MRI

Modern magnetic resonance imaging (MRI) relies on application-specific multi-channel receive coils to achieve high performance, but these coils are typically costly, rigid, and difficult to generalize across anatomies. Recent wireless, low-cost metamaterials offer improved signal-to-noise ratio (SNR) but remain anatomy-dependent, are prone to destructive inter-element interference, and lack demonstrated compatibility with parallel imaging. Herein, a wireless, reconfigurable coaxial loop metasurface (CLM) is introduced as a platform for localized SNR enhancement that can operate either as a standalone element or as an insertable add-on alongside existing clinical receive systems. Through its coaxial architecture and shared current pathways, the CLM establishes a collective in-phase resonant mode that enforces phase-coherent current distributions across all loops, resulting in consistently constructive interference. Benchmarking on a 3.0 T MR system using an 8-loop CLM shows SNR enhancements of up to 14.8-fold and 14.02-fold in the sagittal and axial planes, relative to the birdcage coil (BC). As an add-on to a clinical posterior receive array, it further demonstrates up to 2.9-fold SNR enhancement and compatibility with parallel imaging across ex vivo and in vivo settings. The proposed CLM paves the way toward a new class of reconfigurable and insertable MRI hardware for flexible and system-compatible signal enhancement.

The signal-to-noise ratio (SNR) in magnetic resonance imaging (MRI) governs the quality of signal detection and directly impacts the clarity and reliability of the acquired images. Recent advances in metamaterials have enabled lightweight solutions with selective magnetic responses, offering a route to locally boost SNR in targeted anatomical regions but often with compromised field homogeneity. Here, a wireless metamaterial cage constructed from coaxial cables is engineered for homogeneous SNR enhancement at 3.0 T. With its cylindrical geometry and electromagnetic architecture, the device supports circularly polarized resonance through engineered phase-shifted currents, enabling selective and omnidirectional interaction with the rotating B₁^– field to achieve a uniform magnetic field distribution. Integrated with the Birdcage coil (BC), the device yields a 31.45-fold SNR enhancement while maintaining comparable homogeneity to the BC alone, exhibiting only 12.07% variation within the region of interest (ROI). Benchmarking against a state-of-the-art 16-channel extremity coil further shows that the metacage achieves at least 1.94-fold and 2.24-fold higher SNR in axial and coronal planes, respectively, and exhibits substantially lower SNR variation (12.07% compared to 54.83% for the extremity coil). The results establish the metacage as a compelling platform for next-generation wireless MRI technologies.

Magnetic resonance imaging (MRI) relies on high-performance receive coils to achieve optimal signal-to-noise ratio (SNR), but conventional designs are often bulky and complex. Recent advancements in metamaterial technology have led to the development of metamaterial-inspired receive coils that enhance imaging capabilities and offer design flexibility. However, these configurations typically face challenges related to reduced adaptability and increased physical footprint. This study introduces a hybrid receive coil design that integrates an array of capacitively-loaded ring resonators directly onto the same plane as the coil, preserving its 2D layout without increasing its size. Both the coil and metamaterial are individually non-resonant at the targeted Larmor frequency, but their mutual coupling induces a resonance shift, achieving a frequency match and forming a hybrid structure with enhanced SNR. Experimental validation on a 3.0 T MRI platform shows that this design allows for adjustable trade-offs between peak SNR and penetration depth, making it adaptable for various clinical imaging scenarios.

Metamaterials hold great potential to enhance the imaging performance of magnetic resonance imaging (MRI) as auxiliary devices, due to their unique ability to confine and enhance electromagnetic fields. Despite their promise, the current implementation of metamaterials faces obstacles for practical clinical adoption due to several notable limitations, including their bulky and rigid structures, deviations from optimal resonance frequency, and inevitable interference with the radiofrequency (RF) transmission field in MRI. Herein, we address these restrictions by introducing a flexible and smart metamaterial that enhances sensitivity by conforming to patient anatomies while ensuring comfort during MRI procedures. The proposed metamaterial selectively amplifies the magnetic field during the RF reception phase by passively sensing the excitation signal strength, remaining “off” during the RF transmission phase. Additionally, the metamaterial can be readily tuned to achieve a precise frequency match with the MRI system through a controlling circuit. The metamaterial presented here paves the way for the widespread utilization of metamaterials in clinical MRI, thereby translating this promising technology to the MRI bedside.

