Acceleration Factor Research Articles

Adaptive quadtree splitting parallelization (AQSP) algorithm for the VVC standard

The Versatile Video Coding (VVC) standard, also known as H.266, was released in 2020 as the natural successor to the High Efficiency Video Coding (HEVC) standard. Among its innovative coding tools, VVC extended the concept of quadtree (QT) splitting to the multi-type tree (MTT) structure, introducing binary and ternary partitions to enhance HEVC’s coding efficiency. While this brought significant compression improvements, it also resulted in a substantial increase in encoding time, primarily due to VVC’s larger Coding Tree Unit (CTU) size of 128×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document}128 pixels. To mitigate this, this work introduces a flexible parallel approach for the QT traversal and splitting scheme of the VVC encoder, called adaptive quadtree splitting parallelization (AQSP) algorithm. This approach is based on the distribution of coding units (CUs) among different threads using the current depth level of the QT as a basis to minimize the number of broken dependencies. In this way, the algorithm achieves a good trade-off between time savings and coding efficiency. Experimental results show that, when compared with the original VVC encoder, AQSP achieves an acceleration factor of 2.04×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} with 4 threads at the expense of a low impact in terms of BD rate. These outcomes position AQSP competitively in comparison with other state-of-the-art approaches.

Glacial inception through rapid ice area increase driven by albedo and vegetation feedbacks

Abstract. We present transient simulations of the last glacial inception using the Earth system model CLIMBER-X with dynamic vegetation, interactive ice sheets, and visco-elastic solid Earth responses. The simulations are initialized at the middle of the Eemian interglacial (125 kiloyears before present, ka) and run until 100 ka, driven by prescribed changes in Earth's orbital parameters and greenhouse gas concentrations from ice core data. CLIMBER-X simulates a rapid increase in Northern Hemisphere ice sheet area through MIS5d, with ice sheets expanding over northern North America and Scandinavia, in broad agreement with proxy reconstructions. While most of the increase in ice sheet area occurs over a relatively short period between 119 and 117 ka, the larger part of the increase in ice volume occurs afterwards with an almost constant ice sheet extent. We show that the vegetation feedback plays a fundamental role in controlling the ice sheet expansion during the last glacial inception. In particular, with prescribed present-day vegetation the model simulates a global sea level drop of only ∼ 20 m, compared with the ∼ 35 m decrease in sea level with dynamic vegetation response. The ice sheet and carbon cycle feedbacks play only a minor role during the ice sheet expansion phase prior to ∼ 115 ka but are important in limiting the deglaciation during the following phase characterized by increasing summer insolation. The model results are sensitive to climate model biases and to the parameterization of snow albedo, while they show only a weak dependence on changes in the ice sheet model resolution and the acceleration factor used to speed up the climate component. Overall, our simulations confirm and refine previous results showing that climate–vegetation–cryosphere feedbacks play a fundamental role in the transition from interglacial to glacial states characterizing Quaternary glacial cycles.

Compressed Sensitivity Encoding Artificial Intelligence Accelerates Brain Metastasis Imaging by Optimizing Image Quality and Reducing Scan Time.

