

Diagnosing femoroacetabular impingement (FAI) using manual radiographic, CT and MRI analysis is user-dependent and has only moderate accuracy. We leverage radiomics and machine learning techniques—proven successful in oncology—to automatically extract quantitative MRI features beyond the human eye: first-order statistics, shape-based metrics, and texture matrices. We aimed to differentiate healthy, symptomatic, and asymptomatic hips; measuring reliability and generalisability through external validation. We investigated femoroacetabular impingement (FAI) using three distinct cohorts: 1) a primary cohort of 31 FAI patients (22F, mean age 36) imaged with/without contrast on 3T MRI using four-phase Dixon sequences; 2) healthy volunteers (20 patients); 3) cohort of asymptomatic FAI (31 patients); compared with an external validation cohort of 185 monolateral symptomatic FAI patients’ water-only Dixon images. Automated segmentation (Total Segmentator and STAPLE algorithm) isolated key hip structures. PyRadiomics extracted 6,752 radiomic features across ROIs after 1mm isotropic resampling. Ten machine learning models were evaluated using four-fold cross-validation, maintaining patient-level data separation between training/validation/testing sets. This yielded 480 unique models (10 classifiers × 48 feature sets), with top performers validated against the test set (three-label classification) and cohort 3 (symptomatic identification). Seven out of ten models achieved accuracy >0.85. Notably, Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), Extra Trees Classifier (ETC), and Bagging Classifier (BC) attained perfect accuracy at least once using fewer than 20 features. In the external validation cohort, eight models also surpassed 0.85 accuracy, including five methods—Support Vector Classifier (SVC), Decision Tree Classifier (DTC), Gaussian Naive Bayes (GNB), Quadratic Discriminant Analysis (QDA), and K-Nearest Neighbours (KNN)— with perfect accuracy though with increased variability. This study highlights the effectiveness of 20 novel MRI-based radiomic features to reliably differentiate between symptomatic FAI, asymptomatic FAI, and healthy hips. Decision Tree Classifier and Bagging Classifier predicted correctly 100% of the FAI patients even with varied imaging protocols. Additionally, automated radiomic selection identified novel gluteal muscle features specific to symptomatic FAI, improving the diagnostic yield of MRI beyond bone, cartilage and labrum in FAI.

BACKGROUND: Upfront combination chemoradiotherapy (CRT) represents the standard of care for patients affected by stage III squamous cell carcinoma (SCC) of the anal canal, achieving satisfactory results both in terms of overall survival and local disease control. However, a non-negligible fraction of patients obtain incomplete responses, highlighting the need for innovative prognostic tools. We report the preliminary results of a customized radiomic algorithm designed to predict tumor response to CRT in patients affected by SCC of the anal canal. METHODS: We manually annotated pretreatment T2-weighted turbo spin-echo images of 26 consecutive patients with stage III SCC of the anal canal treated with CRT at our institution from 2012 to 2022. Each patient was classified as complete response (CR, 17 patients), or non-complete response (non-CR, 9 patients) based on the absence or presence of residual disease at imaging and endoscopy after treatment. A total of 132 three-dimensional radiomic features were extracted for each patient and fed to a dedicated machine-learning classifier. RESULTS: Models trained with gray-level co-occurrence matrix features achieved the best performances (accuracy 0.846 ± 0.064, sensitivity 0.900 ± 0.122, specificity 0.833 ± 0.175, area under receiver operating characteristics curve 0.867 ± 0.055), highlighting a more homogeneous distribution of voxel intensities and lower spatial complexity in non-CR patients. CONCLUSION: Our radiomic tool accurately predicted tumor response to CRT in patients with stage III SCC of the anal canal, highlighting a more homogeneous tissue composition in poor responders. RELEVANCE STATEMENT: The more homogeneous radiomic texture observed in non-CR patients may be imputable to a dominant neoplastic clone with a relatively low mitotic index (therefore, limited tissue necrosis), intrinsically more resistant to CRT than faster-proliferating tumors. KEY POINT: A non-negligible fraction of patients with anal SCC respond unsatisfactorily to CRT. Our radiomic model predicted response to CRT based on pretreatment MRI. We observed a more homogeneous tissue composition in poor responders. The slow proliferation of a dominant clone may explain non-CR to CRT.

Phosphorus is a critical nutrient affecting primary productivity across all ecosystems. Many regions worldwide are limited or co-limited by phosphorus availability, which can be alleviated through atmospheric deposition. Dust is known to be a key external source of phosphorus in ecosystems, assumed to be in the form of various insoluble inorganic minerals. We show that this view is largely incomplete and here we present conclusive evidence, that organic phosphorus as diesters, primarily associated with biological materials. Phosphate diesters significantly correlated with soil bacteria found in dust, implying a direct link with microbial soil communities, without excluding the eukaryotic cells. Phosphate diesters in dust, along with abundant alkaline phosphatase, may contribute 70% to daily primary productivity in the eastern Mediterranean, highlighting the potential of organic phosphorus substrates present in dust as airborne microorganisms to impact the biogeochemistry of oligotrophic environments via atmospheric deposition.

Magnetic tunnel junctions (MTJs) are promising building blocks for energy-efficient computing in neuromorphic and optimization applications, but implementing logic functions in coupled MTJs remains a significant challenge. This study offers an approach to building a universal reversible Toffoli gate using interacting macrospins---representing MTJ free layers---evolving under Landau-Lifshitz-Gilbert dynamics. With tuning and thermal annealing, the system reliably converges on correct logic outputs, revealing strategies for embedding logic into magnetic hardware. This approach could enable more complex spintronic computational architectures founded on MTJ-based Boltzmann machines.

Differential equations (DEs) serve as fundamental tools in mathematical modeling across scientific disciplines, yet classical numerical solvers face limitations with large-scale or computationally intensive problems. This study explores a quantum-inspired approach to solving DEs, combining quantum- inspired techniques with classical methods. It focuses on fixed- point arithmetic on quantum circuits, utilizing basic quantum gates to manipulate DE solutions. We expand upon the techniques introduced by Zanger et al. [Quantum, 5, 502 (2021)] by offering a precise computation for a fixed-point signed multiplication scheme, while also presenting a quantum circuit capable of executing the fixed-point division algorithm. We demonstrate the feasibility of our approach through the simulation of a linear Ordinary Differential Equation (ODE), where initial conditions and parameters are encoded into quantum circuits using fixed- point representation. By executing sequences of quantum gates mimicking numerical integration steps, we obtain approximate solutions to the ODE with specified fixed-point precision.

Abstract Purpose To develop and characterize a 128‐channel head array for brain imaging at 10.5 T, evaluate signal‐to‐noise ratio (SNR) relative to ultimate intrinsic SNR (uiSNR) and lower field strengths, and demonstrate human brain anatomical and functional imaging with this unique magnetic field and high‐channel‐count array. Methods The coil consists of a 16‐channel self‐decoupled loop transmit/receive (16Tx/Rx) array with a 112‐loop receive‐only (Rx) insert. Interactions between the 16Tx/Rx array and the 112Rx insert were mitigated using coaxial cable traps placed every 1/16 of a wavelength on each feed cable, locating most preamplifier boards outside the transmitter field, and miniaturizing those placed directly on individual coils. Results The effect of the 112Rx insert on the circumscribing 16Tx/Rx array was minimized, leading to similar transmit field maps obtained experimentally with and without the 112Rx array in place and by electromagnetic simulations of the 16Tx/Rx array alone. The 128‐channel array captured 77% of uiSNR centrally. Significantly higher 1/g‐factor values across the whole brain was achieved compared with 7 T. Excellent SNR, high parallel‐imaging performance, and minimal Tx‐Rx interactions collectively facilitated acquisition of high‐quality, high‐resolution, proof‐of‐concept functional and anatomical images, including with power‐demanding sequences in the human brain. Conclusions Counterintuitive to expectations based on magnetic fields less than or equal to 7 T, the higher channel counts provided SNR gains centrally, capturing about 80% uiSNR. The fraction of uiSNR achieved centrally in 64Rx, 80Rx, and 128Rx arrays suggested that a plateau was being reached at 80%. At this plateau, B 0 ‐dependent SNR gains for 10.5 T relative to 7 T were approximately linear to quadratic for the periphery and the center, respectively.

