Relative SNR Research Articles

In vivo enhancement of calf MRI at 7 T via optimized flexible metasurfaces.

Ultrahigh field (≥7 T) MRI is at the cutting edge of medical imaging, enabling enhanced spatial and spectral resolution as well as enhanced susceptibility contrast. However, transmit ( ) field inhomogeneity due to standing wave effects caused by the shortened RF wavelengths at 7 T is still a challenge to overcome. Novel hardware methods such as dielectric pads have been shown to improve the field inhomogeneity but are currently limited in their corrective effect by the range of high-permittivity materials available and have a fixed shelf life. In this work, an optimized metasurface design is presented that demonstrates in vivo enhancement of the field. A prototype metasurface was optimized by an empirical capacitor sweep and by varying the period size. Phantom temperature experiments were performed to evaluate potential metasurface heating effects during scanning. Lastly, in vivo gradient echo images and maps were acquired on five healthy subjects on a 7 T system. Dielectric pads were also used as a comparison throughout the work as a standard comparison. The metasurfaces presented here enhanced the average relative SNR of the gradient echo images by a factor of 2.26 compared to the dielectric pads factor of 1.61. Average values reflected a similar enhancement of 27.6% with the metasurfaces present versus 8.9% with the dielectric pads. The results demonstrate that metasurfaces provide superior performance to dielectric padding as shown by maps reflecting their direct effects and resulting enhancements in image SNR at 7 T.

Motion-compensated diffusion encoding in multi-shot human brain acquisitions: Insights using high-performance gradients.

To evaluate the utility of up to second-order motion-compensated diffusion encoding in multi-shot human brain acquisitions. Experiments were performed with high-performance gradients using three forms of diffusion encoding motion-compensated through different orders: conventional zeroth-order-compensated pulsed gradients (PG), first-order-compensated gradients (MC1), and second-order-compensated gradients (MC2). Single-shot acquisitions were conducted to correlate the order of motion compensation with resultant phase variability. Then, multi-shot acquisitions were performed at varying interleaving factors. Multi-shot images were reconstructed using three levels of shot-to-shot phase correction: no correction, channel-wise phase correction based on FID navigation, and correction based on explicit phase mapping (MUSE). In single-shot acquisitions, MC2 diffusion encoding most effectively suppressed phase variability and sensitivity to brain pulsation, yielding residual variations of about 10° and of low spatial order. Consequently, multi-shot MC2 images were largely satisfactory without phase correction and consistently improved with the navigator correction, which yielded repeatable high-quality images; contrarily, PG and MC1 images were inadequately corrected using the navigator approach. With respect to MUSE reconstructions, the MC2 navigator-corrected images were in close agreement for a standard interleaving factor and considerably more reliable for higher interleaving factors, for which MUSE images were corrupted. Finally, owing to the advanced gradient hardware, the relative SNR penalty of motion-compensated diffusion sensitization was substantially more tolerable than that faced previously. Second-order motion-compensated diffusion encoding mitigates and simplifies shot-to-shot phase variability in the human brain, rendering the multi-shot acquisition strategy an effective means to circumvent limitations of retrospective phase correction methods.

Improved 3D DESS MR neurography of the lumbosacral plexus with deep learning and geometric image combination reconstruction.

To evaluate the impact of deep learning (DL) reconstruction in enhancing image quality and nerve conspicuity in LSP MRN using DESS sequences. Additionally, a geometric image combination (GIC) method to improve DESS signals' combination was proposed. Adult patients undergoing 3.0 Tesla LSP MRN with DESS were prospectively enrolled. The 3D DESS echoes were separately reconstructed with and without DL and DL-GIC combined reconstructions. In a subset of patients, 3D T2-weighted short tau inversion recovery (STIR-T2w) sequences were also acquired. Three radiologists rated 4 image stacks ('DESS S2', 'DESS S2 DL', 'DESS GIC DL' and 'STIR-T2w DL') for bulk motion, vascular suppression, nerve fascicular architecture, and overall nerve conspicuity. Relative SNR, nerve-to-muscle, -fat, and -vessel contrast ratios were measured. Statistical analysis included ANOVA and Wilcoxon signed-rank tests. p < 0.05 was considered statistically significant. Forty patients (22 females; mean age = 48.6 ± 18.5years) were enrolled. Quantitatively, 'DESS GIC DL' demonstrated superior relative SNR (p < 0.001), while 'DESS S2 DL' exhibited superior nerve-to-background contrast ratio (p value range: 0.002 to < 0.001). Qualitatively, DESS provided superior vascular suppression and depiction of sciatic nerve fascicular architecture but more bulk motion as compared to 'STIR-T2w DL'. 'DESS GIC DL' demonstrated better nerve visualization for several smaller, distal nerve segments than 'DESS S2 DL' and 'STIR-T2w DL'. Application of a DL reconstruction with geometric image combination in DESS MRN improves nerve conspicuity of the LSP, especially for its smaller branch nerves.