Anatomy-specific radio frequency receive coil arrays routinely adopted in magnetic resonance imaging (MRI) for signal acquisition are commonly burdened by their bulky, fixed, and rigid configurations, which may impose patient discomfort, bothersome positioning, and suboptimal sensitivity in certain situations. Herein, leveraging coaxial cables’ inherent flexibility and electric field confining property, we present wireless, ultralightweight, coaxially shielded, passive detuning MRI coils achieving a signal-to-noise ratio comparable to or surpassing that of commercially available cutting-edge receive coil arrays with the potential for improved patient comfort, ease of implementation, and substantially reduced costs. The proposed coils demonstrate versatility by functioning both independently in form-fitting configurations, closely adapting to relatively small anatomical sites, and collectively by inductively coupling together as metamaterials, allowing for extension of the field of view of their coverage to encompass larger anatomical regions without compromising coil sensitivity. The wireless, coaxially shielded MRI coils reported herein pave the way toward next-generation MRI coils.

Recent advancements in metamaterials have yielded the possibility of a wireless solution to improve signal-to-noise ratio (SNR) in magnetic resonance imaging (MRI). Unlike traditional closely packed local coil arrays with rigid designs and numerous components, these lightweight, cost-effective metamaterials eliminate the need for radio frequency cabling, baluns, adapters, and interfaces. However, their clinical adoption is limited by their low sensitivity, bulky physical footprint, and limited, specific use cases. Herein, a wearable metamaterial developed using commercially available coaxial cable, designed for a 3.0 T MRI system is introduced. This metamaterial inherits the coaxially-shielded structure of its constituent cable, confining the electric field within and mitigating coupling to its surroundings. This ensures safer clinical adoption, lower signal loss, and resistance to frequency shifts. Weighing only 50 g, the metamaterial maximizes its sensitivity by conforming to the anatomical region of interest. MRI images acquired using this metamaterial with various pulse sequences achieve an SNR comparable or even surpass that of a state-of-the-art 16-channel knee coil. This work introduces a novel paradigm for constructing metamaterials in the MRI environment, paving the way for the development of next-generation wireless MRI technology.

Metamaterials hold significant promise for enhancing the imaging capabilities of magnetic resonance imaging (MRI) machines as an additive technology, due to their unique ability to enhance local magnetic fields. However, despite their potential, the metamaterials reported in the context of MRI applications have often been impractical. This impracticality arises from their predominantly flat configurations and their susceptibility to shifts in resonance frequencies, preventing them from realizing their optimal performance. Here, a computational method for designing wearable and tunable metamaterials via freeform auxetics is introduced. The proposed computational-design tools yield an approach to solving the complex circle packing problems in an interactive and efficient manner, thus facilitating the development of deployable metamaterials configured in freeform shapes. With such tools, the developed metamaterials may readily conform to a patient’s knee, ankle, head, or any part of the body in need of imaging, and while ensuring an optimal resonance frequency, thereby paving the way for the widespread adoption of metamaterials in clinical MRI applications.

Signal-to-noise ratio (SNR) is one of the most common metrics in assessing the image quality of magnetic resonance imaging (MRI). Among a host of technological developments, various wireless devices, including metamaterials and volumetric wireless resonators have been reported to enhance SNR by redistributing the radio frequency magnetic field in the near field region. While theoretically feasible, their widespread clinical adoption has been limited by their field inhomogeneity, limited spatial coverage and challenges in their applications to higher field (≥3.0T) MRI systems. In this study, a Helmholtz coil-inspired volumetric wireless resonator (HVWR) featuring a uniform magnetic field enhancement within the resonator volume is reported. The HVWR is free from cables, adapters and interface boxes, allowing for ease of fabrication and straightforward installation. The resonator allows for resonance frequency tunability and adaptivity, enabling for passive detuning during the MRI transmission phase. Experimental validation using a 3.0T MRI system demonstrate a substantial SNR boost (5× or higher) being achieved in a region covering the average size of the human knee. This study offers an efficient and practical wireless solution for improved MRI image quality that may be applicable across a range of imaging applications.

Auxetics refers to structures or materials with a negative Poisson’s ratio, thereby capable of exhibiting counterintuitive behaviors. Herein, auxetic structures are exploited to design mechanically tunable metamaterials in both planar and hemispherical configurations operating at megahertz (MHz) frequencies, optimized for their application to magnetic resonance imaging (MRI). Specially, the reported tunable metamaterials are composed of arrays of interjointed unit cells featuring metallic helices, enabling auxetic patterns with a negative Poisson’s ratio. The deployable deformation of the metamaterials yields an added degree of freedom with respect to frequency tunability through the resultant modification of the electromagnetic interactions between unit cells. The metamaterials are fabricated using 3D printing technology and an ≈20 MHz frequency shift of the resonance mode is enabled during deformation. Experimental validation is performed in a clinical (3.0 T) MRI system, demonstrating that the metamaterials enable a marked boost in radiofrequency field strength under resonance-matched conditions, ultimately yielding a dramatic increase in the signal-to-noise ratio (≈4.5×) of MRI. The tunable metamaterials presented herein offer a novel pathway toward the practical utilization of metamaterials in MRI, as well as a range of other emerging applications.

