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.
Coupled nonlinear metamaterials, featuring a self‐adaptive, or intelligent, response that selectively amplifies the magnetic field, are harnessed to enhance the magnetic field for lower radio‐frequency energy excitation and suppress its resonance for higher energy excitation. These intelligent metamaterials serve to enhance the signal‐to‐noise ratio of magnetic resonance imaging by more than tenfold.
The morbidity, mortality, and economic costs resulting from trauma in general, and blunt abdominal trauma in particular, are substantial. The "panscan" (computed tomographic CT examination of the ...head, neck, chest, abdomen, and pelvis) has become an essential element in the early evaluation and decision-making algorithm for hemodynamically stable patients who sustained abdominal trauma. CT has virtually replaced diagnostic peritoneal lavage for the detection of important injuries. Over the past decade, substantial hardware and software developments in CT technology, especially the introduction and refinement of multidetector scanners, have expanded the versatility of CT for examination of the polytrauma patient in multiple facets: higher spatial resolution, faster image acquisition and reconstruction, and improved patient safety (optimization of radiation delivery methods). In this article, the authors review the elements of multidetector CT technique that are currently relevant for evaluating blunt abdominal trauma and describe the most important CT signs of trauma in the various organs. Because conservative nonsurgical therapy is preferred for all but the most severe injuries affecting the solid viscera, the authors emphasize the CT findings that are indications for direct therapeutic intervention.
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.
Purpose
To evaluate the utility of texture analysis for the differentiation of renal tumors, including the various renal cell carcinoma subtypes and oncocytoma.
Materials and methods
Following IRB ...approval, a retrospective analysis was performed, including all patients with pathology-proven renal tumors and an abdominal computed tomography (CT) examination. CT images of the tumors were manually segmented, and texture analysis of the segmented tumors was performed. A support vector machine (SVM) method was also applied to classify tumor types. Texture analysis results were compared to the various tumors and areas under the curve (AUC) were calculated. Similar calculations were performed with the SVM data.
Results
One hundred nineteen patients were included. Excellent discriminators of tumors were identified among the histogram-based features noting features skewness and kurtosis, which demonstrated AUCs of 0.91 and 0.93 (
p
< 0.0001), respectively, for differentiating clear cell subtype from oncocytoma. Histogram feature median demonstrated an AUC of 0.99 (
p
< 0.0001) for differentiating papillary subtype from oncocytoma and an AUC of 0.92 for differentiating oncocytoma from other tumors. Machine learning further improved the results achieving very good to excellent discrimination of tumor subtypes. The ability of machine learning to distinguish clear cell subtype from other tumors and papillary subtype from other tumors was excellent with AUCs of 0.91 and 0.92, respectively.
Conclusion
Texture analysis is a promising non-invasive tool for distinguishing renal tumors on CT images. These results were further improved upon application of machine learning, and support the further development of texture analysis as a quantitative biomarker for distinguishing various renal tumors.
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.
Auxetics refers to structures or materials identified by a negative Poisson's ratio and exhibiting a counterintuitive geometrical behavior. Inspired by auxetics, mechanically tunable magnetic metamaterials in both planar and hemispherical configurations operating at megahertz frequencies are proposed, which serve to enhance local magnetic fields and increase signal‐to‐noise ratio (≈4.5×) in their application to magnetic resonance imaging.
A central goal of modern magnetic resonance imaging (MRI) is to reduce the time required to produce high-quality images. Efforts have included hardware and software innovations such as parallel ...imaging, compressed sensing, and deep learning-based reconstruction. Here, we propose and demonstrate a Bayesian method to build statistical libraries of magnetic resonance (MR) images in k-space and use these libraries to identify optimal subsampling paths and reconstruction processes. Specifically, we compute a multivariate normal distribution based upon Gaussian processes using a publicly available library of T1-weighted images of healthy brains. We combine this library with physics-informed envelope functions to only retain meaningful correlations in k-space. This covariance function is then used to select a series of ring-shaped subsampling paths using Bayesian optimization such that they optimally explore space while remaining practically realizable in commercial MRI systems. Combining optimized subsampling paths found for a range of images, we compute a generalized sampling path that, when used for novel images, produces superlative structural similarity and error in comparison to previously reported reconstruction processes (i.e. 96.3% structural similarity and < 0.003 normalized mean squared error from sampling only 12.5% of the k-space data). Finally, we use this reconstruction process on pathological data without retraining to show that reconstructed images are clinically useful for stroke identification. Since the model trained on images of healthy brains could be directly used for predictions in pathological brains without retraining, it shows the inherent transferability of this approach and opens doors to its widespread use.