Structural, thermal, and dynamic properties of four deep eutectic solvents comprising choline chloride paired with ortho- phenolic derivative hydrogen-bond donors were probed using experiments and molecular simulations. The hydrogen-bond donors include phenol, catechol, o-chlorophenol, and o-cresol, in a 3:1 mixture with the hydrogen-bond acceptor choline chloride. Density, viscosity, and pulsed-field gradient NMR diffusivity measurements were conducted over a range of temperatures. Classical and ab initio molecular dynamics simulation results match experimental data reasonably well. The simulation results were then used to perform a more detailed analysis of the local structure and dynamics of these systems.

Room-temperature ionic liquids (RTIL) represent a versatile class of chemical systems, comprised only of oppositely charged species, whose bulk properties can be fine-tuned by adjusting molecular structures and, consequently, intermolecular interactions. Understanding the intricate dynamics between the two ionic species can aid the rational design of RTIL as electrolytes facilitating electron- or proton-transfer reactions useful for energy applications. Here, we investigate the temperature dependency of the intermolecular interactions via magnetization transfer between oppositely charged ions by means of 1H-19F heteronuclear Overhauser effect spectroscopy (HOESY) for two ionic liquids, namely [BMIM][BF4] and [BMIM][PF6]. We find that cross-relaxation rates vary significantly over a rather small temperature range, crossing the null point and changing sign. Other NMR observables such as chemical shifts, scalar couplings or auto-relaxation rates are less affected by temperature, making intermolecular Overhauser transfer a sensitive tool to study the dynamics and especially intermolecular interactions in RTILs. Molecular Dynamics (MD) simulations on neat RTILs systems were performed to identify the origin of the observed behavior. Good agreement between simulated and experimental cross-relaxation rates was found at different temperatures. On this basis, we could attribute the observed temperature behavior to the modulation of dynamic properties rather than coordination changes of RTILs. Specifically, it is the change in relative diffusion coefficients that describes the temperature behavior of internuclear interactions, rather than the distance of closest approach. This information underscores the versatility and high thermal stability of ionic liquids.

We introduce a novel protocol, which enables Heisenberg-limited quantum-enhanced sensing using the dynamics of any interacting many-body Hamiltonian. Our approach - dubbed butterfly metrology - utilizes a single application of forward and reverse time evolution to produce a coherent superposition of a "scrambled" and "unscrambled" quantum state. In this way, we create metrologically-useful long-range entanglement from generic local quantum interactions. The sensitivity of butterfly metrology is given by a sum of local out-of-time-order correlators (OTOCs) - the prototypical diagnostic of quantum information scrambling. Our approach broadens the landscape of platforms capable of performing quantum-enhanced metrology; as an example, we provide detailed blueprints and numerical studies demonstrating a route to scalable quantum-enhanced sensing in ensembles of solid-state spin defects.

Spin-Hall nano-oscillators (SHNOs) are nanoscale spintronic devices that generate high-frequency (GHz) microwave signals useful for various applications, such as neuromorphic computing and creating Ising systems. Recent research demonstrated that hybrid SHNOs consisting of a ferromagnetic metal (permalloy) and lithium ferrite-based (LAFO) insulating ferrimagnetic thin films have advantages in having lower auto-oscillation threshold currents (Ith) and generating larger microwave output power, making this hybrid structure an attractive candidate for spintronic applications. It is essential to understand how the tunable material properties of LAFO, e.g., its thickness, perpendicular magnetic anisotropy (Ku,LAFO), and saturation magnetization (Ms,LAFO), affect magnetic dynamics in hybrid SHNOs. We investigate the change in Ith and the output power of the device as the LAFO parameters vary. We find the Ith does not depend strongly on these parameters, but the output power has a highly nonlinear dependence on Ms,LAFO and Ku,LAFO. We further investigate the nature of the excited spin-wave modes as a function of Ku,LAFO and determine a critical value of Ku,LAFO above which propagating spin-waves are excited. Our simulation results provide a roadmap for designing hybrid SHNOs to achieve targeted spin excitation characteristics.

ABSTRACT Purpose To develop and characterize the performance of a 128-channel head array for brain imaging at 10.5 tesla and evaluate the potential of brain imaging at this unique, >10 tesla magnetic field. Methods The coil is composed of a 16-channel self-decoupled loop transmit/receive array with a 112-loop receive-only (Rx) insert. Interactions between the outer transmitter and the inner 112Rx insert were mitigated using coaxial cable traps placed every 1/16 of a wavelength on each feed cable, locating most preamplifier boards outside the transmitter field and miniaturizing those placed directly on individual coils. Results The 128-channel array described herein achieved 77% of ultimate intrinsic SNR in the center of the brain. Transmit field maps obtained experimentally on a phantom with and without the receive array were similar and matched EM simulations, leading to FDA approval for human imaging. Anatomical and functional data, including with power demanding sequences, were acquired successfully on human volunteers. Conclusions Counterintuitive to expectations based on magnetic fields ≤7T, the higher channel counts provided SNR gains centrally, capturing ∼80% uiSNR. Fraction of uiSNR achieved centrally in 64Rx, 80Rx, and 128Rx arrays suggested that a plateau was being reached at 80%. At this plateau, linear to approximately quadratic B 0 dependent SNR gains for the periphery and the center, respectively, were observed for 10.5T relative 7T.

PURPOSE: We developed a 3D vision transformer-based neural network to reconstruct electrical properties (EP) from magnetic resonance measurements. THEORY AND METHODS: Our network uses the magnitude of the transmit magnetic field of a birdcage coil, the associated transceive phase, and a Canny edge mask that identifies the object boundaries as inputs to compute the EP maps. We trained our network on a dataset of 10 000 synthetic tissue-mimicking phantoms and fine-tuned it on a dataset of 11 000 realistic head models. We assessed performance in-distribution simulated data and out-of-distribution head models, with and without synthetic lesions. We further evaluated our network in experiments for an inhomogeneous phantom and a volunteer. RESULTS: The conductivity and permittivity maps had an average peak normalized absolute error (PNAE) of 1.3% and 1.7% for the synthetic phantoms, respectively. For the realistic heads, the average PNAE for the conductivity and permittivity was 1.8% and 2.7%, respectively. The location of synthetic lesions was accurately identified, with reconstructed conductivity and permittivity values within 15% and 25% of the ground-truth, respectively. The conductivity and permittivity for the phantom experiment yielded 2.7% and 2.1% average PNAEs with respect to probe-measured values, respectively. The in vivo EP reconstruction truthfully preserved the subject's anatomy with average values over the entire head similar to the expected literature values. CONCLUSION: We introduced a new learning-based approach for reconstructing EP from MR measurements obtained with a birdcage coil, marking an important step towards the development of clinically-usable in vivo EP reconstruction protocols.