Evaluation of Image Quality of Compressed Sensing Magnetic Resonance Images

The aim of the study was to compare the image quality of reconstructed images using the 3D T1 variable flip angle (CUBE) sequence with and without compressed sensing (CS). A phantom was prepared by diluting the Gadolinium contrast medium with saline at the concentrations of 0, 0.2, 0.6, 1.0, and 4.0 mM. Moreover, images were obtained using the 3D T1 CUBE sequence with and without the use of CS. When CS was used, images were reconstructed at increasing CS factor values (i.e., 1.2, 1.4, 1.6, 1.8, and 2.0), while all other variables were unaltered. Measurements were analyzed using Student’s t-tests and ANOVA. Moreover, the SSIM and RMSE estimates were evaluated using the ICY program and the relative SNR errors were quantified. The scan time reduced by up to 1min 35sec from the conventional 3D T1 CUBE sequence (3min 3sec) to 3D T1 CUBE sequence using CS (CS factor=2.0). The SNR values of the conventional 3D T1 CUBE and 3D T1 CUBE sequences using the CS technique were not significantly (p0.05) different when the CS factors varied from 1.2 to 2.0. Moreover, the estimated SSIM were similar, while the root mean square error (RMSE) values varied when the CS factor varied from 1.2 to 2.0, based on the use of the conventional 3D T1 CUBE sequence. Therefore, the 3D T1 CUBE sequence using the CS technique can achieve an acceptable image quality that is not considerably different from that of the conventional method.

In situ single particle classification reveals distinct 60S maturation intermediates in cells.

Previously, we showed that high-resolution template matching can localize ribosomes in two-dimensional electron cryo-microscopy (cryo-EM) images of untilted Mycoplasma pneumoniae cells with high precision (Lucas et al., 2021). Here, we show that comparing the signal-to-noise ratio (SNR) observed with 2DTM using different templates relative to the same cellular target can correct for local variation in noise and differentiate related complexes in focused ion beam (FIB)-milled cell sections. We use a maximum likelihood approach to define the probability of each particle belonging to each class, thereby establishing a statistic to describe the confidence of our classification. We apply this method in two contexts to locate and classify related intermediate states of 60S ribosome biogenesis in the Saccharomyces cerevisiae cell nucleus. In the first, we separate the nuclear pre-60S population from the cytoplasmic mature 60S population, using the subcellular localization to validate assignment. In the second, we show that relative 2DTM SNRs can be used to separate mixed populations of nuclear pre-60S that are not visually separable. 2DTM can distinguish related molecular populations without the need to generate 3D reconstructions from the data to be classified, permitting classification even when only a few target particles exist in a cell.

