Phase-resolved Images Research Articles

Ferumoxytol-Enhanced 5D Multiphase Steady-State Imaging Using Rotating Cartesian K-Space With Low-Rank Reconstruction for Pediatric Congenital Heart Disease.

The rotating Cartesian k-space multiphase steady-state imaging with contrast (ROCK-MUSIC) pulse sequence enables acquisition of whole-heart, cardiac phase-resolved images in pediatric congenital heart disease (CHD) without reliance on the ventilator gating signal. Multidimensional reconstruction with low rank tensor (LRT) has shown promise for resolving complex cardiorespiratory motion. To enhance ROCK-MUSIC by resolving cardiorespiratory phases using LRT reconstruction and to enable semi-automatic hyperparameter tuning by developing an image quality scoring model. Retrospective. Thirty patients (45% female, age 2 days to 6.7 years) with CHD. 3-T, four-dimensional (4D) spoiled gradient recalled echo sequence. Eigenvector-based iTerative Self-consistent Parallel Imaging Reconstruction (ESPIRiT) served as the reference comparison for LRT reconstruction. A 4-point Likert scale was used for cardiac and vascular image quality scoring based on cardiac chamber definition, lumen signal uniformity, vascular margin clarity, and motion artifact. Ejection fraction and ventricular volumes were assessed in 16 patients. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness were computed. Intraclass correlation coefficients, Wilcoxon signed-rank test, Bland-Altman. A P-value <0.05 was considered statistically significant. Relative to ESPIRiT, LRT images received significantly higher cardiac (2.81 ± 0.57 vs. 3.19 ± 0.54) and vascular (2.81 ± 0.60 vs. 3.36 ± 0.53) image quality scores. Image quality scoring with semi-automated hyperparameter tuning showed strong correlations (R2 = 0.748) among image quality, SNR, and septal sharpness. Comparison of ejection fraction and volumetry derived from ESPIRiT, and LRT showed no significant systematic difference (P = 0.32). Integration of low-rank reconstruction with ROCK-MUSIC acquisition may be feasible, and semi-automatic hyperparameter tuning could be effective for generating cardiorespiratory resolved images. 2 TECHNICAL EFFICACY: Stage 1.

Multi-wavelength interference phase imaging for automatic breast cancer detection and delineation using diffuse reflection imaging

Millions of women globally are impacted by the major health problem of breast cancer (BC). Early detection of BC is critical for successful treatment and improved survival rates. In this study, we provide a progressive approach for BC detection using multi-wavelength interference (MWI) phase imaging based on diffuse reflection hyperspectral (HS) imaging. The proposed findings are based on the measurement of the interference pattern between the blue (446.6 nm) and red (632 nm) wavelengths. We consider implementing a comprehensive image processing and categorization method based on the use of Fast Fourier (FF) transform analysis pertaining to a change in the refractive index between tumor and normal tissue. We observed that cancer growth affects tissue organization dramatically, as seen by persistently increased refractive index variance in tumors compared normal areas. Both malignant and normal tissue had different depth data collected from it that was analyzed. To enhance the categorization of ex-vivo BC tissue, we developed and validated a training classifier algorithm specifically designed for categorizing HS cube data. Following the application of signal normalization with the FF transform algorithm, our methodology achieved a high level of performance with a specificity (Spec) of 94% and a sensitivity (Sen) of 90.9% for the 632 nm acquired image categorization, based on preliminary findings from breast specimens under investigation. Notably, we successfully leveraged unstained tissue samples to create 3D phase-resolved images that effectively highlight the distinctions in diffuse reflectance features between cancerous and healthy tissue. Preliminary data revealed that our imaging method might be able to assist specialists in safely excising malignant areas and assessing the tumor bed following resection automatically at different depths. This preliminary investigation might result in an effective "in-vivo" disease description utilizing optical technology using a typical RGB camera with wavelength-specific operation with our quantitative phase MWI imaging methodology.