Accelerating the image acquisition speed of MR imaging without compromising the image quality is challenging. This study aimed to evaluate the feasibility of contrast-enhanced (CE) 3D T1WI and CE 3D-FLAIR sequences reconstructed with compressed sensitivity encoding artificial intelligence (CS-AI) for detecting brain metastases (BM) and explore the optimal acceleration factor (AF) for clinical BM imaging. Fifty-one patients with cancer with suspected BM were included. Fifty participants underwent different customized CE 3D-T1WI or CE 3D-FLAIR sequence scans. Compressed SENSE encoding acceleration 6 (CS6), a commercially available standard sequence, was used as the reference standard. Quantitative and qualitative methods were used to evaluate image quality. The SNR and contrast-to-noise ratio (CNR) were calculated, and qualitative evaluations were independently conducted by 2 neuroradiologists. After exploring the optimal AF, sample images were obtained from 1 patient by using both optimized sequences. Quantitatively, the CNR of the CS-AI protocol for CE 3D-T1WI and CE 3D-FLAIR sequences was superior to that of the CS protocol under the same AF (P < .05). Compared with reference CS6, the CS-AI groups had higher CNR values (all P < .05), with the CS-AI10 scan having the highest value. The SNR of the CS-AI group was better than that of the reference for both CE 3D-T1WI and CE 3D-FLAIR sequences (all P < .05). Qualitatively, the CS-AI protocol produced higher image quality scores than did the CS protocol with the same AF (all P < .05). In contrast to the reference CS6, the CS-AI group showed good image quality scores until an AF of up to 10 (all P < .05). The CS-AI10 scan provided the optimal images, improving the delineation of normal gray-white matter boundaries and lesion areas (P < .05). Compared with the reference, CS-AI10 showed reductions in scan time of 39.25% and 39.93% for CE 3D-T1WI and CE 3D-FLAIR sequences, respectively. CE 3D-T1WI and CE 3D-FLAIR sequences reconstructed with CS-AI for the detection of BM may provide a more effective alternative reconstruction approach than CS. CS-AI10 is suitable for clinical applications, providing optimal image quality and a shortened scan time.

Loss-of-Life Analyses Based on Modified Arrhenius and Relative Aging Rate for Non-Thermally Upgraded Paper in Oil-Immersed Transformer

This study evaluates the Loss-of-Life (LOL) based on the modified relative aging rate of an Oil Natural Air Natural (ONAN) transformer with voltage and power ratings of 132/33 kV and 60 MVA. The study’s methodology included the determination of the Hotspot Temperature (HST) based on the differential equation in IEC 60076-7. The loading and ambient temperature profiles for HST determination are forecasted based on the Seasonal Autoregressive Integrated Moving Average (SARIMA). Next, a new relative aging rate was developed based on the Arrhenius equation, considering the pre-exponential factors governed by oxygen, moisture in paper, and acids at different content levels. The LOL was computed based on the new relative aging rate. The study’s main aim is to examine the impact of pre-exponential factors on the LOL based on modified Arrhenius and relative aging rate. The results indicate that the LOLs for different conditions increase as the oxygen, moisture, low molecular weight acid (LMA), and high molecular weight acid (HMA) increase. The LOLs are 46 days, 1,354 days, and 2,662 days in the presence of 12,000 ppm, 21,000 ppm, and 30,000 ppm of oxygen. In 1%, 3%, and 5% moisture, the LOLs are 477 days, 2,799 days, and 7,315 days. At 1% moisture, the LOL is 1,418 days for LMA, while for HMA, it is 122 days. The LMA has the highest impact on the LOL compared to other aging acceleration factors.

Advances in techniques for simulating decadal changes to barrier islands morphology

We advanced an integrated framework for the analysis of decadal morphological changes in barrier islands. When applied to Taiwan's Waisanding Barrier Island, this refined methodology proved both precise and effective. Utilizing the Coastal Modeling System by the U.S. Army Corps of Engineers for simulations, we sourced representative wave datasets from the European Centre's ERA5. By employing streamlined harmonic constituents, we distilled tidal characteristics, achieving a simplified representation of environmental parameters essential for morphological simulations. Throughout this process, we paired all conditions with morphology acceleration factors, optimizing computational demands. A critical observation was the contrasting time scales between wave activities and topographical evolutions. We delved into various wave representation techniques, including segmentations based on season, month, and height. Initial season-based categorizations overestimated erosion levels. In contrast, simulations factoring in diverse wave height configurations per interval aligned more congruently with empirical observations.

Denoising parameter dependence of coronary artery depictability in compressed sensing magnetic resonance angiography.