Quality assessment of digital images produced using handheld X-ray equipment

This study aimed at the assessment and monitoring of digital image quality parameters resulting from the use of portable X-ray equipment in clinical examinations. A multicentric study was conducted for quality assessment of images produced using the portable NOMAD® with the DIGORA® ™ Optime UV computed radiography (CR) system. The digital image quality was evaluated in terms of high- and low-contrast spatial resolution, contrast noise ratio (CNR), and signal-to-noise ratio (SNR). The samples comprised six biomaterials: zirconia (Zr), lyophilized bone (LB), photopolymerizable restorative resin (PRR), glass ionomer cement (GIC), GIC photopolymerizable (GICP), and double adhesive resin cement (DARC). The DICOM image (processed pixels) and raw data (without processing) were quantitatively analyzed. The qualitative visual analysis was performed in an adequately illuminated environment and then repeated in an environment equivalent to that of a clinical practice using conventional monitoring equipment. The relative biomaterial contrast was normalized by the high-contrast result of Zr. The Zr image demonstrated no noise because the standard deviation of the digital image biomaterial pixel mean was zero. In contrast, the relative SNR of the biomaterials was normalized by the DARC result. The relative CNR values according to different aluminum thicknesses were 0.11 for LB and 0.3–0.35 for PRR, GIC, and GICP. The spatial resolution was identical for both high-resolution and conventional monitors; however, for a 0.2-s clinical exposure, the resolution produced by the high-quality monitor increased. The quality control tests indicated the compatibility of the CR system-assisted portable X-ray equipment, verifying the status and image quality.

19F MRI Imaging Strategies to Reduce Isoflurane Artifacts in In Vivo Images

PurposeIsoflurane (ISO) is the most commonly used preclinical inhalation anesthetic. This is a problem in 19F MRI of fluorine contrast agents, as ISO signals cause artifacts that interfere with unambiguous image interpretation and quantification; the two most attractive properties of heteronuclear MRI. We aimed to avoid these artifacts using MRI strategies that can be applied by any pre-clinical researcher.ProceduresThree strategies to avoid ISO chemical shift displacement artifacts (CSDA) in 19F MRI are described and demonstrated with measurements of 19F-containing agents in phantoms and in vivo (n = 3 for all strategies). The success of these strategies is compared to a standard Rapid Acquisition with Relaxation Enhancement (RARE) sequence, with phantom and in vivo validation. ISO artifacts can successfully be avoided by (1) shifting them outside the region of interest using a narrow signal acquisition bandwidth, (2) suppression of ISO by planning a frequency-selective suppression pulse before signal acquisition or by (3) preventing ISO excitation with a 3D sequence with a narrow excitation bandwidth.ResultsAll three strategies result in complete ISO signal avoidance (p < 0.0001 for all methods). Using a narrow acquisition bandwidth can result in loss of signal to noise ratio and distortion of the image, and a frequency-selective suppression pulse can be incomplete when B1-inhomogeneities are present. Preventing ISO excitation with a narrow excitation pulse in a 3D sequence yields the most robust results (relative SNR 151 ± 28% compared to 2D multislice methods, p = 0.006).ConclusionWe optimized three easily implementable methods to avoid ISO signal artifacts and validated their performance in phantoms and in vivo. We make recommendation on the parameters that pre-clinical studies should report in their method section to make the used approach insightful.

PITEM: Permutations-Based Instruction Tracking Via Electromagnetic Side-Channel Signal Analysis

The emergence of cyber-physical systems (CPS) and internet of things (IoT) devices impose significant security and privacy concerns that necessitate robust monitoring and malware detection systems. This paper proposes PITEM, a framework for instruction-level monitoring and malware detection using electromagnetic (EM) side-channels. PITEM identifies instruction types with similar EM emanations using hierarchical clustering. To track all combinations of these instruction types, we generate EM signatures for all permutations of them. In testing, we predict the permutation class of testing traces by a matched-filter-like predictor. We test the performance on two devices (FPGA-based and ARM-based) with 50 MHz and 1 GHz clock frequencies. We achieve 95.67% and 87.35% accuracies for these devices for single execution of permutations. We note that the accuracy increases to 100% when permutation blocks are repeated. Furthermore, we test the limits of the system by tracking permutations of instructions of the same type. With sufficient bandwidth and number of repetitions, individual instructions can be resolved with 87.5% and 95.78% accuracies for these devices. The performance is evaluated for different relative signal-to-noise ratio (SNR) levels and performance is stable for relative SNR values > 15 dB. Finally, we demonstrate PITEMs ability to detect fine-grained malware with 99.89% accuracy.

Capacitive versus Overlap Decoupling of Adjacent Radio Frequency Phased Array Coil Elements: An Imaging Robustness Comparison When Sample Load Varies for 3 Tesla MRI.