Motion-compensated low-rank reconstruction for simultaneous structural and functional UTE lung MRI.

Three-dimensional UTE MRI has shown the ability to provide simultaneous structural and functional lung imaging, but it is limited by respiratory motion and relatively low lung parenchyma SNR. The purpose of this paper is to improve this imaging by using a respiratory phase-resolved reconstruction approach, named motion-compensated low-rank reconstruction (MoCoLoR), which directly incorporates motion compensation into a low-rank constrained reconstruction model for highly efficient use of the acquired data. The MoCoLoR reconstruction is formulated as an optimization problem that includes a low-rank constraint using estimated motion fields to reduce the rank, optimizing over both the motion fields and reconstructed images. The proposed reconstruction along with XD and motion state-weighted motion-compensation (MostMoCo) methods were applied to 18 lung MRI scans of pediatric and young adult patients. The data sets were acquired under free-breathing and without sedation with 3D radial UTE sequences in approximately 5 min. After reconstruction, they went through ventilation analyses. Performance across reconstruction regularization and motion-state parameters were also investigated. The in vivo experiments results showed that MoCoLoR made efficient use of the data, provided higher apparent SNR compared with state-of-the-art XD reconstruction and MostMoCo reconstructions, and yielded high-quality respiratory phase-resolved images for ventilation mapping. The method was effective across the range of patients scanned. The motion-compensated low-rank regularized reconstruction approach makes efficient use of acquired data and can improve simultaneous structural and functional lung imaging with 3D-UTE MRI. It enables the scanning of pediatric patients under free-breathing and without sedation.

Magnetic field modulated dynamics of partial discharges in defects of high voltage insulating materials

This paper presents original measurement methodology and detection approach to determine the influence of the magnetic field on the partial discharge (PD) dynamics. The application areas refer to insulation systems of both grid and industrial network devices and emerging segments such as high-speed rail, electric vehicles or more electric aircrafts. Conventionally PD measurements are executed only in electric field, however the interaction of magnetic and electric fields influences the dynamics of PDs. The measurement technique allowed to detect quantitatively the effect of magnetic fields on PDs in two representative arrangements: in gaseous void in dielectric material and in corona point-plane setup. Measurements in both configurations have revealed amplification of PDs intensity. The quantitative comparison of PDs evolution in the magnetic field is a novel aspect shown in this paper. Combination of phase resolved images and time-sequence intensity diagrams obtained at magnetic field switching allowed to visualize and determine quantitatively this impact. This effect is attributed to the extension of the charged particle trajectory and the amplification of the electron energy due to acceleration. Thus, the investigated impact of a magnetic field can be perceived as an additional element that influences the PD dynamics.

PRIOR: Prior-Regularized Iterative Optimization Reconstruction For 4D CBCT.

4D cone-beam computed tomography (CBCT) is an important imaging modality in image-guided radiation therapy to address the motion-induced artifacts caused by organ movements during the respiratory process. However, due to the extremely sparse projection data for each temporal phase, 4D CBCT reconstructions will suffer from severe streaking artifacts. Therefore, to tackle the streak artifacts and provide high-quality images, we proposed a framework termed Prior-Regularized Iterative Optimization Reconstruction (PRIOR) for 4D CBCT. The PRIOR framework combines the physics-based model and data-driven method simultaneously, with powerful feature extracting capacity, significantly promoting the image quality compared to single model-based or deep learning-based methods. Besides, we designed a specialized deep learning model named PRIOR-Net, which can effectively excavate the static information in the prior image reconstructed from the fully-sampled projections at the encoding stage to improve the reconstruction performance for individual phase-resolved images. Both the simulated and clinical 4D CBCT datasets were performed to evaluate the performance of the PRIOR-Net and the PRIOR framework. Compared with the advanced 4D CBCT reconstruction methods, the proposed methods achieve promising results quantitatively and qualitatively in streak artifact suppression, soft tissue restoration, and tiny detail preservation.