Abstract Purpose Toward pushing the boundaries of ultrahigh fields for human brain imaging, we wish to evaluate experimentally achievable SNR relative to ultimate intrinsic SNR (uiSNR) at 10.5T, develop design strategies toward approaching the latter, quantify magnetic field–dependent SNR gains, and demonstrate the feasibility of whole‐brain, high‐resolution human brain imaging at this uniquely high field strength. Methods A dual row 16‐channel self‐decoupled transmit (Tx) and receive (Rx) array was developed for 10.5T using custom Tx/Rx switches. A 64‐channel receive‐only array was built to fit into the 16‐channel Tx/Rx array. Electromagnetic modeling and experiments were used to define safe operational power limits. Experimental SNR was evaluated relative to uiSNR at 10.5T and 7T. Results The 64‐channel Rx array alone captured approximately 50% of the central uiSNR at 10.5T, while an identical array developed for 7T captured about 76% of uiSNR at 7T. The 16‐channel Tx/80‐channel Rx configuration brought the fraction of uiSNR captured at 10.5T to levels comparable to the 64‐channel Rx array at 7T. SNR data displayed an approximate dependence over a large central region when evaluated in the context of uiSNR. Whole‐brain, high‐resolution ‐weighted and T 1 ‐weighted anatomical and gradient‐recalled‐echo BOLD‐EPI functional MRI images were obtained at 10.5T for the first time with such an advanced array. Conclusion We demonstrated the ability to approach the uiSNR at 10.5T over the human brain, achieving large SNR gains over 7T, currently the most commonly used ultrahigh‐field platform. Whole‐brain, high‐resolution anatomical and EPI‐based functional MRI data were obtained at 10.5T, illustrating the promise of greater than 10T fields in studying the human brain.

High Resolution Image Download MS PowerPoint Slide Magnesium (Mg 2+) is the most abundant divalent cation in the cell and is essential to nearly every biochemical reaction involving adenosine triphosphate (ATP) and its lower energy counterpart, adenosine diphosphate (ADP). In this work, we examine the solution dynamics of ADP at different concentrations and record the changes thereof due to the presence of Mg 2+ ions. Relaxation and diffusion experiments were performed on a range of ADP solutions with increasing magnesium concentration. The most significant changes of both relaxation and diffusion behaviors are observed when adding Mg 2+ up to 0.5 ADP equivalent (eq), with most of the changes complete at 1 eq. Molecular dynamics simulations also show a significant structure introduced by Mg 2+ with very stable pyramidal coordination with the phosphate oxygens. A more extended structure found in the presence of Mg 2+ is consistent with the experimental slowing of diffusion and an increase in the spin–lattice relaxation rate. We do not observe direct evidence of aggregation in solution, although translational diffusion is slowed down significantly at higher concentrations (while solvent diffusion remains constant).

High Resolution Image Download MS PowerPoint Slide Metal–insulator transitions (MITs) in resistive switching materials can be triggered by an electric stimulus that produces significant changes in the electrical response. When these phases have distinct magnetic characteristics, dramatic changes in the spin excitations are also expected. The transition metal oxide La 0.7 Sr 0.3 MnO 3 (LSMO) is a ferromagnetic metal at low temperatures and a paramagnetic insulator above room temperature. When LSMO is in its metallic phase, a critical electrical bias has been shown to lead to an MIT that results in the formation of a paramagnetic resistive barrier transverse to the applied electric field. Using spin-transfer ferromagnetic resonance spectroscopy, we show that even for electrical biases less than the critical value that triggers the MIT, there is magnetic phase separation, with the spin-excitation resonances varying systematically with applied bias. Therefore, voltage-triggered MITs in LSMO can alter magnetic resonance characteristics, offering an effective method for tuning synaptic weights in neuromorphic circuits.

Rechargeable batteries represent a key transformative technology for electric vehicles, portable electronics, and renewable energy. Despite enormous developments in battery research, there are few nondestructive diagnostic techniques compatible with realistic commercial cell enclosures. Many battery failures result from the loss or chemical degradation of electrolyte. Here we show measurements through battery enclosures that allow quantification of electrolyte amount, composition, and potentially degradation. This study employs instrumentation and techniques developed in the context of zero-to-ultralow-field nuclear magnetic resonance (ZULF NMR), with optical atomic magnetometers as the detection elements. In contrast to conventional NMR methodology, the reduced background magnetic fields employed here make even potentially thick stacks of battery housing and electrodes transparent to the lower-frequency electromagnetic fields involved. Both the solvent and lithium-salt components of the electrolyte signature can be quantified, as the results described herein demonstrate.

Femoroacetabular impingement (FAI) is a cause of hip pain and can lead to hip osteoarthritis. Radiological measurements obtained from radiographs or magnetic resonance imaging (MRI) are normally used for FAI diagnosis, but they require time-consuming manual interaction, which limits accuracy and reproducibility. This study compares standard radiologic measurements against radiomics features automatically extracted from MRI for the identification of FAI patients versus healthy subjects. Three-dimensional Dixon MRI of the pelvis were retrospectively collected for 10 patients with confirmed FAI and acquired for 10 healthy subjects. The femur and acetabulum were segmented bilaterally and associated radiomics features were extracted from the four MRI contrasts of the Dixon sequence (water-only, fat-only, in-phase, and out-of-phase). A radiologist collected 21 radiological measurements typically used in FAI. The Gini importance was used to define 9 subsets with the most predictive radiomics features and one subset for the most diagnostically relevant radiological measurements. For each subset, 100 Random Forest machine learning models were trained with different data splits and fivefold cross-validation to classify healthy subjects versus FAI patients. The average performance among the 100 models was computed for each subset and compared against the performance of the radiological measurements. One model trained using the radiomics features datasets yielded 100% accuracy in the detection of FAI, whereas all other radiomics features exceeded 80% accuracy. Radiological measurements yielded 74% accuracy, consistent with previous work. The results of this preliminary work highlight for the first time the potential of radiomics for fully automated FAI diagnosis.

High Resolution Image Download MS PowerPoint Slide This study reports on the conformational states of nicotinamide adenine dinucleotide (NADH) in water/DMSO mixtures and examines the influence of ion binding. We observe evidence of conformational changes through a series of NMR techniques, including 31 P NMR relaxation (R 1 and R 2), chemical exchange saturation transfer (CEST), and diffusion-ordered spectroscopy (DOSY) experiments. The observed variation of the conformational states is indicative of the solvent’s influence on NADH’s structural flexibility. The experimental findings, in combination with viscosity data, are shown to be in line with findings from earlier molecular dynamics studies. The reported observations emphasize the critical role of the solvent environment in determining the conformational states of NADH and similar molecules with relevance for the biophysiological context. The results found herein can help in studying the biophysical behavior of NADH and similar biomolecules and their associated metabolic pathways under various solvent conditions.

PURPOSE: To introduce an alternative idea for fat suppression that is suited both for low-field applications where conventional fat-suppression approaches become ineffective due to narrow spectral separation and for applications with strong B0 homogeneities. METHODS: Separation of fat and water is achieved by sweeping the frequency of RF saturation pulses during continuous radial acquisition and calculating frequency-resolved images using regularized iterative reconstruction. Voxel-wise signal-response curves are extracted that reflect tissue's response to RF saturation at different frequencies and allow the classification into fat or water. This information is then utilized to generate water-only composite images. The principle is demonstrated in free-breathing abdominal and neck examinations using stack-of-stars 3D balanced SSFP (bSSFP) and gradient-recalled echo (GRE) sequences at 0.55 and 3T. Moreover, a potential extension toward quantitative fat/water separation is described. RESULTS: Experiments with a proton density fat fraction (PDFF) phantom validated the reliability of fat/water separation using signal-response curves. As demonstrated for abdominal imaging at 0.55T, the approach resulted in more uniform fat suppression without loss of water signal and in improved CSF-to-fat signal ratio. Moreover, the approach provided consistent fat suppression in 3T neck exams where conventional spectrally-selective fat saturation failed due to strong local B0 inhomogeneities. The feasibility of simultaneous fat/water quantification has been demonstrated in a PDFF phantom. CONCLUSION: The proposed principle achieves reliable fat suppression in low-field applications and adapts to high-field applications with strong B0 inhomogeneity. Moreover, the principle potentially provides a basis for developing an alternative approach for PDFF quantification.