Using numerical indices and visual evaluation, we evaluated the dependence of coronary-artery depictability on the denoising parameter in compressed sensing magnetic resonance angiography (CS-MRA). This study was conducted to clarify the acceleration factor (AF) and denoising factor (DF) dependence of CS-MRA image quality. Vascular phantoms and clinical images were acquired using three-dimensional CS-MRA on a clinical 1.5T system. For the phantom measurements, we compared the full width at half maximum (FWHM), sharpness, and contrast ratio of the vascular profile curves for various AFs and DFs. In the clinical cases, the FWHM, sharpness, contrast ratio, signal-to-noise ratio, noise level values, and visual evaluation results were compared for various DFs. Phantom image analyses demonstrated that the respective measurements of the FWHM, sharpness, and contrast ratios did not significantly change with an increase in AF. The FWHM and sharpness measurements slightly changed with the DF level. However, the contrast ratio tended to increase with an increase in the DF level. In the clinical cases, the FWHM and sharpness showed no significant differences, even when the DF level was changed. However, the contrast ratio tended to decrease as the DF level increased. When the DF levels of the clinical cases increased, the background signals of the myocardium, fat, and noise levels decreased. We investigated the dependence of the coronary-artery depictability on AF and DF using CS-MRA. Analysis of the coronary-artery profile curves indicated that a better image quality was achieved with a stronger DF on coronary CS-MRA.

Feature Fusion for Multi-Coil Compressed MR Image Reconstruction.

Magnetic resonance imaging (MRI) occupies a pivotal position within contemporary diagnostic imaging modalities, offering non-invasive and radiation-free scanning. Despite its significance, MRI's principal limitation is the protracted data acquisition time, which hampers broader practical application. Promising deep learning (DL) methods for undersampled magnetic resonance (MR) image reconstruction outperform the traditional approaches in terms of speed and image quality. However, the intricate inter-coil correlations have been insufficiently addressed, leading to an underexploitation of the rich information inherent in multi-coil acquisitions. In this article, we proposed a method called "Multi-coil Feature Fusion Variation Network" (MFFVN), which introduces an encoder to extract the feature from multi-coil MR image directly and explicitly, followed by a feature fusion operation. Coil reshaping enables the 2D network to achieve satisfactory reconstruction results, while avoiding the introduction of a significant number of parameters and preserving inter-coil information. Compared with VN, MFFVN yields an improvement in the average PSNR and SSIM of the test set, registering enhancements of 0.2622dB and 0.0021dB respectively. This uplift can be attributed to the integration of feature extraction and fusion stages into the network's architecture, thereby effectively leveraging and combining the multi-coil information for enhanced image reconstruction quality. The proposed method outperforms the state-of-the-art methods on fastMRI dataset of multi-coil brains under a fourfold acceleration factor without incurring substantial computation overhead.

Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study

In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the microstructure of myocardial tissue in living hearts, providing insights into cardiac function and enabling the development of innovative therapeutic strategies. However, the integration of cDTI into routine clinical practice poses challenging due to the technical obstacles involved in the acquisition, such as low signal-to-noise ratio and prolonged scanning times. In this study, we investigated and implemented three different types of deep learning-based MRI reconstruction models for cDTI reconstruction. We evaluated the performance of these models based on the reconstruction quality assessment, the diffusion tensor parameter assessment as well as the computational cost assessment. Our results indicate that the models discussed in this study can be applied for clinical use at an acceleration factor (AF) of ×2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes 2$$\\end{document} and ×4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes 4$$\\end{document}, with the D5C5 model showing superior fidelity for reconstruction and the SwinMR model providing higher perceptual scores. There is no statistical difference from the reference for all diffusion tensor parameters at AF ×2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes 2$$\\end{document} or most DT parameters at AF ×4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes 4$$\\end{document}, and the quality of most diffusion tensor parameter maps is visually acceptable. SwinMR is recommended as the optimal approach for reconstruction at AF ×2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes 2$$\\end{document} and AF ×4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes 4$$\\end{document}. However, we believe that the models discussed in this study are not yet ready for clinical use at a higher AF. At AF ×8\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes 8$$\\end{document}, the performance of all models discussed remains limited, with only half of the diffusion tensor parameters being recovered to a level with no statistical difference from the reference. Some diffusion tensor parameter maps even provide wrong and misleading information.