Cardiac phase-resolved late gadolinium enhancement imaging.

Late gadolinium enhancement (LGE) with cardiac magnetic resonance (CMR) imaging is the clinical reference for assessment of myocardial scar and focal fibrosis. However, current LGE techniques are confined to imaging of a single cardiac phase, which hampers assessment of scar motility and does not allow cross-comparison between multiple phases. In this work, we investigate a three step approach to obtain cardiac phase-resolved LGE images: (1) Acquisition of cardiac phase-resolved imaging data with varying T1 weighting. (2) Generation of semi-quantitative maps for each cardiac phase. (3) Synthetization of LGE contrast to obtain functional LGE images. The proposed method is evaluated in phantom imaging, six healthy subjects at 3T and 20 patients at 1.5T. Phantom imaging at 3T demonstrates consistent contrast throughout the cardiac cycle with a coefficient of variation of 2.55 ± 0.42%. In-vivo results show reliable LGE contrast with thorough suppression of the myocardial tissue is healthy subjects. The contrast between blood and myocardium showed moderate variation throughout the cardiac cycle in healthy subjects (coefficient of variation 18.2 ± 3.51%). Images were acquired at 40–60 ms and 80 ms temporal resolution, at 3T and 1.5, respectively. Functional LGE images acquired in patients with myocardial scar visualized scar tissue throughout the cardiac cycle, albeit at noticeably lower imaging resolution and noise resilience than the reference technique. The proposed technique bears the promise of integrating the advantages of phase-resolved CMR with LGE imaging, but further improvements in the acquisition quality are warranted for clinical use.

Patient-specific deep learning model to enhance 4D-CBCT image for radiomics analysis

Objective. 4D-CBCT provides phase-resolved images valuable for radiomics analysis for outcome prediction throughout treatment courses. However, 4D-CBCT suffers from streak artifacts caused by under-sampling, which severely degrades the accuracy of radiomic features. Previously we developed group-patient-trained deep learning methods to enhance the 4D-CBCT quality for radiomics analysis, which was not optimized for individual patients. In this study, a patient-specific model was developed to further improve the accuracy of 4D-CBCT based radiomics analysis for individual patients. Approach. This patient-specific model was trained with intra-patient data. Specifically, patient planning 4D-CT was augmented through image translation, rotation, and deformation to generate 305 CT volumes from 10 volumes to simulate possible patient positions during the onboard image acquisition. 72 projections were simulated from 4D-CT for each phase and were used to reconstruct 4D-CBCT using FDK back-projection algorithm. The patient-specific model was trained using these 305 paired sets of patient-specific 4D-CT and 4D-CBCT data to enhance the 4D-CBCT image to match with 4D-CT images as ground truth. For model testing, 4D-CBCT were simulated from a separate set of 4D-CT scan images acquired from the same patient and were then enhanced by this patient-specific model. Radiomics features were then extracted from the testing 4D-CT, 4D-CBCT, and enhanced 4D-CBCT image sets for comparison. The patient-specific model was tested using 4 lung-SBRT patients’ data and compared with the performance of the group-based model. The impact of model dimensionality, region of interest (ROI) selection, and loss function on the model accuracy was also investigated. Main results. Compared with a group-based model, the patient-specific training model further improved the accuracy of radiomic features, especially for features with large errors in the group-based model. For example, the 3D whole-body and ROI loss-based patient-specific model reduces the errors of the first-order median feature by 83.67%, the wavelet LLL feature maximum by 91.98%, and the wavelet HLL skewness feature by 15.0% on average for the four patients tested. In addition, the patient-specific models with different dimensionality (2D versus 3D) or loss functions (L1 versus L1 + VGG + GAN) achieved comparable results for improving the radiomics accuracy. Using whole-body or whole-body+ROI L1 loss for the model achieved better results than using the ROI L1 loss alone as the loss function. Significance. This study demonstrated that the patient-specific model is more effective than the group-based model on improving the accuracy of the 4D-CBCT radiomic features analysis, which could potentially improve the precision for outcome prediction in radiotherapy.