Metal-insulator transitions (MITs) in resistive switching materials can be triggered by an electric stimulus that produces significant changes in the electrical response. When these phases have distinct magnetic characteristics, dramatic changes in spin excitations are also expected. The transition metal oxide La0.7Sr0.3MnO3 (LSMO) is a ferromagnetic metal at low temperatures and a paramagnetic insulator above room temperature. When LSMO is in its metallic phase a critical electrical bias has been shown to lead to an MIT that results in the formation of a paramagnetic resistive barrier transverse to the applied electric field. Using spin-transfer ferromagnetic resonance spectroscopy, we show that even for electrical biases less than the critical value that triggers the MIT, there is magnetic phase separation with the spin-excitation resonances varying systematically with applied bias. Thus, applied voltages provide a means to alter spin resonance characteristics of interest for neuromorphic circuits.

Abstract Purpose To develop multichannel transmit and receive arrays towards capturing the ultimate-intrinsic-SNR (uiSNR) at 10.5 Tesla (T) and to demonstrate the feasibility and potential of whole-brain, high-resolution human brain imaging at this high field strength. Methods A dual row 16-channel self-decoupled transmit (Tx) array was converted to a 16Tx/Rx transceiver using custom transmit/receive switches. A 64-channel receive-only (64Rx) array was built to fit into the 16Tx/Rx array. Electromagnetic modeling and experiments were employed to define safe operation limits of the resulting 16Tx/80Rx array and obtain FDA approval for human use. Results The 64Rx array alone captured approximately 50% of the central uiSNR at 10.5T while the identical 7T 64Rx array captured ∼76% of uiSNR at this lower field strength. The 16Tx/80Rx configuration brought the fraction of uiSNR captured at 10.5T to levels comparable to the performance of the 64Rx array at 7T. SNR data obtained at the two field strengths with these arrays displayed dependent increases over a large central region. Whole-brain high resolution T 2 * and T 1 weighted anatomical and gradient-recalled echo EPI BOLD fMRI images were obtained at 10.5T for the first time with such an advanced array, illustrating the promise of >10T fields in studying the human brain. Conclusion We demonstrated the ability to approach the uiSNR at 10.5T over the human brain with a novel, high channel count array, achieving large SNR gains over 7T, currently the most commonly employed ultrahigh field platform, and demonstrate high resolution and high contrast anatomical and functional imaging at 10.5T.

Abstract We introduce three architecture modifications to enhance the performance of the end-to-end (E2E) variational network (VarNet) for undersampled MRI reconstructions. We first implemented the Feature VarNet, which propagates information throughout the cascades of the network in an N-channel feature-space instead of a 2-channel feature-space. Then, we add an attention layer that utilizes the spatial locations of Cartesian undersampling artifacts to further improve performance. Lastly, we combined the Feature and E2E VarNets into the Feature-Image (FI) VarNet, to facilitate cross-domain learning and boost accuracy. Reconstructions were evaluated on the fastMRI dataset using standard metrics and clinical scoring by three neuroradiologists. Feature and FI VarNets outperformed the E2E VarNet for 4 × ×, 5 × × and 8 × × Cartesian undersampling in all studied metrics. FI VarNet secured second place in the public fastMRI leaderboard for 4 × × Cartesian undersampling, outperforming all open-source models in the leaderboard. Radiologists rated FI VarNet brain reconstructions with higher quality and sharpness than the E2E VarNet reconstructions. FI VarNet excelled in preserving anatomical details, including blood vessels, whereas E2E VarNet discarded or blurred them in some cases. The proposed FI VarNet enhances the reconstruction quality of undersampled MRI and could enable clinically acceptable reconstructions at higher acceleration factors than currently possible.

The authors acknowledge financial

PURPOSE: We examined magnetic field dependent SNR gains and ability to capture them with multichannel receive arrays for human head imaging in going from 7 T, the most commonly used ultrahigh magnetic field (UHF) platform at the present, to 10.5 T, which represents the emerging new frontier of >10 T in UHFs. METHODS: Electromagnetic (EM) models of 31-channel and 63-channel multichannel arrays built for 10.5 T were developed for 10.5 T and 7 T simulations. A 7 T version of the 63-channel array with an identical coil layout was also built. Array performance was evaluated in the EM model using a phantom mimicking the size and electrical properties of the human head and a digital human head model. Experimental data was obtained at 7 T and 10.5 T with the 63-channel array. Ultimate intrinsic SNR (uiSNR) was calculated for the two field strengths using a voxelized cloud of dipoles enclosing the phantom or the digital human head model as a reference to assess the performance of the two arrays and field depended SNR gains. RESULTS: uiSNR calculations in both the phantom and the digital human head model demonstrated SNR gains at 10.5 T relative to 7 T of 2.6 centrally, ˜2 at the location corresponding to the edge of the brain, ˜1.4 at the periphery. The EM models demonstrated that, centrally, both arrays captured ˜90% of the uiSNR at 7 T, but only ˜65% at 10.5 T, leading only to ˜2-fold gain in array SNR in going from 7 to 10.5 T. This trend was also observed experimentally with the 63-channel array capturing a larger fraction of the uiSNR at 7 T compared to 10.5 T, although the percentage of uiSNR captured were slightly lower at both field strengths compared to EM simulation results. CONCLUSIONS: Major uiSNR gains are predicted for human head imaging in going from 7 T to 10.5 T, ranging from ˜2-fold at locations corresponding to the edge of the brain to 2.6-fold at the center, corresponding to approximately quadratic increase with the magnetic field. Realistic 31- and 63-channel receive arrays, however, approach the central uiSNR at 7 T, but fail to do so at 10.5 T, suggesting that more coils and/or different type of coils will be needed at 10.5 T and higher magnetic fields.

The study of electrolytic solutions is of relevance in many research fields, ranging from biophysics, materials, and colloid science to catalysis and electrochemistry. The dependence of solution dynamics on the nature of electrolytes and their concentrations has been the subject of many experimental and computational studies, yet it remains challenging to obtain a full understanding of the factors that govern solution behavior. Here, we provide additional insights into the behavior of aqueous solutions of alkali chlorides by combining 17O relaxation data with diffusion and viscosity data and contrast their behavior with 1H nuclear magnetic resonance relaxation data. The main findings are that 17O relaxation correlates well with viscosity data but not with diffusion data, while 1H relaxation correlates with neither. Certain ionic trends match known ion-specific series behavior, especially at high concentrations. Notably, we also examine the ranges of the interactions and conclude that the majority of the effects are tied to local water reorientation dynamics.

PURPOSE: Recent numerical and empirical results proved that high permittivity materials (HPM) used in pads placed near the subject or directly integrated with coils can increase the SNR and reduce the specific absorption rate (SAR) in MRI. In this paper, we propose an analytical investigation of the effect on the magnetic field distribution of a layer of HPM surrounding an anatomy-mimicking cylindrical sample. METHODS: The study is based on a reformulation of the Mie scattering for cylindrical geometry, following an approach recently introduced for spherical samples. The total field in each medium is decomposed in terms of inward and outward electromagnetic waves, and the fields are expressed as series of cylindrical harmonics, whose coefficients can be interpreted as classical reflection and transmission coefficients. RESULTS: Our new formulation allows a quantitative evaluation of the effect of the HPM layer for varying permittivity and thickness, and it provides an intuitive understanding of such effect in terms of propagation and scattering of the RF field. CONCLUSION: We show how HPM can filter out the modes that only contribute to the noise or RF power deposition, resulting in higher SNR or lower SAR, respectively. Our proposed framework provides physical insight on how to properly design HPM for MRI applications.