Fast imaging of lenticulostriate arteries by high-resolution black-blood T1-weighted imaging with variable flip angles and acceleration by compressed sensitivity encoding

ObjectiveWe investigated the feasibility of using compressed sensitivity encoding (CS-SENSE) to accelerate high-resolution black-blood T1-weighted imaging with variable flip angles (T1WI-VFA) for efficient visualization and characterization of lenticulostriate arteries (LSAs) on a 3.0 T MR scanner. Materials and methodsTwenty-five healthy volunteers and 18 patients with the cerebrovascular disease were prospectively enrolled. Healthy volunteers underwent T1WI-VFA sequences with different acceleration factors (AFs), including conventional sensitivity encoding (SENSE) AF = 3 and CS-SENSE AF = 3, 4, 5, and 6 (SENSE3, CS3, CS4, CS5, CS6, respectively) at 3 Tesla MRI scanner. Objective evaluation (contrast ratio and number, length, and branches of LSAs) and subjective evaluation (overall image quality and LSA visualization scores) were used to assess image quality and LSA visualization. Comparisons were performed among the 5 sequences to select the best AF. All patients underwent both T1WI-VFA with the optimal AF and digital subtraction angiography (DSA) examination, and the number of LSAs observed by T1WI-VFA was compared with that by DSA. ResultsPair-wise comparisons among CS3, CS4, and SENSE3 revealed no significant differences in both objective measurements and subjective evaluation (all P > 0.05). In patients, there was no significant difference in LSA counts on the same side between T1WI-VFA with CS4 and DSA (3, 3–4 and 3, 3–3, P = 0.243). ConclusionsCS3 provided better LSA visualization but a longer scan duration compared to CS4. And, CS4 strikes a good balance between LSA visualization and acquisition time, which is recommended for routine clinical use.

Towards clinical translation of 7 Tesla MRI in the human brain

Clinical translation of 7 tesla (T) MRI of the brain promises high image quality and potentially improved clinical diagnosis for patients compared to current standard lower field-strength MRI at 1.5 and 3T.Here we describe how physics principles underlying ultra-high field (UHF) strength MRI affect 7T image quality, and how these can be exploited to translate 7T brain imaging into clinical practice. UHF MRI profits from higher inherent signal-to-noise ratio (SNR) and a resultant increase in achievable spatial resolution or acceleration factors; increase in sensitivity to magnetic susceptibility differences and a higher amplitude of the Blood Oxygen Level Dependent (BOLD) signal; increase in longitudinal relaxation time; and increased frequency dispersion and spectral resolution in MR spectroscopy.Examples are presented of different brain pathologies, which are better illustrated on 7T compared to lower field strength by applying sequences and imaging techniques that exploit these intrinsic strengths of 7T MRI. This includes imaging of various vascular pathologies, epilepsy and brain tumours.

Phản ứng động đất của các hệ nhị tuyến tính đàn - dẻo và tự tâm định dưới tác dụng của các chuyển động nền do động đất xa và gần

This study presents seismic response of single degree of freedom (SDOF) elasto-plastic and self-centering systems under far and near-fault ground motions. These parameters was conducted to determine the influence on SDOF structure response in term of ductility, absolute acceleration, absorbed energy, strength ratio and strength reduction factor. It was found that maximum ductility for bilinear elasto-plastic systems was qualitatively similar to self-centering hysteretic systems, by adjusting the value of α and β, a self-centering hysteretic systems could be made qualitatively better than bilinear elasto-plastic systems in term of ductility. With respect to the absorbed energy, the self-centering hysteretic systems always less than comparable bilinear elasto-plastic systems. A three 3-story steel frame Self-Centering Brace Frame (SCBF) was designed according AISC seismic provison to validate of response of single degree of freedom self centering systems. 20 far field earthquake groundmotion are used for analysing of three 3-story steel frame SCBF under DBE (Design Based Earthquake) and MCE (Maximum Considerded Earthquake) level by using computer program PISA3D. The maxmimun value of interstory drift are under 1% and 2%, while residual drift are small.