Breaking Lorentz reciprocity is fundamental to an array of functional radiofrequency (RF) and optical devices, such as isolators and circulators. The application of external excitation, such as magnetic fields and spatial–temporal modulation, has been employed to achieve nonreciprocal responses. Alternatively, nonlinear effects may also be employed to break reciprocity in a completely passive fashion. Herein, a coupled system comprised of linear and nonlinear meta-atoms that achieves nonreciprocity based on the coupling and frequency detuning of its constituent meta-atoms is presented. An analytical model is developed based on the coupled mode theory (CMT) in order to design and optimize the nonreciprocal meta-atoms in this coupled system. Experimental demonstration of an RF isolator is performed, and the contrast between forward and backward propagation approximates 20 dB. Importantly, the use of the CMT model developed herein enables a generalizable capacity to predict the limitations of nonlinearity-based nonreciprocity, thereby facilitating the development of novel approaches to breaking Lorentz reciprocity. The CMT model and implementation scheme presented in this work may be deployed in a wide range of applications, including integrated photonic circuits, optical metamaterials, and metasurfaces, among others.

Metamaterials provide a powerful platform to probe and enhance nonlinear responses in physical systems toward myriad applications. Herein, the development of a coupled nonlinear metamaterial (NLMM) featuring a self-adaptive response that selectively amplifies the magnetic field is reported. The resonance of the NLMM is suppressed in response to higher degrees of radio-frequency excitation strength and recovers during a subsequent low excitation strength phase, thereby exhibiting an intelligent, or nonlinear, behavior by passively sensing excitation signal strength and responding accordingly. The nonlinear response of the NLMM enables us to boost the signal-to-noise ratio during magnetic resonance imaging to an unprecedented degree. These results provide insights into a new paradigm to construct NLMMs consisting of coupled resonators and pave the way toward the utilization of NLMMs to address a host of practical technological applications.

Magnetic resonance imaging (MRI) represents a mainstay among the diagnostic imaging tools in modern healthcare. Signal-to-noise ratio (SNR) represents a fundamental performance metric of MRI, the improvement of which may be translated into increased image resolution or decreased scan time. Recently, efforts towards the application of metamaterials in MRI have reported improvements in SNR through their capacity to interact with electromagnetic radiation. While promising, the reported applications of metamaterials to MRI remain impractical and fail to realize the full potential of these unique materials. Here, we report the development of a magnetic metamaterial enabling a marked boost in radio frequency field strength, ultimately yielding a dramatic increase in the SNR (~ 4.2×) of MRI. The application of the reported magnetic metamaterials in MRI has the potential for rapid clinical translation, offering marked enhancements in SNR, image resolution, and scan efficiency, thereby leading to an evolution of this diagnostic tool.

Introduction: Transformer-based deep learning has shown great potential in medical imaging, but its real-world applicability remains limited due to the scarcity of annotated data. This study aims to develop a practical framework for the few-shot deployment of pretrained MRI transformers across diverse brain imaging tasks. Methods: We employ a Masked Autoencoder (MAE) pretraining strategy on a large-scale, multi-cohort brain MRI dataset comprising over 31 million 2D slices to learn transferable representations. For classification tasks, a frozen MAE encoder with a lightweight linear head (MAE-classify) is used. For segmentation, we propose MAE-FUnet, a hybrid architecture that fuses pretrained MAE embeddings with multi-scale CNN features. Extensive evaluations are conducted on multiple datasets, including NACC, ADNI, OASIS, NFBS, SynthStrip, and MRBrainS18, under controlled few-shot settings. Results: The proposed framework achieves state-of-the-art performance in MRI sequence classification, reaching an accuracy of 99.24% with only 6,152 trainable parameters. For segmentation tasks, MAE-FUnet consistently outperforms strong baselines, achieving superior Dice and IoU scores across skull stripping and multi-class anatomical segmentation benchmarks. The model also demonstrates enhanced robustness and stability under data-limited conditions, with lower performance variance compared to competing methods. Discussion: These results highlight the effectiveness of pretrained MAE representations for few-shot medical imaging tasks. The proposed framework enables efficient, scalable, and adaptable deployment of transformer-based models in data-constrained clinical environments. The fusion of global transformer embeddings with local CNN features provides a generalizable design paradigm for a wide range of medical imaging applications.