Different pathologies of the hip are characterized by the abnormal shape of the bony structures of the joint, namely the femur and the acetabulum. Three-dimensional (3D) models of the hip can be used for diagnosis, biomechanical simulation, and planning of surgical treatments. These models can be generated by building 3D surfaces of the joint's structures segmented on magnetic resonance (MR) images. Deep learning can avoid time-consuming manual segmentations, but its performance depends on the amount and quality of the available training data. Data augmentation and transfer learning are two approaches used when there is only a limited number of datasets. In particular, data augmentation can be used to artificially increase the size and diversity of the training datasets, whereas transfer learning can be used to build the desired model on top of a model previously trained with similar data. This study investigates the effect of data augmentation and transfer learning on the performance of deep learning for the automatic segmentation of the femur and acetabulum on 3D MR images of patients diagnosed with femoroacetabular impingement. Transfer learning was applied starting from a model trained for the segmentation of the bony structures of the shoulder joint, which bears some resemblance to the hip joint. Our results suggest that data augmentation is more effective than transfer learning, yielding a Dice similarity coefficient compared to ground-truth manual segmentations of 0.84 and 0.89 for the acetabulum and femur, respectively, whereas the Dice coefficient was 0.78 and 0.88 for the model based on transfer learning. The Accuracy for the two anatomical regions was 0.95 and 0.97 when using data augmentation, and 0.87 and 0.96 when using transfer learning. Data augmentation can improve the performance of deep learning models by increasing the diversity of the training dataset and making the models more robust to noise and variations in image quality. The proposed segmentation model could be combined with radiomic analysis for the automatic evaluation of hip pathologies.

mixtures at concentrations below 100 nM. This advance is due to the electrochemical properties of NG sensors which, in combination with the engineered FSCV waveform, provided distinguishable cyclic voltammograms (CVs) for DA and 5-HT. We also demonstrate the generalizability of the prediction model across different NG sensors, which arises from the consistent voltammetric fingerprints produced by our NG sensors. Curiously, the proposed engineered waveform also improves the distinguishability of DA and 5-HT CVs obtained from traditional carbon fiber (CF) microelectrodes. Nevertheless, this improved distinguishability of CVs obtained from CF is inferior to that of NG sensors, arising from differences in the electrochemical properties of the sensor materials. Our findings demonstrate the potential of NG sensors and our proposed FSCV waveform for future brain studies.

We propose Physics-Informed Fourier Networks for Electrical Properties (EP) Tomography (PIFON-EPT), a novel deep learning-based method for EP reconstruction using noisy and/or incomplete magnetic resonance (MR) measurements. Our approach leverages the Helmholtz equation to constrain two networks, responsible for the denoising and completion of the transmit fields, and the estimation of the object's EP, respectively. We embed a random Fourier features mapping into our networks to enable efficient learning of high-frequency details encoded in the transmit fields. We demonstrated the efficacy of PIFON-EPT through several simulated experiments at 3 and 7 tesla(T) MR imaging, and showed that our method can reconstruct physically consistent EP and transmit fields. Specifically, when only 20% of the noisy measured fields were used as inputs, PIFON-EPT reconstructed the EP of a phantom with ≤ 5% error, and denoised and completed the measurements with ≤ 1% error. Additionally, we adapted PIFON-EPT to solve the generalized Helmholtz equation that accounts for gradients of EP between inhomogeneities. This yielded improved results at interfaces between different materials without explicit knowledge of boundary conditions. PIFON-EPT is the first method that can simultaneously reconstruct EP and transmit fields from incomplete noisy MR measurements, providing new opportunities for EPT research.

Li-ion cells based on layered transition metal oxides (LTMO) demonstrate the best overall performance to date. A detailed understanding of ion transport and charge storage mechanisms in these cathode materials is key to improved design, performance, and safety of cells. The magnetism of LTMO-based materials depends on the concentration and the type of the intercalant. This phenomenon provides a source of sensitive magnetic resonance imaging (MRI) contrast for studies of Li-ion cell function and failure mechanisms. Surface-scan MRI is a nondestructive operando technique designed for artifact-free mapping of strongly inhomogeneous magnetic fields near various portable devices and battery cells. Recent experiments revealed nonuniform distributions of current density and magnetic susceptibility in common Li-ion pouch cells. Further analysis of the surface-scan MRI data suggests the coexistence of several magnetic phases and the presence of transient Li concentration gradients in the cathode. These hypotheses are validated herein through the observation of propagating magnetic susceptibility fronts in Li x CoO 2 cathodes of resting state pouch cells. We show evidence for the cathode lithium distribution to follow the areas of high current densities, which is a surprising result, given that the cathode generally has very high conductivity. Furthermore, equalization of the lithiation levels is a slow process happening over several days. Such observations of structural varieties and solid-state ion transport are possible in any material with pronounced intercalation-dependent magnetic properties. The methodology described in this work is a powerful tool for the analysis of kinetic phenomena in a wide range of pouch cells.

Abstract Phosphate is an essential anion in the human body, comprising approximately 1% of the total body weight, and playing a vital role in metabolism, cell membranes, and bone formation. We have recently provided spectroscopic, microscopic, and computational evidence indicating that phosphates can aggregate much more readily in solution than previously thought. This prior work provided indirect evidence through the observation of unusual P NMR relaxation and line‐broadening effects with increasing temperature. Here, we show that, under conditions of slow exchange and selective RF saturation, additional features become visible in chemical exchange saturation transfer (CEST) experiments, which appear to be related to the previously reported phosphate clustering. In particular, CEST shows pronounced dips several ppm upfield of the main phosphate resonance at low temperatures, while direct P spectroscopy does not produce any signals in that range. We study the pH dependence of these new spectroscopic features and present exchange and spectroscopic parameters based on fitting the CEST data. These findings could be of importance in the investigation of phosphate dynamics, especially in the biological milieu.

Abstract Fast‐scan cyclic voltammetry (FSCV) in combination with carbon fiber (CF) sensors has been a longstanding tool for studying neurochemical signaling in the brain. However, further progress toward brain mapping requires improvements in sensitivity, chemical selectivity, and channel count. Addressing these critical needs has been an impetus behind the search for alternative carbon materials that could outperform CF. Given the significant research activities, an objective assessment of key sensor performance metrics is crucial for guiding the material discovery. Here, a framework for assessing the sensitivity and selectivity performance of FSCV sensors is put forth and visualizing the comparison in the form of a performance plot. An example of this analysis by using nanographitic carbon as a model material is provided. It is hoped that the proposed framework can help accelerate the research progress by providing an improved guideline for evaluating emerging FSCV sensors.

Abstract Purpose To introduce a method for the estimation of the ideal current patterns (ICP) that yield optimal signal‐to‐noise ratio (SNR) for realistic heterogeneous tissue models in MRI. Theory and Methods The ICP were calculated for different surfaces that resembled typical radiofrequency (RF) coil formers. We constructed numerical electromagnetic (EM) bases to accurately represent EM fields generated by RF current sources located on the current‐bearing surfaces. Using these fields as excitations, we solved the volume integral equation and computed the EM fields in the sample. The fields were appropriately weighted to calculate the optimal SNR and the corresponding ICP. We demonstrated how to qualitatively use ICP to guide the design of a coil array to maximize SNR inside a head model. Results In agreement with previous analytic work, ICP formed large distributed loops for voxels in the middle of the sample and alternated between a single loop and a figure‐eight shape for a voxel 3‐cm deep in the sample's cortex. For the latter voxel, a surface quadrature loop array inspired by the shape of the ICP reached of the optimal SNR at 3T, whereas a single loop placed above the voxel reached only of the optimal SNR. At 7T, the performance of the two designs decreased to and, respectively, suggesting that loops could be suboptimal at ultra‐high field MRI. Conclusion ICP can be calculated for human tissue models, potentially guiding the design of application‐specific RF coil arrays.

Probabilistic Neuromorphic Computing Unlike modern computers that are engineered to be fully deterministic, the brain leverages stochasticity at immense scales to achieve energy-efficient intelligence. In article number 2204569, Shashank Misra, James B. Aimone, and co-workers describe how the incorporation of materials and devices that exhibit stochastic behaviors into brain-inspired circuits can enable neuromorphic systems that natively perform probabilistic computations.