Electromigration of Power Devices Part 2: Power Device in Applications in Controlled Environment

In the power semiconductor industry, there is a continuous development toward a higher current capability of devices while device’s dimensions shrink. Although higher current is preferred, the reliability risk of the electromigration (EM) remains challenging. In the present study, a special EM test vehicle is used to investigate the pure EM behavior of power device, following our previous work. The selection of the test condition is discussed in detail, which is defined based on the temperature and current profiles of the device in the actual application, focusing on the applications of the power devices in a controlled environment, i.e., data centers or telecommunication servers. The current and temperature profiles of the device are discussed based on the device’s maximum power dissipation and its respective thermal design. The results show that the different combinations of currents and temperatures can lead to different ratios of acceleration factor between the temperature and the current. Consequently, an inappropriate selection of the testing condition can result in the over-domination of temperature over the current, leading to wrong conclusions. The effects of the PCB surface finish (both Ni and Sn plating) as well as different electron flow directions on the EM were studied. It is concluded that the maximum current that can be applied to a power device in the application within a controlled environment is rather limited by the cooling capability of the system than the EM induced device failure.

Improved temporal resolution and acceleration on 4D-MR angiography based on superselective pseudo-continuous arterial spin labeling combined with CENTRA-keyhole and view-sharing (4D-S-PACK) using an interpolation algorithm on the temporal axis and compressed sensing–sensitivity encoding (CS-SENSE)

PurposeTwo major drawbacks of 4D-MR angiography based on superselective pseudo-continuous arterial spin labeling combined with CENTRA-keyhole and view-sharing (4D-S-PACK) are the low temporal resolution and long scanning time. We investigated the feasibility of increasing the temporal resolution and accelerating the scanning time on 4D-S-PACK by using CS-SENSE and PhyZiodynamics, a novel image-processing program that interpolates images between phases to generate new phases and reduces image noise. MethodsSeven healthy volunteers were scanned with a 3.0 T MR scanner to visualize the internal carotid artery (ICA) system. PhyZiodynamics is a novel image-processing that interpolates images between phases to generate new phases and reduces image noise, and by increasing temporal resolution using PhyZiodynamics, inflow dynamic data (reference) were acquired by changing the labeling durations (100–2000 msec, 31 phases) in 4D-S-PACK. From this set of data, we selected seven time intervals to calculate interpolated time points with up to 61 intervals using ×10 for the generation of interpolated phases with PhyZiodynamics. In the denoising process of PhyZiodynamics, we processed the none, low, medium, high noise reduction dataset images. The time intensity curve (TIC), the contrast-to-noise ratio (CNR) were evaluated. In accelerating with CS-SENSE for 4D-S-PACK, 4D-S-PACK were scanned different SENSE or CS-SENSE acceleration factors: SENSE3, CS3–6. Signal intensity (SI), CNR, were evaluated for accelerating the 4D-S-PACK. With regard to arterial vascular visualization, we evaluated the middle cerebral artery (MCA: M1–4 segments). ResultsIn increasing temporal resolution, the TIC showed a similar trend between the reference dataset and the interpolated dataset. As the noise reduction weight increased, the CNR of the interpolated dataset were increased compared to that of the reference dataset. In accelerating 4D-S-PACK, the SI values of the SENSE3 dataset and CS dataset with CS3–6 were no significant differences. The image noise increased with the increase of acceleration factor, and the CNR decreased with the increase of acceleration factor. Significant differences in CNR were observed between acceleration factor of SENSE3 and CS6 for the M1–4 (P < 0.05). Visualization of small arteries (M4) became less reliable in CS5 or CS6 images. Significant differences were found for the scores of M2, M3 and M4 segments between SENSE3 and CS6. ConclusionWith PhyZiodynamics and CS-SENSE in 4D-S-PACK, we were able to shorten the scan time while improving the temporal resolution.