Phased array (PA) receive coils are built such that coil elements approximate independent antenna behavior. One method of achieving this goal is to use an available decoupling method to decouple adjacent coil elements. The purpose of this work was to compare the relative performance of two decoupling methods as a function of variation in sample load. Two PA receive coils with 5 channels (5-ch) each, equal outer dimensions, and formed on 12 cm diameter cylindrical phantoms of conductivities 0.3, 0.6, and 0.9 S/m were evaluated for relative signal-to-noise ratio (SNR) and parallel imaging performance. They were only tuned and matched to the 0.6 S/m phantom. Simulated and measured axial, sagittal, and coronal 5-ch PA coil SNR ratios were compared by dividing the overlap by the capacitive decoupled coil SNR results. Issues related to the selection of capacitor values for the two decoupling methods were evaluated by taking the ratio of the match and tune capacitors for large and small 2 channel (2-ch) PA coils. The SNR ratios showed that the SNR of the two decoupling methods were very similar. The inverse geometry-factor maps showed similar but better overall parallel imaging performance for the capacitive decoupled method. The quotients for the 2-ch PA coils’ maximum and minimum capacitor value ratios are 3.28 and 1.38 for the large and 3.28 and 2.22 for the small PA. The results of this paper demonstrate that as the sample load varies, the capacitive and overlap decoupling methods are very similar in relative SNR and this similarity continues for parallel imaging performance. Although, for the 5-ch coils studied, the capacitive decoupling method has a slight SNR and parallel imaging advantage and it was noted that the capacitive decoupled coil is more likely to encounter unbuildable PA coil configurations.

Urban Riverway Extraction from High-Resolution SAR Image Based on Blocking Segmentation and Discontinuity Connection

An urban riverway extraction method is proposed for high-resolution synthetic aperture radar (SAR) images. First, the original image is partitioned into overlapping sub-image blocks, in which the sub-image blocks that do not cover riverways are regarded as background. Sub-image blocks covering riverways are then filtered using the iterative adaptive speckle reduction anisotropic diffusion (SRAD) that introduces the relative signal-to-noise ratio (RSNR). The filtered images are segmented quickly by the Sauvola algorithm, and the false riverway fragments are removed by the area and aspect ratio of the connected component in the segmentation results. Using the minimum convex hull of each riverway segment as the connection object, the seeds are automatically determined by the difference between adjacent pyramid layers, and the sub-image block riverway extraction result is used as the bottom layer. The discontinuity connection between river segments is achieved by multi-layer region growth. Finally, the processed sub-image blocks are stitched to get the riverway extraction results for the entire image. To verify the applicability and usefulness of the proposed approach, high-resolution SAR imagery obtained by the Gaofen-3 (GF-3) satellite was used in the assessment. The qualitative and quantitative evaluations of the experimental results show that the proposed method can effectively and completely extract complex urban riverways from high-resolution SAR images.

Correction and optimization of symmetric echo-planar spectroscopic imaging for hyperpolarized [1-13C]-pyruvate

Symmetric echo-planar spectroscopic imaging (EPSI) supports higher spectral bandwidth and improves signal-to-noise efficiency compared to flyback EPSI with the same readout bandwidth, but suffers from artifacts that are associated with non-uniform temporal sampling in k-t space. Our goal is to eliminate these artifacts and enhance observation of hyperpolarized [1-13C] pyruvate and its metabolites using symmetric EPSI. We used symmetric EPSI to efficiently acquire radially encoded spectroscopic imaging projections with a spectral under-sampling scheme that was optimized for HP pyruvate and its metabolites. A simple approach called selective correction of off-resonance effects (SCORE) was developed and applied to eliminate spectral artifacts. Simulations were used to assess the relative SNR performance of this technique, and a phantom study was carried out at 3 T to evaluate this method and compare it with alternative strategies. SCORE correction eliminated spectral artifacts due to chemical shift and non-uniform sampling in time. It is also compatible with established methods to eliminate artifacts caused by eddy currents. SCORE corrected symmetric EPSI supported maximal EPSI spectral bandwidth and improved SNR efficiency. Symmetric EPSI with SCORE correction offers a straightforward, efficient, and effective framework for assessment of hyperpolarized [1-13C] pyruvate and its metabolites.

