搜索结果：Magnetic resonance in medicine

With the increasing interest in deploying Artificial Intelligence in medicine, we previously introduced HAIM (Holistic AI in Medicine), a framework that fuses multimodal data to solve downstream clinical tasks. However, HAIM uses data in a task-agnostic manner and lacks explainability. To address these limitations, we introduce xHAIM (Explainable HAIM), a novel framework leveraging Generative AI to enhance both prediction and explainability through four structured steps: (1) automatically identifying task-relevant patient data across modalities, (2) generating comprehensive patient summaries, (3) using these summaries for improved predictive modeling, and (4) providing clinical explanations by linking predictions to patient-specific medical knowledge. Evaluated on the HAIM-MIMIC-MM dataset, xHAIM improves average AUC from 79.9% to 90.3% across chest pathology and operative tasks. Importantly, xHAIM transforms AI from a black-box predictor into an explainable decision support system, enabling clinicians to interactively trace predictions back to relevant patient data, bridging AI advancements with clinical utility.

Medicine, including fields in healthcare and life sciences, has seen a flurry of quantum-related activities and experiments in the last few years (although biology and quantum theory have arguably been entangled ever since Schrödinger's cat). The initial focus was on biochemical and computational biology problems; recently, however, clinical and medical quantum solutions have drawn increasing interest. The rapid emergence of quantum computing in health and medicine necessitates a mapping of the landscape. In this review, clinical and medical proof-of-concept quantum computing applications are outlined and put into perspective. These consist of over 40 experimental and theoretical studies. The use case areas span genomics, clinical research and discovery, diagnostics, and treatments and interventions. Quantum machine learning (QML) in particular has rapidly evolved and shown to be competitive with classical benchmarks in recent medical research. Near-term QML algorithms have been trained with diverse clinical and real-world data sets. This includes studies in generating new molecular entities as drug candidates, diagnosing based on medical image classification, predicting patient pe

The influence of a uniform external magnetic field on the dynamical spin response of cuprate superconductors in the superconducting state is studied based on the kinetic energy driven superconducting mechanism. It is shown that the magnetic scattering around low and intermediate energies is dramatically changed with a modest external magnetic field. With increasing the external magnetic field, although the incommensurate magnetic scattering from both low and high energies is rather robust, the commensurate magnetic resonance scattering peak is broadened. The part of the spin excitation dispersion seems to be an hourglass-like dispersion, which breaks down at the heavily low energy regime. The theory also predicts that the commensurate resonance scattering at zero external magnetic field is induced into the incommensurate resonance scattering by applying an external magnetic field large enough.

In this work, we study thermally-generated spin current in the system consisting of a quantum dot connected to two magnetic insulators. The external leads are kept at different temperatures which leads to an imbalance of magnon populations in two magnetic insulators resulting in the flow of the magnon (spin) current. We take into account many-body magnon interactions and incorporate energy-dependent density of states of the magnetic insulators. Both features can strongly affect magnon distribution in the magnetic insulators and the coupling strengths between the leads and the dot, and thus, the thermally generated spin current. All the calculations are carried out in the weak coupling regime. We show, that results obtained with a density of states being a function of energy differ significantly from the ones obtained with a density of states taken as a constant. In turn, magnon interactions in the leads proved to be important at high temperatures and large values of energy of transported spin waves.

The field of research on magnetic van der Waals compounds -- a special subclass of quasi-two-dimensional materials -- is currently rapidly expanding due to the relevance of these compounds to fundamental research where they serve as a playground for the investigation of different models of quantum magnetism and also in view of their unique magneto-electronic and magneto-optical properties pertinent to novel technological applications. The Electron Spin Resonance (ESR) spectroscopy plays an important role in the exploration of the rich magnetic behavior of van der Waals compounds due to its high sensitivity to magnetic anisotropies and unprecedentedly high energy resolution that altogether enable one to obtain thorough insights into the details of the spin structure in the magnetically ordered state and the low-energy spin dynamics in the ordered and paramagnetic phases. This article provides an overview of the recent achievements in this field made by the ESR spectroscopic techniques encompassing representatives of antiferro- and ferromagnetic van der Waals compounds of different crystal structures and chemical composition as well as of a special category of these materials termed