Far more publications are available for osteoarthritis of the knee than of the hip. Recognizing this research gap, the Arthritis Foundation (AF), in partnership with the Hospital for Special Surgery (HSS), convened an in-person meeting of thought leaders to review the state of the science of and clinical approaches to hip osteoarthritis. This article summarizes the recommendations gleaned from 5 presentations given in the "early hip osteoarthritis" session of the 2023 Hip Osteoarthritis Clinical Studies Conference, which took place on February 17 and 18, 2023, in New York City. It also summarizes the workgroup recommendations from a small-group discussion on clinical research gaps.

High Resolution Image Download MS PowerPoint Slide Low sensitivity is the primary limitation to extending nuclear magnetic resonance (NMR) techniques to more advanced chemical and structural studies. Photochemically induced dynamic nuclear polarization (photo-CIDNP) is an NMR hyperpolarization technique where light is used to excite a suitable donor–acceptor system, creating a spin-correlated radical pair whose evolution drives nuclear hyperpolarization. Systems that exhibit photo-CIDNP in solids are not common, and this effect has, up to now, only been observed for 13 C and 15 N nuclei. However, the low gyromagnetic ratio and natural abundance of these nuclei trap the local hyperpolarization in the vicinity of the chromophore and limit the utility for bulk hyperpolarization. Here, we report the first example of optically enhanced solid-state 1 H NMR spectroscopy in the high-field regime. This is achieved via photo-CIDNP of a donor–chromophore–acceptor molecule in a frozen solution at 0.3 T and 85 K, where spontaneous spin diffusion among the abundant strongly coupled 1 H nuclei relays polarization through the whole sample, yielding a 16-fold bulk 1 H signal enhancement under continuous laser irradiation at 450 nm. These findings enable a new strategy for hyperpolarized NMR beyond the current limits of conventional microwave-driven DNP.

Low sensitivity is the primary limitation to extending nuclear magnetic resonance (NMR) techniques to more advanced chemical and structural studies. Photochemically induced dynamic nuclear polarization (photo-CIDNP) is an NMR hyperpolarization technique where light is used to excite a suitable donor–acceptor system, creating a spin-correlated radical pair whose evolution drives nuclear hy-perpolarization. Systems that exhibit photo-CIDNP in solids are not common and this effect has, up to now, only been observed for 13C and 15N nuclei. However, the low gyromagnetic ratio and natural abundance of these nuclei trap the local hyperpolarization in the vicinity of the chromophore and limit the utility for bulk hyperpolarization. Here we report the first example of optically enhanced solid-state 1H NMR spectroscopy in the high-field regime. This is achieved via photo-CIDNP of a donor–chromophore–acceptor molecule in a frozen solution at 0.3 T and 85 K, where spontaneous spin diffusion among the abundant strongly coupled 1H nuclei relays polarization through the whole sample, yielding a 16-fold bulk 1H signal enhancement under continuous laser irradiation at 450 nm. These findings enable a new strategy for hyperpolarized NMR beyond the current limits of conventional microwave-driven DNP.

Introduction Femoroacetabular Impingement (FAI) is a hip pathology characterized by impingement of the femoral head-neck junction against the acetabular rim, due to abnormalities in bone morphology. FAI is normally diagnosed by manual evaluation of morphologic features on magnetic resonance imaging (MRI). In this study, we assess, for the first time, the feasibility of using radiomics to detect FAI by automatically extracting quantitative features from images. Material and methods 17 patients diagnosed with monolateral FAI underwent pre-surgical MR imaging, including a 3D Dixon sequence of the pelvis. An expert radiologist drew regions of interest on the water-only Dixon images outlining femur and acetabulum in both impingement (IJ) and healthy joints (HJ). 182 radiomic features were extracted for each hip. The dataset numerosity was increased by 60 times with an ad-hoc data augmentation tool. Features were subdivided by type and region in 24 subsets. For each, a univariate ANOVA F-value analysis was applied to find the 5 features most correlated with IJ based on p-value, for a total of 48 subsets. For each subset, a K-nearest neighbor model was trained to differentiate between IJ and HJ using the values of the radiomic features in the subset as input. The training was repeated 100 times, randomly subdividing the data with 75%/25% training/testing. Results The texture-based gray level features yielded the highest prediction max accuracy (0.972) with the smallest subset of features. This suggests that the gray image values are more homogeneously distributed in the HJ in comparison to IJ, which could be due to stress-related inflammation resulting from impingement. Conclusions We showed that radiomics can automatically distinguish IJ from HJ using water-only Dixon MRI. To our knowledge, this is the first application of radiomics for FAI diagnosis. We reported an accuracy greater than 97%, which is higher than the 90% accuracy for detecting FAI reported for standard diagnostic tests (90%). Our proposed radiomic analysis could be combined with methods for automated joint segmentation to rapidly identify patients with FAI, avoiding time-consuming radiological measurements of bone morphology.

Nuclear magnetic resonance relaxometry represents a powerful tool for extracting dynamic information. Yet, obtaining links to molecular motion is challenging for many ions that relax through the quadrupolar mechanism, which is mediated by electric field gradient fluctuations and lacks a detailed microscopic description. For sodium ions in aqueous electrolytes, we combine ab initio calculations to account for electron cloud effects with classical molecular dynamics to sample long-time fluctuations, and obtain relaxation rates in good agreement with experiments over broad concentration and temperature ranges. We demonstrate that quadrupolar nuclear relaxation is sensitive to subpicosecond dynamics not captured by previous models based on water reorientation or cluster rotation. While ions affect the overall water retardation, experimental trends are mainly explained by dynamics in the first two solvation shells of sodium, which contain mostly water. This work thus paves the way to the quantitative understanding of quadrupolar relaxation in electrolyte and bioelectrolyte systems.

Phosphates and polyphosphates play ubiquitous roles in biology as integral structural components of cell membranes and bone, or as vehicles of energy storage via adenosine triphosphate and phosphocreatine. The solution phase space of phosphate species appears more complex than previously known. We present nuclear magnetic resonance (NMR) and cryogenic transmission electron microscopy (cryo-TEM) experiments that suggest phosphate species including orthophosphates, pyrophosphates, and adenosine phosphates associate into dynamic assemblies in dilute solutions that are spectroscopically "dark." Cryo-TEM provides visual evidence of the formation of spherical assemblies tens of nanometers in size, while NMR indicates that a majority population of phosphates remain as unassociated ions in exchange with spectroscopically invisible assemblies. The formation of these assemblies is reversibly and entropically driven by the partial dehydration of phosphate groups, as verified by diffusion-ordered spectroscopy (DOSY), indicating a thermodynamic state of assembly held together by multivalent interactions between the phosphates. Molecular dynamics simulations further corroborate that orthophosphates readily cluster in aqueous solutions. This study presents the surprising discovery that phosphate-containing molecules, ubiquitously present in the biological milieu, can readily form dynamic assemblies under a wide range of commonly used solution conditions, highlighting a hitherto unreported property of phosphate's native state in biological solutions.