Spatiotemporal structure-aware dictionary learning-based 4D CBCT reconstruction.

Four-dimensional cone-beam computed tomography (4D CBCT) is developed to reconstruct a sequence of phase-resolved images, which could assist in verifying the patient's position and offering information for cancer treatment planning. However, 4D CBCT images suffer from severe streaking artifacts and noise due to the extreme sparse-view CT reconstruction problem for each phase. As a result, it would cause inaccuracy of treatment estimation. The purpose of this paper was to develop a new 4D CBCT reconstruction method to generate a series of high spatiotemporal 4D CBCT images. Considering the advantage of (DL) on representing structural features and correlation between neighboring pixels effectively, we construct a novel DL-based method for the 4D CBCT reconstruction. In this study, both a motion-aware dictionary and a spatially structural 2D dictionary are trained for 4D CBCT by excavating the spatiotemporal correlation among ten phase-resolved images and the spatial information in each image, respectively. Specifically, two reconstruction models are produced in this study. The first one is the motion-aware dictionary learning-based 4D CBCT algorithm, called motion-aware DL based 4D CBCT (MaDL). The second one is the MaDL equipped with a prior knowledge constraint, called pMaDL. Qualitative and quantitative evaluations are performed using a 4D extended cardiac torso (XCAT) phantom, simulated patient data, and two sets of patient data sets. Several state-of-the-art 4D CBCT algorithms, such as the McKinnon-Bates (MKB) algorithm, prior image constrained compressed sensing (PICCS), and the high-quality initial image-guided 4D CBCT reconstruction method (HQI-4DCBCT) are applied for comparison to validate the performance of the proposed MaDL and prior constraint MaDL (pMaDL) pmadl reconstruction frameworks. Experimental results validate that the proposed MaDL can output the reconstructions with few streaking artifacts but some structural information such as tumors and blood vessels, may still be missed. Meanwhile, the results of the proposed pMaDL demonstrate an improved spatiotemporal resolution of the reconstructed 4D CBCT images. In these improved 4D CBCT reconstructions, streaking artifacts are suppressed primarily and detailed structures are also restored. Regarding the XCAT phantom, quantitative evaluations indicate that an average of 58.70%, 45.25%, and 40.10% decrease in terms of root-mean-square error (RMSE) and an average of 2.10, 1.37, and 1.37 times in terms of structural similarity index (SSIM) are achieved by the proposed pMaDL method when compared with piccs, PICCS, MaDL(2D), and MaDL(2D), respectively. Moreover the proposed pMaDL achieves a comparable performance with HQI-4DCBCT algorithm in terms of RMSE and SSIM metrics. However, pMaDL has a better ability to suppress streaking artifacts than HQI-4DCBCT. The proposed algorithm could reconstruct a set of 4D CBCT images with both high spatiotemporal resolution and detailed features preservation. Moreover the proposed pMaDL can effectively suppress the streaking artifacts in the resultant reconstructions, while achieving an overall improved spatiotemporal resolution by incorporating the motion-aware dictionary with a prior constraint into the proposed 4D CBCT iterative framework.

A Deep Learning Approach for Discrimination of Single- and Multi-Source Corona Discharges

Insulation system health is crucial for reliable, lifelong operation of almost any electrical apparatus. While many studies have focused on the testing, modeling, and analysis of insulation aging mechanisms, research is needed to overcome new challenges in electric power systems. Fortunately, the progress in data analytics methods has opened up new opportunities to extract information from datasets. This study aims to make use of deep learning algorithms to lay the foundation for an online condition monitoring system that is capable of discriminating single- and multi-source corona discharges. In this article, we report the results of experimental testing and conversion of the data into phase-resolved partial discharge images, which we fed into deep neural networks. We begin by reviewing some of the most successful image recognition models including AlexNet, Inception-V3, residual network (ResNet), and DenseNet. Thereafter, we develop and optimize a ResNet model to achieve the highest accuracy model with the lowest computational cost.