Multi-parameter Analytical Method for B1 and SNR Analysis (MAMBA): An open source RF coil design tool

In Magnetic Resonance Imaging (MRI), radio frequency (RF) coils of different forms and shapes are used to maximize signal-to-noise ratio (SNR). RF coils are designed for clinical applications and have dimensions comparable with the target body part to be imaged, and they perform best when loaded by human tissue majority of which have conductivity values higher than 0.5 S/m. However, they are not properly tuned and matched for samples having low conductivity such as solid samples with low water content. Moreover, for samples with low filling factor and low conductivity, the noise in MRI is dominated by RF coil losses. In this case, RF coil design can be optimized to improve image SNR.Here, a new software tool (Multi-parameter Analytical Method for B1 and SNR Analysis) MAMBA is presented to design and compare volume coils of birdcage, solenoid, and loop-gap design for these samples. The input parameters of the tool are the sample properties, the coil design and the hardware properties, of which a relative SNR is determined. For that, a figure of merit is calculated from the coil sensitivity, applied resonant frequency and the resistive losses of sample, coil and capacitive components. The tool was tested in an ancient Egyptian mummy head which represents an extreme case of MRI with short T2*. Two optimized birdcage coils were designed using MAMBA, constructed and compared to a commercial transmit receive head coil. Calculated relative SNR values are in good agreement with the measurements.

Predicting the noise in hybrid (phase and attenuation) x-ray images acquired with the edge illumination technique.

To analyze the noise performance of the edge illumination phase-based x-ray imaging technique when applying "single-shot" phase retrieval. The latter consists in applying a sample-specific low-pass filter to the raw data, leading to "hybrid" images in which phase and attenuation contrast are merged with each other. The second objective is to compare the hybrid images with attenuation-only images based on their respective signal-to-noise ratio (SNR). Noise is propagated from the raw images into the retrieved hybrid images, yielding analytic expressions for the variances and noise power spectra of the latter. An expression for the relative SNR between hybrid and attenuation images is derived. A comparison with simulated data is performed. Experimental data are also shown and discussed in the context of the theory. The noise transfer into the retrieved hybrid images is strongly related to the setup and acquisition parameters, as well as the imaged sample itself. Consequently, the relative merit between hybrid and attenuation images also depends on these criteria. Generally, the hybrid approach tends to perform worse for highly attenuating samples, as the availability of phase contrast is outweighed by the loss of photons that is necessarily encountered in hybrid acquisitions. On the contrary, the hybrid approach can lead to a much better SNR for weakly attenuating samples, as here phase effects lead to much stronger contrast, outweighing the reduction in photon numbers. The analytic expressions inform the design of edge illumination setups that lead to minimum noise transfer into the retrieved hybrid images. We also anticipate our theory to guide the decision as to which imaging mode (hybrid or attenuation) to use in order to maximize SNR for a specific sample.

Transfer Learning for Semi-Supervised Automatic Modulation Classification in ZF-MIMO Systems

Automatic modulation classification (AMC) is an essential technology for the non-cooperative communication systems, and it is widely applied into various communications scenarios. In the recent years, deep learning (DL) has been introduced into AMC due to its outstanding identification performance. However, it is almost impossible to implement previously proposed DL-based AMC algorithms without large number of labeled samples, while there are generally few labeled sample and large unlabel samples in the realistic communication scenarios. In this paper, we propose a transfer learning (TL)-based semi-supervised AMC (TL-AMC) in a zero-forcing aided multiple-input and multiple-output (ZF-MIMO) system. TL-AMC has a novel deep reconstruction and classification network (DRCN) structure that consists of convolutional auto-encoder (CAE) and convolutional neural network (CNN). Unlabeled samples flow from CAE for modulation signal reconstruction, while labeled samples are fed into CNN for AMC. Knowledge is transferred from the encoder layer of CAE to the feature layer of CNN by sharing their weights, in order to avoid the ineffective feature extraction of CNN under the limited labeled samples. Simulation results demonstrated the effectiveness of TL-AMC. In detail, TL-AMC performs better than CNN-based AMC under the limited samples. What’s more, when compared with CNN-based AMC trained on massive labeled samples, TL-AMC also achieved the similar classification accuracy at the relative high SNR regime.