Recent advancements in deep learning have enabled the development of generalizable models that achieve state-of-the-art performance across various imaging tasks. Vision Transformer (ViT)-based architectures, in particular, have demonstrated strong feature extraction capabilities when pre-trained on large-scale datasets. In this work, we introduce the Magnetic Resonance Image Processing Transformer (MR-IPT), a ViT-based image-domain framework designed to enhance the generalizability and robustness of accelerated MRI restoration. Unlike conventional deep learning models that require separate training for different acceleration factors, MR-IPT is pre-trained on a large-scale dataset encompassing multiple undersampling patterns and acceleration settings, enabling a unified framework. By leveraging a shared transformer backbone, MR-IPT effectively learns universal feature representations, allowing it to generalize across diverse restoration tasks. Extensive experiments demonstrate that MR-IPT outperforms both CNN-based and existing transformer-based methods, achieving superior quality across varying acceleration factors and sampling masks. Moreover, MR-IPT exhibits strong robustness, maintaining high performance even under unseen acquisition setups, highlighting its potential as a scalable and efficient solution for accelerated MRI. Our findings suggest that transformer-based general models can significantly advance MRI restoration, offering improved adaptability and stability compared to traditional deep learning approaches.

Recent advances in MRI reconstruction have demonstrated remarkable success through deep learning-based models. However, most existing methods rely heavily on large-scale, task-specific datasets, making reconstruction in data-limited settings a critical yet underexplored challenge. While regularization by denoising (RED) leverages denoisers as priors for reconstruction, we propose Regularization by Neural Style Transfer (RNST), a novel framework that integrates a neural style transfer (NST) engine with a denoiser to enable magnetic field-transfer reconstruction. RNST generates high-field-quality images from low-field inputs without requiring paired training data, leveraging style priors to address limited-data settings. Our experiment results demonstrate RNST’s ability to reconstruct high-quality images across diverse anatomical planes (axial, coronal, sagittal) and noise levels, achieving superior clarity, contrast, and structural fidelity compared to lower-field references. Crucially, RNST maintains robustness even when style and content images lack exact alignment, broadening its applicability in clinical environments where precise reference matches are unavailable. By combining the strengths of NST and denoising, RNST offers a scalable, data-efficient solution for MRI field-transfer reconstruction, demonstrating significant potential for resource-limited settings.

Deep learning-based MRI reconstruction models have achieved superior performance these days. Most recently, diffusion models have shown remarkable performance in image generation, in-painting, super-resolution, image editing and more. As a generalized diffusion model, cold diffusion further broadens the scope and considers models built around arbitrary image transformations such as blurring, down-sampling, etc. In this paper, we propose a k-space cold diffusion model that performs image degradation and restoration in k-space without the need for Gaussian noise. We provide comparisons with multiple deep learning-based MRI reconstruction models and perform tests on a well-known large open-source MRI dataset. Our results show that this novel way of performing degradation can generate high-quality reconstruction images for accelerated MRI.

The application of compressed sensing (CS)-enabled data reconstruction for accelerating magnetic resonance imaging (MRI) remains a challenging problem. This is due to the fact that the information lost in k-space from the acceleration mask makes it difficult to reconstruct an image similar to the quality of a fully sampled image. Multiple deep learning-based structures have been proposed for MRI reconstruction using CS, in both the k-space and image domains, and using unrolled optimization methods. However, the drawback of these structures is that they are not fully utilizing the information from both domains (k-space and image). Herein, we propose a deep learning-based attention hybrid variational network that performs learning in both the k-space and image domains. We evaluate our method on a well-known open-source MRI dataset (652 brain cases and 1172 knee cases) and a clinical MRI dataset of 243 patients diagnosed with strokes from our institution to demonstrate the performance of our network. Our model achieves an overall peak signal-to-noise ratio/structural similarity of 40.92 ± 0.29/0.9577 ± 0.0025 (fourfold) and 37.03 ± 0.25/0.9365 ± 0.0029 (eightfold) for the brain dataset, 31.09 ± 0.25/0.6901 ± 0.0094 (fourfold) and 29.49 ± 0.22/0.6197 ± 0.0106 (eightfold) for the knee dataset, and 36.32 ± 0.16/0.9199 ± 0.0029 (20-fold) and 33.70 ± 0.15/0.8882 ± 0.0035 (30-fold) for the stroke dataset. In addition to quantitative evaluation, we undertook a blinded comparison of image quality across networks performed by a subspecialty trained radiologist. Overall, we demonstrate that our network achieves a superior performance among others under multiple reconstruction tasks.