CycN-Net: A Convolutional Neural Network Specialized for 4D CBCT Images Refinement.

Four-dimensional cone-beam computed tomography (4D CBCT) has been developed to provide a sequence of phase-resolved reconstructions in image-guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts and noise because the phase-resolved image is an extremely sparse-view CT procedure wherein a few under-sampled projections are used for the reconstruction of each phase. Aiming at improving the overall quality of 4D CBCT images, we proposed two CNN models, named N-Net and CycN-Net, respectively, by fully excavating the inherent property of 4D CBCT. To be specific, the proposed N-Net incorporates the prior image reconstructed from entire projection data based on U-Net to boost the image quality for each phase-resolved image. Based on N-Net, a temporal correlation among the phase-resolved images is also considered by the proposed CycN-Net. Extensive experiments on both XCAT simulation data and real patient 4D CBCT datasets were carried out to verify the feasibility of the proposed CNNs. Both networks can effectively suppress streaking artifacts and noise while restoring the distinct features simultaneously, compared with the existing CNN models and two state-of-the-art iterative algorithms. Moreover, the proposed method is robust in handling complicated tasks of various patient datasets and imaging devices, which implies its excellent generalization ability.

High-quality initial image-guided 4D CBCT reconstruction.

Four-dimensional cone-beam computed tomography (4D CBCT) has been developed to provide a sequence of phase-resolved reconstructions in image-guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts because the 4D CBCT reconstruction process is an extreme sparse-view CT procedure wherein only under-sampled projections are used for the reconstruction of each phase. To obtain a set of 4D CBCT images achieving both high spatial and temporal resolution, we propose an algorithm by providing a high-quality initial image at the beginning of the iterative reconstruction process for each phase to guide the final reconstructed result toward its optimal solution. The proposed method consists of three steps to generate the initial image. First, a prior image is obtained by an iterative reconstruction method using the measured projections of the entire set of 4D CBCT images. The prior image clearly shows the appearance of structures in static regions, although it contains blurring artifacts in motion regions. Second, the robust principal component analysis (RPCA) model is adopted to extract the motion components corresponding to each phase-resolved image. Third, a set of initial images are produced by the proposed linear estimation model that combines the prior image and the RPCA-decomposed motion components. The final 4D CBCT images are derived from the simultaneous algebraic reconstruction technique (SART) equipped with the initial images. Qualitative and quantitative evaluations were performed by using two extended cardiac-torso (XCAT) phantoms and two sets of patient data. Several state-of-the-art 4D CBCT algorithms were performed for comparison to validate the performance of the proposed method. The image quality of phase-resolved images is greatly improved by the proposed method in both phantom and patient studies. The results show an outstanding spatial resolution, in which streaking artifacts are suppressed to a large extent, while detailed structures such as tumors and blood vessels are well restored. Meanwhile, the proposed method depicts a high temporal resolution with a distinct respiratory motion change at different phases. For simulation phantom, quantitative evaluations of the simulation data indicate that an average of 36.72% decrease at EI phase and 42% decrease at EE phase in terms of root-mean-square error (RMSE) are achieved by our method when comparing with PICCS algorithm in Phantom 1 and Phantom 2. In addition, the proposed method has the lowest entropy and the highest normalized mutual information compared with the existing methods in simulation experiments, such as PRI, RPCA-4DCT, SMART, and PICCS. And for real patient cases, the proposed method also achieves the lowest entropy value compared with the competitive method. The proposed algorithm can generate an optimal initial image to improve iterative reconstruction performance. The final sequence of phase-resolved volumes guided by the initial image achieves high spatiotemporal resolution by eliminating motion-induced artifacts. This study presents a practical 4D CBCT reconstruction method with leading image quality.