Estimation of low-level components lost through chromatographic separations with finite detection limits.

The search for biomarkers allowing the assessment of disease by early diagnosis is facilitated by liquid chromatography. However, it is not clear how many components are lost due to being present in concentrations below the detection limit and/or being obscured by chromatographic peak overlap. First, we extend the study of missing components undertaken by Enke and Nagels, who employed the log-normal probability density function (pdf) for the distribution of signal intensities (and concentrations) of three mixtures. The Weibull and exponential pdfs, which have a higher probability of small-concentration components than the log-normal pdf, are also investigated. Results show that assessments of the loss of low-intensity signals by curve fitting are ambiguous. Next, we simulate synthetic chromatograms to compare the loss of peaks from superposition (overlap) with neighboring peaks to the loss arising from lying below the limit of detection (LOD) imposed by a finite signal-to-noise ratio (SNR). The simulations are made using amplitude pdfs based on the Enke-Nagels data as functions of relative column efficiency, i.e., saturation, and SNR. Results show that at the highest efficiencies, the lowest-amplitude peaks are lost below the LOD. However, at small and medium efficiencies, peak overlap is the dominant loss mechanism, suggesting that low-level components will not be found easily in liquid chromatography with single channel detectors regardless of SNR. A simple treatment shows that a multichannel detector, e.g., a mass spectrometer, is necessary to expose more low-level components.

Quantitative Evaluation of Channel Micro-Doppler Capacity for MIMO UWB Radar Human Activity Signals Based on Time–Frequency Signatures

A novel quantitative method to evaluate channel micro-Doppler capacity of multiple-input and multiple-output (MIMO) system is proposed here. The method is valid for ultrawideband (UWB) MIMO radar human activity systems based on time–frequency signatures, and the quality measure will be noted as a relative signal to noise ratio (RSNR). The method quantifies these signatures and evaluates the relative superiority or inferiority of these MIMO channels. Examples of micro-Doppler signature ( $\mu $ Ds) characteristics of human activities in a channel will be considered and compared to that of all other channels. First, the MIMO UWB radar human activity signal is modeled, and its corresponding time–frequency ( $T$ – $F$ ) characteristics are analyzed to justify the rationality of using the new RSNR metric. Second, the method is evaluated using experimental data and the capability of distinguishing the $\mu \text{D}$ capacity differences among channels is demonstrated. This new method clearly and accurately shows much better visible $\mu \text{D}$ evaluation performance than that of the conventional signal to noise ratio in time domain ( ${\mathrm {SNR}}_{t}$ ). Moreover, this evaluation method can still work well, even for signals with low signal to noise ratio (SNR) down to −4-dB level. Therefore, it can be successfully used to select the superior channels and eliminate any inferior channels or provide confidence coefficients for the collected multiple channel data of human activities. This method should lead to a significant reduction of the inferior channels’ influence on further MIMO-based classification or imaging of human activities.

Three-Dimensional GRE T1ρ mapping of the brain using tailored variable flip-angle scheduling.

To introduce a new approach called tailored variable flip-angle (VFA) scheduling for SNR-efficient 3D T1ρ mapping of the brain using a magnetization-prepared gradient-echo sequence. Simulations were used to assess the relative SNR efficiency, quantitative accuracy, and spatial blurring of tailored VFA scheduling for T1ρ mapping of brain tissue compared with magnetization-prepared angle-modulated partitioned k-space spoiled gradient-echo snapshots (MAPSS), a state-of-the-art technique for accurate 3D gradient-echo T1ρ mapping. Simulations were also used to calculate optimal imaging parameters for tailored VFA scheduling versus MAPSS, without and with nulling of CSF. Four participants were imaged at 3T MRI to demonstrate the feasibility of tailored VFA scheduling for T1ρ mapping of the brain. Using MAPSS as a reference standard, in vivo data were used to validate the relative SNR efficiency and quantitative accuracy of the new approach. Tailored VFA scheduling can provide a 2-fold to 4-fold gain in the SNR of the resulting T1ρ map as compared with MAPSS when using identical sequence parameters while limiting T1ρ quantification errors to 2% or less. In vivo whole-brain 3D T1ρ maps acquired with tailored VFA scheduling had superior SNR efficiency than is achievable with MAPSS, and the SNR efficiency improved with a greater number of views per segment. Tailored VFA scheduling is an SNR-efficient GRE technique for 3D T1ρ mapping of the brain that provides increased flexibility in choice of imaging parameters compared with MAPSS, which may benefit a variety of applications.