The success of precision medicine requires computational models that can effectively process and interpret diverse physiological signals across heterogeneous patient populations. While foundation models have demonstrated remarkable transfer capabilities across various domains, their effectiveness in handling individual-specific physiological signals - crucial for precision medicine - remains largely unexplored. This work introduces a systematic pipeline for rapidly and efficiently evaluating foundation models' transfer capabilities in medical contexts. Our pipeline employs a three-stage approach. First, it leverages physiological simulation software to generate diverse, clinically relevant scenarios, particularly focusing on data-scarce medical conditions. This simulation-based approach enables both targeted capability assessment and subsequent model fine-tuning. Second, the pipeline projects these simulated signals through the foundation model to obtain embeddings, which are then evaluated using linear methods. This evaluation quantifies the model's ability to capture three critical aspects: physiological feature independence, temporal dynamics preservation, and medical scenario d

Robust and homogeneous lipid suppression is mandatory for coronary magnetic resonance angiography (MRA) since coronary arteries are commonly embedded in fat. However, effective large volume lipid suppression becomes challenging when performing radial whole-heart coronary MRA and the problem may even be exacerbated at increasing magnetic field strengths. Incomplete fat suppression also generates artifacts, and may affect advanced motion correction methods. The aim was to evaluate a recently reported lipid insensitive MRI method for self-navigated coronary MRA at 3T. Lipid insensitive binomial off resonant excitation (LIBRE) radiofrequency (RF) excitation pulses were included into a self-navigated 3D radial GRE coronary MRA sequence at 3T. LIBRE was compared against conventional fat saturation (FS) and binomial 1-180°-1 water excitation (WE). First, fat suppression of all techniques was numerically characterized using Matlab and experimentally validated in phantoms and in legs of human volunteers. Subsequently, free-breathing self-navigated coronary MRA was performed using the LIBRE pulse as well as FS and WE in ten volunteers. Results obtained in the simulations were confirmed by th

Different approaches have improved the sensitivity of either electron or nuclear magnetic resonance to the single spin level. For optical detection it has essentially become routine to observe a single electron spin or nuclear spin. Typically, the systems in use are carefully designed to allow for single spin detection and manipulation, and of those systems, diamond spin defects rank very high, being so robust that they can be addressed, read out and coherently controlled even under ambient conditions and in a versatile set of nanostructures. This renders them as a new type of sensor, which has been shown to detect single electron and nuclear spins among other quantities like force, pressure and temperature. Adapting pulse sequences from classic NMR and EPR, and combined with high resolution optical microscopy, proximity to the target sample and nanoscale size, the diamond sensors have the potential to constitute a new class of magnetic resonance detectors with single spin sensitivity. As diamond sensors can be operated under ambient conditions, they offer potential application across a multitude of disciplines. Here we review the different existing techniques for magnetic resonanc

In this article, we present an up-to-date overview of the potential biomedical applications of sodium MRI in vivo. Sodium MRI is a subject of increasing interest in translational research as it can give some direct and quantitative biochemical information on the tissue viability, cell integrity and function, and therefore not only help the diagnosis but also the prognosis of diseases and treatment outcomes. It has already been applied in vivo in most of human tissues, such as brain for stroke or tumor detection and therapeutic response, in breast cancer, in articular cartilage, in muscle and in kidney, and it was shown in some studies that it could provide very useful new information not available through standard proton MRI. However, this technique is still very challenging due to the low detectable sodium signal in biological tissue with MRI and hardware/software limitations of the clinical scanners. The article is divided in three parts: (1) the role of sodium in biological tissues, (2) a short review on sodium magnetic resonance, and (3) a review of some studies on sodium MRI on different organs/diseases to date.

This review is a compilation of relevant concepts in designing Halbach multipoles for magnetic resonance applications. The main focus is on providing practical guidelines to plan, design and build such magnets. Therefore, analytical equations are presented for estimating the magnetic field from ideal to realistic systems. Various strategies of homogenizing magnetic fields are discussed together with concepts of opening such magnets without force, or combining them for variable fields. Temperature compensation and other practical aspects are also reviewed. For magnetic resonance two polarities (di- and quadrupole) are of main interest, but higher polarities are also included.