BACKGROUND: Magnetic resonance fingerprinting (MRF) is a method to speed up acquisition of quantitative MRI data. However, MRF does not usually produce contrast-weighted images that are required by radiologists, limiting reachable total scan time improvement. Contrast synthesis from MRF could significantly decrease the imaging time. PURPOSE: To improve clinical utility of MRF by synthesizing contrast-weighted MR images from the quantitative data provided by MRF, using U-nets that were trained for the synthesis task utilizing L1- and perceptual loss functions, and their combinations. STUDY TYPE: Retrospective. POPULATION: Knee joint MRI data from 184 subjects from Northern Finland 1986 Birth Cohort (ages 33-35, gender distribution not available). FIELD STRENGTH AND SEQUENCE: A 3 T, multislice-MRF, proton density (PD)-weighted 3D-SPACE (sampling perfection with application optimized contrasts using different flip angle evolution), fat-saturated T2-weighted 3D-space, water-excited double echo steady state (DESS). ASSESSMENT: Data were divided into training, validation, test, and radiologist's assessment sets in the following way: 136 subjects to training, 3 for validation, 3 for testing, and 42 for radiologist's assessment. The synthetic and target images were evaluated using 5-point Likert scale by two musculoskeletal radiologists blinded and with quantitative error metrics. STATISTICAL TESTS: Friedman's test accompanied with post hoc Wilcoxon signed-rank test and intraclass correlation coefficient. The statistical cutoff P <0.05 adjusted by Bonferroni correction as necessary was utilized. RESULTS: The networks trained in the study could synthesize conventional images with high image quality (Likert scores 3-4 on a 5-point scale). Qualitatively, the best synthetic images were produced with combination of L1- and perceptual loss functions and perceptual loss alone, while L1-loss alone led to significantly poorer image quality (Likert scores below 3). The interreader and intrareader agreement were high (0.80 and 0.92, respectively) and significant. However, quantitative image quality metrics indicated best performance for the pure L1-loss. DATA CONCLUSION: Synthesizing high-quality contrast-weighted images from MRF data using deep learning is feasible. However, more studies are needed to validate the diagnostic accuracy of these synthetic images. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.

The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event-driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain's ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co-design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware-based probabilistic computing technologies.

quantitative 5-HT detection. These measurements reveal that water plays a significant role in driving side reactions during low-voltage FSCV measurements, leading to the observation of a well-defined secondary redox couple we associated with the redox reaction of tryptamine 4,5-dione. Remarkably, these side reactions can persist subsequent to the primary redox events associated with 5-HT. Furthermore, the results reveal a critical deviation from this ideal redox behavior if the FSCV anodic limit exceeds +0.8 V, which can be attributed to the generation of radical species from water oxidation. These new insights could lead to new FSCV protocols for more reliable 5-HT detection.

High static field MR scanners can produce human tissue images of astounding clarity, but rely on high frequency electromagnetic radiation that generates complicated in-tissue field patterns that are patient-specific and potentially harmful. Many such scanners use parallel transmitters to better control field patterns, but then adjust the transmitters based on general guidelines rather than optimizing for the specific patient, mostly because computing patient-specific fields was presumed far too slow. It was recently demonstrated that the combination of fast low-resolution tissue mapping and fast voxel-based field simulation can be used to perform a patient-specific MR safety check in minutes. However, the field simulation required several of those minutes, making it too slow to perform the dozens of simulations that would be needed for patient-specific optimization. In this paper we describe a compressed-perturbation-matrix technique that nearly eliminates the computational cost of including complex coils (or coils and shields) in voxel-based field simulation of tissue, thereby reducing simulation time from minutes to seconds. The approach is demonstrated on a wide variety of head+coil and head+coil+shield configurations, using the implementation in MARIE 2.0, the latest version of the open-source MR field simulator MARIE.

Electrical properties (EP), namely permittivity and electric conductivity, dictate the interactions between electromagnetic waves and biological tissue. EP can be potential biomarkers for pathology characterization, such as cancer, and improve therapeutic modalities, such radiofrequency hyperthermia and ablation. MR-based electrical properties tomography (MR-EPT) uses MR measurements to reconstruct the EP maps. Using the homogeneous Helmholtz equation, EP can be directly computed through calculations of second order spatial derivatives of the measured magnetic transmit or receive fields (B1⁺, B1⁻). However, the numerical approximation of derivatives leads to noise amplifications in the measurements and thus erroneous reconstructions. Recently, a noise-robust supervised learning-based method (DL-EPT) was introduced for EP reconstruction. However, the pattern-matching nature of such network does not allow it to generalize for new samples since the network's training is done on a limited number of simulated data. In this work, we leverage recent developments on physics-informed deep learning to solve the Helmholtz equation for the EP reconstruction. We develop deep neural network (NN) algorithms that are constrained by the Helmholtz equation to effectively de-noise the B1⁺ measurements and reconstruct EP directly at an arbitrarily high spatial resolution without requiring any known B1⁺ and EP distribution pairs.

Gapped two-dimensional topological phases can feature ungappable edge states which are robust even in the absence of protecting symmetries. In this Letter we argue that a multipartite entanglement measure recently proposed in the context of holography, the Markov gap, provides a universal diagnostic of ungappable edge states. Defined as a difference of the reflected entropy and mutual information h(A:B)=SR(A:B)-I(A:B) between two parties, we argue that for $A,B$ being adjacent subregions in the bulk h=c+3ln0.16em0ex2, where c+ is the minimal total central charge of the boundary theory. As evidence, we prove that $h=0$ for string-net models and numerically verify that h=|C|3ln0.16em0ex2 for a Chern-$C$ insulator. Our Letter establishes a unique bulk entanglement criteria for the presence of a conformal field theory on the boundary.

Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short- and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This Perspective discusses select examples of these approaches and provides an outlook on the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.

OBJECTIVE: We developed a hybrid volume surface integral equation (VSIE) method based on domain decomposition to perform fast and accurate magnetic resonance imaging (MRI) simulations that include both remote and local conductive elements. METHODS: We separated the conductive surfaces present in MRI setups into two domains and optimized electromagnetic (EM) modeling for each case. Specifically, interactions between the body and EM waves originating from local radiofrequency (RF) coils were modeled with the precorrected fast Fourier transform, whereas the interactions with remote conductive surfaces (RF shield, scanner bore) were modeled with a novel cross tensor train-based algorithm. We compared the hybrid-VSIE with other VSIE methods for realistic MRI simulation setups. RESULTS: The hybrid-VSIE was the only practical method for simulation using 1 mm voxel isotropic resolution (VIR). For 2 mm VIR, our method could be solved at least 23 times faster and required 760 times lower memory than traditional VSIE methods. CONCLUSION: The hybrid-VSIE demonstrated a marked improvement in terms of convergence times of the numerical EM simulation compared to traditional approaches in multiple realistic MRI scenarios. SIGNIFICANCE: The efficiency of the novel hybrid-VSIE method could enable rapid simulations of complex and comprehensive MRI setups.

A proposal of quantum cognition advances the hypothesis that quantum entanglement between 31P nuclei could serve as a means of information storage in the brain. Testing this hypothesis requires an understanding of how long-lived these quantum effects may be. We used NMR spectroscopy and molecular dynamics simulations to study the mechanisms that limit these quantum processes in 18O-enriched molecules of pyrophosphate, the simplest biomolecule that can sustain quantum-entangled 31P nuclear spin singlet states. We confirmed that chemical shift anisotropy limits the singlet magnetization order lifetimes in high magnetic fields, and we discovered that rapid rotation of the phosphate groups limits the lifetime in low magnetic fields. These findings represent an important starting point in studying whether quantum cognition can be a true biological phenomenon.

Nuclear magnetic resonance relaxometry represents a powerful tool for extracting dynamic information. Yet, obtaining links to molecular motion is challenging for many ions that relax through the quadrupolar mechanism, which is mediated by electric field gradient fluctuations and lacks a detailed microscopic description. For sodium ions in aqueous electrolytes, we combine ab initio calculations to account for electron cloud effects with classical molecular dynamics to sample long-time fluctuations, and obtain relaxation rates in good agreement with experiments over broad concentration and temperature ranges. We demonstrate that quadrupolar nuclear relaxation is sensitive to subpicosecond dynamics not captured by previous models based on water reorientation or cluster rotation. While ions affect the overall water retardation, experimental trends are mainly explained by dynamics in the first two solvation shells of sodium, which contain mostly water. This work thus paves the way to the quantitative understanding of quadrupolar relaxation in electrolyte and bioelectrolyte systems.