Phase-space joint resolved PD characteristics of defects on insulator surface in GIS

Partial discharges (PDs) initiated by conductive particles attached to the insulator surface in gas-insulated metal-enclosed switchgear (GIS) may occur at different particle tips with varying spatial information. PD modes also differ depending on the spatial position and voltage phase. In this study, PDs were investigated as per phase-space joint resolved characteristics using two photomultiplier tubes and phase-resolved discharge images. PDs were observed to occur mainly at the negative particle tip regardless of the half-cycle. A sudden increase in apparent PD charge is attributed to the corona-creepage transition. The PD mode transition mechanism and effectiveness of the on-site dielectric test for defect detection were also determined. The results presented here may provide theoretical support for GIS defect identification and threat assessment.

Correspondence between phase resolved partial discharge patterns and corona discharge modes

Corona discharge under super low frequency (30–300 Hz) voltage is common in high-voltage transmission lines and equipment. The phase resolved partial discharge (PRPD) pattern is the most commonly used tool for power equipment condition-monitoring in the electrical engineering field. The present study centers on the correspondence between PRPD patterns and corona discharge modes under a wide range of applied voltage. Electric and optical emission characteristic measurement methods are applied to obtain PRPD patterns and phase resolved discharge images. The transition of corona discharge types and space charge behavior under different applied voltage are investigated. The electric field generated by negative needle electrode is shown to erase residual positive ions, which leads to an abundance of positive streamer bursts when applied voltage is low. Once the negative glow corona emerges under higher applied voltage, dense positive ions in the cathode sheath suppress the positive streamer bursts. The existence of residual positive ions is crucial for the transition from positive streamer discharge to glow corona discharge. A comprehensive understanding of PRPD patterns may help electrical engineers to assess the threat of partial discharge of the power equipment correctly.

Correspondence between phase resolved partial discharge patterns and corona discharge modes

Corona discharge under super low frequency (30–300 Hz) voltage is common in high-voltage transmission lines and equipment. The phase resolved partial discharge (PRPD) pattern is the most commonly used tool for power equipment condition-monitoring in the electrical engineering field. The present study centers on the correspondence between PRPD patterns and corona discharge modes under a wide range of applied voltage. Electric and optical emission characteristic measurement methods are applied to obtain PRPD patterns and phase resolved discharge images. The transition of corona discharge types and space charge behavior under different applied voltage are investigated. The electric field generated by negative needle electrode is shown to erase residual positive ions, which leads to an abundance of positive streamer bursts when applied voltage is low. Once the negative glow corona emerges under higher applied voltage, dense positive ions in the cathode sheath suppress the positive streamer bursts. The existence of residual positive ions is crucial for the transition from positive streamer discharge to glow corona discharge. A comprehensive understanding of PRPD patterns may help electrical engineers to assess the threat of partial discharge of the power equipment correctly.

Effect of Modulation of the Inlet Velocity and Equivalence Ratio Gradients on the Stabilization of Stratified Axisymmetric Bluff-Body Flames

An investigation of ultralean stratified, disk stabilized, propane flames operated with acoustic modulation of the inlet velocity and fuel-air mixture profiles is presented. Transverse acoustic forcing was applied to the air, upstream of a double-cavity premixer section, formed along three concentric disks, which fueled the stabilization region with a radial mixture gradient. Measurements and supporting Large Eddy Simulations with a nine-step mechanism for propane combustion were performed to evaluate variations in the ultralean flame characteristics under forced and unforced conditions. The effects of forcing on the heat release profiles and on the interaction of the toroidal flame with the recirculation region are examined and discussed. The impact of the acoustic excitation of inlet conditions on the local extinction behavior is, also, assessed by monitoring a local stability criterion and by analyzing phase-resolved chemiluminescence images.