In this work, we study the rotating magnetic field driven domain wall (DW) motion in antiferromagnetic nanowires, using the micromagnetic simulations of the classical Heisenberg spin model. We show that in low frequency region, the rotating field alone could efficiently drive the DW motion even in the absence of Dzyaloshinskii-Moriya interaction (DMI). In this case, the DW rotates synchronously with the magnetic field, and a stable precession torque is available and drives the DW motion with a steady velocity. In large frequency region, the DW only oscillates around its equilibrium position and cannot propagate. The dependences of the velocity and critical frequency differentiating the two motion modes on several parameters are investigated in details, and the direction of the DW motion can be controlled by modulating the initial phase of the field. Interestingly, a unidirectional DW motion is predicted attributing to the bulk DMI, and the nonzero velocity for high frequency is well explained. Thus, this work does provide useful information for further antiferromagnetic spintronics applications.

The availability of compact, low-cost magnetic resonance imaging instruments would further broaden the substantial impact of this technology. We report highly sensitive detection of magnetic resonance using low-stress silicon nitride (SiN$_x$) membranes. We use these membranes as low-loss, high-frequency mechanical oscillators and find they are able to mechanically detect spin-dependent forces with high sensitivity enabling ultrasensitive magnetic resonance detection. The high force detection sensitivity stems from their high mechanical quality factor $Q\sim10^6$ combined with the low mass of the resonator. We use this excellent mechanical force sensitivity to detect the electron spin magnetic resonance using a SiN$_x$ membrane as a force detector. The demonstrated force sensitivity at 300 K is 4 fN/$\sqrt{\mathrm{Hz}}$, indicating a potential low temperature (4 K) sensitivity of 25 aN/$\sqrt{\mathrm{Hz}}$. Given their sensitivity, robust construction, large surface area and low cost, SiN$_x$ membranes can potentially serve as the central component of a compact room-temperature ESR and NMR instrument that has superior spatial resolution to conventional approaches.

PURPOSE: Magnetic Resonance Fingerprinting (MRF) with spiral readout enables rapid quantification of tissue relaxation times. However, it is prone to blurring due to off-resonance effects. Hence, fat blurring into adjacent regions might prevent identification of small tumors by their quantitative T1 and T2 values. This study aims to correct for the blurring artifacts, thereby enabling fast quantitative mapping in the female breast. METHODS: The impact of fat blurring on spiral MRF results was first assessed by simulations. Then, MRF was combined with 3-point Dixon water-fat separation and spiral blurring correction based on conjugate phase reconstruction. The approach was assessed in phantom experiments and compared to Cartesian reference measurements, namely inversion recovery (IR), multi-echo spin echo (MESE) and Cartesian MRF, by normalized root mean square error (NRMSE) and standard deviation (STD) calculations. Feasibility is further demonstrated in-vivo for quantitative breast measurements of 6 healthy female volunteers, age range 24-31 years. RESULTS: In the phantom experiment, the blurring correction reduced the NRMSE per phantom vial on average from 16% to 8% for T1 and fr

MgMn$_6$Sn$_6$ is the itinerant ferromagnet on the kagome lattice with high ordering temperature featuring complex electronic properties due to the nontrivial topological electronic band structure, where the spin-orbit coupling (SOC) plays a crucial role. Here, we report a detailed ferromagnetic resonance (FMR) spectroscopic study of MgMn$_6$Sn$_6$ aimed to elucidate and quantify the intrinsic magnetocrystalline anisotropy that is responsible for the alignment of the Mn magnetic moments in the kagome plane. By analyzing the frequency, magnetic field, and temperature dependences of the FMR modes, we have quantified the magnetocrystalline anisotropy energy density that reaches the value of approximately $ 3.5\cdot 10^6$ erg/cm$^3$ at $T = 3$ K and reduces to about $1\cdot 10^6$ erg/cm$^3$ at $T = 300$ K. The revealed significantly strong magnetic anisotropy suggests a sizable contribution of the orbital magnetic moment to the spin magnetic moment of Mn, supporting the scenario of the essential role of SOC for the nontrivial electronic properties of MgMn$_6$Sn$_6$.