Topographical assessment of neurocortical connectivity by using directed transfer function and partial directed coherence during meditation.

Due to the presence of nonlinearity and volume conduction in electroencephalography (EEG), sometimes it's challenging to find out the actual brain network from neurodynamical alteration. In this paper, two well-known time-frequency brain connectivity measures, namely partial directed coherence (PDC) and directed transfer function (DTF), have been applied to evaluate the performance analysis of EEG signals obtained during meditation. These measures are implemented to the multichannel meditation EEG data to get the directed neural information flow. Mostly the assessment of PDC and DTF is entirely subjective and there are probabilities to have erroneous connectivity estimation. To avoid the subjective evaluation, the performance results are compared in terms of absolute energy, signal-to-noise ratio (SNR) and relative SNR (R-SNR) scale. In most of the cases, the PDC result is found to be more efficient than DTF. The limitation of DTF and PDC in terms of the time-varying multivariate autoregressive (MVAR) model is highlighted. The time-varying MVAR model can track the neurodynamical changes better than any other method. In the present study, we would like to show that the PDC-based connectivity gives a better understanding of the non-symmetric relation in EEG obtained during Kriya Yoga meditation in comparison to DTF. However, it needs to be investigated further to warrant this claim.

Grid Evolution Method for DOA Estimation

Off-grid direction of arrival (OGDOA) estimation methods deal with the situations where true direction of arrivals (DOAs) are not on the discretized sampling grid. However, existing OGDOA estimation methods are faced with a tradeoff between density of initial grid and computational workload. Furthermore, these methods fail if more than one true DOA is located in a same grid interval. In order to speed up the OGDOA estimation methods and solve the problem of more than one DOA in one initial grid interval, a grid evolution direction of arrival (GEDOA) estimation method is proposed. Based on the combination of OGDOA estimation methods and grid refinement, this new approach makes the grid nonuniformly evolve from coarse to dense. The proposed method contains two subprocesses, i.e., learning process and fission process. The learning process aims to update locations of grid points and estimate DOAs. The fission process aims to generate new grid points and guarantees that there is only one DOA in a grid interval. The two processes iterate alternatively. Finally, an adaptive and nonuniform grid and an estimated spatial spectrum based on this grid are achieved. Compared with the previous methods, GEDOA has better computational efficiency because fewer grid points are used at each iteration. Furthermore, GEDOA has better resolution and lower MSE at relative high SNR. This is because that the evolved grid obtained by this new approach is information adaptive. Numerical simulations validate the effectiveness of the proposed method.

Comparision of new element designs for combined RF-Shim arrays at 7 T.

To identify novel concepts for RF-shim loop architectures suitable for 7T made of two concentric conducting loops fulfilling RF and DC functions, respectively, and to determine their relative SNR performance. The goal is to minimize interference between the two systems while making efficient use of the space closest to the body. We show by means of theoretical derivation of the frequency spectrum that the proposed two-loop structure exhibits an anti-resonant null and a resonant peak in the frequency domain. The proposed structure is comprised of two concentric wire loops either arranged as nested loops or in the form of a coaxial cable, in which the two conductors carry the RF and shim signals, respectively. We use theory, simulation, and phantom measurements to obtain frequency spectra and SNR maps for the proposed structures. Retained SNR is found to be 75% in the coaxial loop and ranges from 57% to 67% in three different coaxial configurations. We have found both implementations to be a viable concept for the use in RF-shim devices if remaining SNR limitations can be overcome. We have investigated two new design modalities in 7T RF-shim coil design that separate the RF and shim conductors such that the previously proposed toroidal chokes are eliminated - thereby removing undesired additional loss, bulk, and design complexity.