Near-Field Imaging of Infrared Nanoantenna Modes Under Oblique Illumination

Plasmonic nanoantennas provide powerful tools in enhancing light–matter interactions by linking propagating far fields with localized near fields. Microscopic understanding on the enhancements of light–matter interactions requires precise knowledges on the near-field distributions upon nanoantenna excitations. In this article, we study near-field distributions of nanoantennas under oblique illumination. We acquire the amplitude- and phase-resolved images of the near fields on Au nanorods in the infrared range, by use of the scattering-type scanning near-field optical microscopy and the interferometric homodyne detection. Asymmetric near-field distributions are experimentally visualized and theoretically reproduced. The asymmetry in the near-field distributions is attributed to the preferential excitation of the surface plasmon polariton in one direction and the finite loss of the surface plasmon polariton inside the cavity. Furthermore, we organize the excitation conditions of the nanoantenna modes under ...

Hybrid plasmonic waveguides formed by metal coating of dielectric ridges.

Bound hybrid plasmon-polariton modes supported by waveguides, which are formed by gold coating of ridges etched into a silica substrate, are analyzed using numerical simulations and investigated experimentally using near-field microscopy at telecom wavelengths (1425-1625 nm). Drastic modifications of the fundamental mode profile along with changes in the mode confinement and propagation loss are found when varying the ridge height. The main mode characteristics (effective mode index, propagation length, and mode profile) are determined from the experimental amplitude- and phase-resolved near-field images and compared with the simulations. The possibility of strongly influencing the mode properties along with subwavelength confinement found simultaneously with relatively long propagation can further be exploited in mode shaping and sensing applications.

Robust phase unwrapping for phase images in Fourier domain Doppler optical coherence tomography.

To solve the 2 ? phase ambiguity for phase-resolved Doppler images in Doppler optical coherence tomography, we present a modified network programming technique for the first time to the best of our knowledge. The proposed method assumes that error of the discrete derivatives between unwrapped phase image and wrapped phase image can be arbitrary values instead of integer-multiple of 2 ? , which makes the real-phase restoration accurate and robust against noise. We compared our proposed method with the network programming method. Parameters including root-mean-square-error and noise amplification degree were adopted for comparison. The experimental study on simulated images, phantom, and real-vessel OCT images were performed. The proposed method consistently achieves optimal results.

Near-field characterization of bound plasmonic modes in metal strip waveguides.

Propagation of bound plasmon-polariton modes along 30-nm-thin gold strips on a silica substrate at the free-space wavelength of 1500 nm is investigated both theoretically and experimentally when decreasing the strip width from 1500 nm down to the aspect-ratio limited width of 30 nm, which ensures deep subwavelength mode confinement. The main mode characteristics (effective mode index, propagation length, and mode profile) are determined from the experimental amplitude- and phase-resolved near-field images for various strip widths (from 30 to 1500 nm), and compared to numerical simulations. The mode supported by the narrowest strip is found to be laterally confined within ~ 100 nm at the air side, indicating that the realistic limit for radiation nanofocusing in air using tapered metal strips is ~ λ/15.

Microwave soft x-ray microscopy for nanoscale magnetization dynamics in the 5-10 GHz frequency range.

We present a scanning transmission x-ray microscopy setup combined with a novel microwave synchronization scheme for studying high frequency magnetization dynamics at synchrotron light sources. The sensitivity necessary to detect small changes in the magnetization on short time scales and nanometer spatial dimensions is achieved by combining the excitation mechanism with single photon counting electronics that is locked to the synchrotron operation frequency. Our instrument is capable of creating direct images of dynamical phenomena in the 5-10 GHz range, with high spatial resolution. When used together with circularly polarized x-rays, the above capabilities can be combined to study magnetic phenomena at microwave frequencies, such as ferromagnetic resonance (FMR) and spin waves. We demonstrate the capabilities of our technique by presenting phase resolved images of a ∼6 GHz nanoscale spin wave generated by a spin torque oscillator, as well as the uniform ferromagnetic precession with ∼0.1° amplitude at ∼9 GHz in a micrometer-sized cobalt strip.