We investigated how to construct low-order subspace basis sets to accurately represent electromagnetic fields generated within inhomogeneous arbitrary objects by radio-frequency sources external to a Huygen's surface. The basis generation relies on the singular value decomposition of Green's functions integro-differential operators which makes it feasible to derive a reduced-order yet stable model. We present a detailed study of the theoretical and numerical requisites for generating such basis, and show how it can be used to calculate performance limits in magnetic resonance imaging applications. Finally, we propose a novel numerical framework for the computation of characteristic modes of arbitrary inhomogeneous objects. We validated accuracy and convergence properties of the numerical basis against a complete analytical basis in the case of a uniform spherical object. We showed that the discretization of the Huygens's surface has a minimal effect on the accuracy of the calculations, which mainly depended on the electromagnetic solver resolution and order of approximation.

Recent progress in MRI methods development led to a novel concept for operando screening of commercial battery cells. Sensing electrochemical processes inside an operating cell can be done via the detection of associated magnetic fields. This concept is based on the classic phenomenon described by Ampère's circuital and Biot–Savart laws. A new method referred to as Surface-Scan MRI employs ultra-fast quantitative mapping of the magnetic field in a thin layer of polymer placed in direct contact with the cell. Preliminary experimental work demonstrated the ability of Surface-Scan MRI to detect cells that undergone overcharging, a hazardous event that can degrade cells' components and ultimately creates a risk of fire. In the future, the analysis of Surface-Scan MRI data can rely largely on numerical simulations of electromagnetic phenomena based on accurate models of real cells. A series of numerical tests performed in this work confirms the capacity of Surface-Scan MRI to detect a variety of defects associated with electrochemical degradation of cathode, anode and electrolyte materials. Effects of morphology, location and conductivity of the defects on magnetic field distributions were examined for realistic cell models including flat jelly roll configurations. MRI resolution limits associated with the defect domain size and with the distance between the defect and the detection medium were estimated. Location and morphology of highly conductive defects (σ > 105 S m−1), e.g. regions rich in quasi-metallic dendrites, can be directly identified from Surface-Scan MRI images. Low conductivity defects, e.g. degraded electrolyte or cathode and anode coatings, manifest via patterns much larger than the defects.

OBJECTIVE: We propose a framework to interpret the effects of High Permittivity Materials (HPM) on the performance of radiofrequency coils in Magnetic Resonance Imaging (MRI). METHODS: Based on a recent formulation of the scattering and propagation properties of spheres, we expanded the field in a layered sphere as a superposition of ingoing and outgoing travelling waves, which allowed us to study the field components with a non-homogeneous transmission line model. We investigated the effects of a layer of HPM surrounding a head-mimicking uniform sphere at 7 tesla. RESULTS: Through the analysis of impedance and reflection coefficients, we show that by adjusting the properties of the HPM, it is possible to selectively amplify individual modes, or combination of them, modifying the overall field distribution in the sample and increasing signal-to-noise ratio at specific locations. Our results demonstrate that the observed enhanced MRI performance is not merely due to secondary fields generated by displacement currents in the HPM. CONCLUSIONS: Our formulation explains the effect of HPM in terms of matching, enabling the optimization of the electrical properties of the HPM with a simple mode-matching formula. SIGNIFICANCE: The proposed method could guide the design of novel radiofrequency coils with integrated HPM.

Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This paper discusses select examples of these approaches, and provides a perspective for the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.

Ferromagnetic nanoconstrictions can serve as integrate-and-fire neurons, communicating via spin-wave pulses analogous to neural spikes. The nanoconstriction geometry leads to easy-plane oscillators which manifest phase dynamics analogous to damped driven oscillators. The authors propose a sample geometry where nanoconstrictions undergo phase slips and produce pulses of spin-wave signals when triggered by a small spin torque. They show with the help of macrospin modeling and micromagnetic simulations that these spin-wave pulses can excite other nanoconstriction neurons and that they possess the basic fan-in and fan-out capability required for neuromorphic systems.

-maleate in deuterated chloroform as solvent. The correspondence between experiment and simulations is achieved without adjustable parameters. These studies highlight the importance of considering unusual and difficult-to-control mechanisms, such as dipolar couplings to low-gamma solvent nuclei, and to residual paramagnetic species, which often can represent lifetime limiting factors. These results also point to the power of molecular dynamics simulations to provide insights into little-known NMR relaxation mechanisms.

entities around the bridging P-O bonds, and is treated by a combination of MD simulations and quantum chemistry calculations. Spin-lattice relaxation was predicted well without adjustable parameters, and for SO relaxation one parameter was extracted from the comparison between experiment and computation (a correlation coefficient between the rotational motion of the groups).

PURPOSE: To present a novel 3T 24-channel glove array that enables hand and wrist imaging in varying postures. METHODS: The glove array consists of an inner glove holding the electronics and an outer glove protecting the components. The inner glove consists of four main structures: palm, fingers, wrist, and a flap that rolls over on top. Each structure was constructed out of three layers: a layer of electrostatic discharge flame-resistant fabric, a layer of scuba neoprene, and a layer of mesh fabric. Lightweight and flexible high impedance coil (HIC) elements were inserted into dedicated tubes sewn into the fabric. Coil elements were deliberately shortened to minimize the matching interface. Siemens Tim 4G technology was used to connect all 24 HIC elements to the scanner with only one plug. RESULTS: The 24-channel glove array allows large motion of both wrist and hand while maintaining the SNR needed for high-resolution imaging. CONCLUSION: In this work, a purpose-built 3T glove array that embeds 24 HIC elements is demonstrated for both hand and wrist imaging. The 24-channel glove array allows a great range of motion of both the wrist and hand while maintaining a high SNR and providing good theoretical acceleration performance, thus enabling hand and wrist imaging at different postures to extract kinematic information.

The body-centered cubic (bcc) polymorph of NaCB 11 H 12 has been stabilized at room temperature by high-energy mechanical milling. Temperature-dependent electrochemical impedance spectroscopy shows an optimum at 45-min milling time, leading to an rt conductivity of 4 mS cm –1. Mechanical milling suppresses an order–disorder phase transition in the investigated temperature range. Nevertheless, two main regimes can be identified, with two clearly distinct activation energies. Powder X-ray diffraction and 23 Na solid-state NMR reveal two different Na + environments, which are partially occupied, in the bcc polymorph. The increased number of available sodium sites w.r.t. ccp polymorph raises the configurational entropy of the bcc phase, contributing to a higher ionic conductivity. Mechanical treatment does not alter the oxidative stability of NaCB 11 H 12. Electrochemical test on a symmetric cell (Na|NaCB 11 H 12 |Na) without control of the stack pressure provides a critical current density of 0.12 mA cm –2, able to fully charge/discharge a 120 mA h g –1 specific capacity positive electrode at the rate of C /2.

In a spintronic resonator a radio-frequency signal excites spin dynamics that can be detected by the spin-diode effect. Such resonators are generally based on ferromagnetic metals and their responses to spin torques. New and richer functionalities can potentially be achieved with quantum materials, specifically with transition metal oxides that have phase transitions that can endow a spintronic resonator with hysteresis and memory. Here we present the spin torque ferromagnetic resonance characteristics of a hybrid metal-insulator-transition oxide/ ferromagnetic metal nanoconstriction. Our samples incorporate [Formula: see text], with Ni, Permalloy ([Formula: see text]) and Pt layers patterned into a nanoconstriction geometry. The first order phase transition in [Formula: see text] is shown to lead to systematic changes in the resonance response and hysteretic current control of the ferromagnetic resonance frequency. Further, the output signal can be systematically varied by locally changing the state of the [Formula: see text] with a dc current. These results demonstrate new spintronic resonator functionalities of interest for neuromorphic computing.