We demonstrate one-dimensional nuclear magnetic resonance imaging of the semiconductor GaAs with 170 nanometer slice separation and resolve two regions of reduced nuclear spin polarization density separated by only 500 nanometers. This is achieved by force detection of the magnetic resonance, Magnetic Resonance Force Microscopy (MRFM), in combination with optical pumping to increase the nuclear spin polarization. Optical pumping of the GaAs creates spin polarization up to 12 times larger than the thermal nuclear spin polarization at 5 K and 4 T. The experiment is sensitive to sample volumes containing $\sim 4 \times 10^{11}$ $^{71}$Ga$/\sqrt{Hz}$. These results demonstrate the ability of force-detected magnetic resonance to apply magnetic resonance imaging to semiconductor devices and other nanostructures.

The last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase current mathematical and statistical techniques that enable modelers to gain insight into (models of) gene regulation, and generate testable predictions. We introduce a range of modeling frameworks, but focus on ordinary differential equation (ODE) models since they remain the most widely used approach in systems biology and medicine and continue to offer great potential. We present methods for the analysis of a single model, comprising applications of standard dynamical systems approaches such as nondimensionalization, steady state, asymptotic and sensitivity analysis, and more recent statistical and algebraic approaches to compare models with data. We present parameter estimation and model comparison techniques, focusing on Bayesian analysis and coplanarity via algebraic geometry. Our intention is that this (non exhaustive) review may serve as a useful starting point for the analysis of models in systems medicine.

Iterative self-consistent parallel imaging reconstruction (SPIRiT) is an effective self-calibrated reconstruction model for parallel magnetic resonance imaging (PMRI). The joint L1 norm of wavelet coefficients and joint total variation (TV) regularization terms are incorporated into the SPIRiT model to improve the reconstruction performance. The simultaneous two-directional low-rankness (STDLR) in k-space data is incorporated into SPIRiT to realize improved reconstruction. Recent methods have exploited the nonlocal self-similarity (NSS) of images by imposing nonlocal low-rankness of similar patches to achieve a superior performance. To fully utilize both the NSS in Magnetic resonance (MR) images and calibration consistency in the k-space domain, we propose a nonlocal low-rank (NLR)-SPIRiT model by incorporating NLR regularization into the SPIRiT model. We apply the weighted nuclear norm (WNN) as a surrogate of the rank and employ the Nash equilibrium (NE) formulation and alternating direction method of multipliers (ADMM) to efficiently solve the NLR-SPIRiT model. The experimental results demonstrate the superior performance of NLR-SPIRiT over the state-of-the-art methods via three

We numerically solve the Liouville equation for the Tavis Cummings model of multiple spins coupled to a lossless single mode cavity, starting from an initial condition with small numbers of fully polarized spins tipped by a specified angle, and the cavity in its ground Fock state. Time evolution of the magnetizations and cavity states, following small to medium nutation by a classical field, yields a microscopic quantum mechanical picture of radiation damping in magnetic resonance, and the formation of the free induction signal, that is, the transfer of Zeeman energy, via spin coherence, to cavity coherence. Although the motion of the Bloch vector is nonclassical, our quantum description is related to the macroscopic picture of NMR reception, by showing the close relationship between the usual radiation damping constant, and the quantum mechanical Rabi nutation frequency (as enhanced by cavity coupling and stimulated emission.) That is, each is the product, of a nutation rate per oscillator current, and a current. Although the current in the damping constant is explicitly limited by cavity losses, which do not enter the formula for the Rabi frequency, we nonetheless show (in an app

Purpose: To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. Methods: NLINV-Net is a model-based neural network architecture that directly estimates images and coil sensitivities from (radial) k-space data via non-linear inversion (NLINV). Combined with a training strategy using self-supervision via data undersampling (SSDU), it can be used for imaging problems where no ground truth reconstructions are available. We validated the method for (1) real-time cardiac imaging and (2) single-shot subspace-based quantitative T1 mapping. Furthermore, region-optimized virtual (ROVir) coils were used to suppress artifacts stemming from outside the FoV and to focus the k-space based SSDU loss on the region of interest. NLINV-Net based reconstructions were compared with conventional NLINV and PI-CS (parallel imaging + compressed sensing) reconstruction and the effect of the region-optimized virtual coils and the type of training loss was evaluated qualitatively. Results: NLINV-Net based reconstructions contain significantly less noise than the NLINV-based counterpart. ROVir coils effectively suppress