The purpose of this study was to determine the quantification accuracy of virtual unenhanced images and establish the lower limit of iodine quantification as a function of dose.
A large elliptical ...and cylindric phantom mimicking the patient abdomen was scanned on two commercial dual-energy CT scanners, an IQon Spectral CT (Philips Healthcare) and a Revolution CT with Gemstone Spectral Imaging Xtream suite (GE Healthcare). The phantom contained simulated soft tissue, blood, and bone with known elemental composition. It also contained simulated iodine concentrations (0.2-15.0 mg/mL) and iodine-enhanced blood (0.5-5.0 mg/mL). The mean absolute error in CT value for virtual unenhanced images and mean absolute percent error in iodine, calcium, and fat-specific images were measured.
For virtual unenhanced images, when excluding the simulated bone, the mean absolute error in CT value was 8.0 ± 5.0 (SD) HU and 9.0 ± 6.2 HU for the IQon and the Revolution CT, respectively (
= 0.61). The mean error in CT value of the simulated bone was -90.5 ± 111.6 HU and -98.5 ± 117.8 HU on the IQon and the Revolution CT, respectively (
= 0.08). For iodine-specific images, the mean absolute percent error was 13.7% and 8.3% for the IQon and the Revolution CT, respectively, above 0.5 mg/mL iodine concentration, and 150% and 100% at less than 0.5 mg/mL iodine concentration. The mean absolute percent error increased from 16.2% at 100% radiation dose to 18.9% and 24% at 75% and 50% dose, respectively, on the IQon; and from 8.8% at 100% dose to 11.1% and 17.8% at 75% and 50%, respectively, on the Revolution CT.
Virtual unenhanced images are reasonably accurate for simulated soft tissues and contrast materials, except for simulated bone. The lower limit of iodine quantification is radiation-dose dependent. For typical dose levels, 0.5 mg/mL iodine concentration is the lower threshold for iodine detection accuracy.
Detecting low energy photons, such as photons in the long-wave infrared range, is a technically challenging proposition using naturally occurring materials. In order to address this challenge, we ...herein demonstrate a micro-bolometer featuring an integrated metamaterial absorber (MA), which takes advantage of the resonant absorption and frequency selective properties of the MA. Importantly, our micro-bolometer exhibits polarization insensitivity and high absorption due to a novel metal-insulator-metal (MIM) absorber design, operating at 8-12 µm wavelength. The metamaterial structures we report herein feature an interconnected design, optimized towards their application to micro-bolometer-based, long-wave infrared detection. The micro-bolometers were fabricated using a combination of conventional photolithography and electron beam lithography (EBL), the latter owing to the small feature sizes within the design. The absorption response was designed using the coupled mode theory (CMT) and the finite integration technique, with the fabricated devices characterized using Fourier-transform infrared spectroscopy (FTIR). The metamaterial-based micro-bolometer exhibits a responsivity of approximately 198 V/W over the 8-12 µm wavelength regime, detectivity of ∼ 0.6 × 10
9
Jones, thermal response time of ∼ 3.3 ms, and a noise equivalent temperature difference (NETD) of ∼33 mK under 1mA biasing current at room-temperature and atmosphere pressure. The ultimate detectivity and NETD are limited by Johnson noise and heat loss with thermal convection through air; however, further optimization could be achieved by reducing the thermal conductivity via vacuum packaging. Under vacuum conditions, the detectivity may be increased in excess of two-fold, to ∼ 1.5 × 10
9
Jones. Finally, an infrared image of a soldering iron was generated using a single-pixel imaging process, serving as proof-of-concept of this detection platform. The results presented in this work pave the road towards high-efficiency and frequency-selective detection in the long-wave infrared range through the integration of infrared MAs with micro-bolometers.
Abstract The purpose of this study was to evaluate the potential utility of texture analysis of parametric apparent diffusion coefficient (ADC) maps in quantifying hepatic fibrosis. To this end, ...using ex vivo murine liver tissues from a dietary model of hepatic fibrosis, an array of texture analysis techniques, including histogram-based, gray-level co-occurrence matrix-based, and gray-level run-length-based features, was used to evaluate correlations with liver fibrosis. Moderate to very strong correlation between several of the texture-based features and both subjective as well as digital image analysis-based assessments of hepatic fibrosis was demonstrated. This rigorous study of texture analysis applied to parametric ADC maps in a liver fibrosis model study demonstrates and validates the potential utility of texture-based features for the noninvasive, quantitative assessment of hepatic fibrosis.
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