Groundwater chemical characteristic analysis and water source identification model study in Gubei coal mine, Northern Anhui Province, China

This study aims to accurately identify mine water sources and reduce the hazards caused by water inrush accidents in coal mines. Taking the Gubei coal mine as an example, the water quality results of the water samples from the Cenozoic unconsolidated aquifer, Permian sandstone fracture aquifer, and Carboniferous Taiyuan Formation limestone karst fracture aquifer in the mine area were tested, and K++Na+, Ca2+, Mg2+, Cl−, SO42−, HCO3−, TDS (Total Dissolved Solids), and pH were selected as the main indicators to study the water chemistry characteristics of the aquifer through water chemistry component analysis, major ion content analysis, Piper trilinear analysis, and correlation analysis. Thirty-five groups of water samples were randomly selected and imported into SPSS software for factor analysis (FA) and downsized to three main factors as the input variables of the artificial neural network model. The particle swarm optimization (PSO) code was written based on the MATLAB platform to improve the self-adjustment weights and acceleration factors for optimizing the initial weights and thresholds of the Back-Propagation (BP) neural network. The training and prediction samples were learned in the ratio of 8:2, and the recognition results were compared with the traditional BP neural network model. Results showed that the groundwater of the Gubei coal mine demonstrated a water quality vertical zoning pattern, and the chemical composition was dominated by cation K++Na+ and anion Cl−. The FA-PSO-BP neural network model has a higher accuracy of water source discrimination compared with the cluster analysis and the FA-BP neural network model. The FA-PSO-BP neural network model is worthy of further application in the problem of water source identification in mine water inrush.

Comparing Strain Assessment in Compressed Sensing and Conventional Cine MRI.

The aim of this study is to assess the feasibility of compressed sensing (CS) acceleration methods compared to conventional segmented cine (Seg) cardiac magnetic resonance (CMR) for evaluating left ventricular (LV) function and strain by feature tracking (FT). In this prospective study, 45 healthy volunteers underwent CMR imaging used Seg, threefold (CS3), fourfold (CS4), and eightfold (CS8) CS acceleration. Cine images were scored for quality (1-5 scale). LV volumetric and functional parameters and global longitudinal (GLS), circumferential (GCS), and radial strains (GRS) were quantified. LV volumetric and functional parameters exhibited no differences between Seg and all CS cines (all P > 0.05). The strains were similar for Seg, CS3, and CS4 (all P > 0.05). Similarly, no significant differences were observed in GRS and GCS between Seg and CS8 (all P > 0.05), but the global longitudinal strain was significantly lower for CS8 versus Seg (P < 0.001). Image quality declined with CS acceleration, especially in long-axis views with CS8. CS cine MRI at acceleration factor 4 maintained good image quality and accurate measurements of LV function and strain, although there was a slight reduction in the quality of long-axis images and GLS with CS8. CS acceleration up to a factor of 4 enabled fast CMR evaluations, making it suitable for clinical use.

Tensor denoising of quantitative multi-parameter mapping.

Quantitative multi-parameter mapping (MPM) provides maps of physical quantities representing physiologically meaningful tissue characteristics, which allows to investigate microstructure-function relationships reflecting normal or pathologic processes in the brain. However, the achievable spatial resolution and stability of MPM for basic research or clinical applications is severely constrained by SNR limits of the MR acquisition process, resulting in relatively long acquisition times. To increase SNR, we denoise MPM acquisitions using principal component analysis along tensors exploiting the Marchenko-Pastur law (tMPPCA). tMPPCA denoising was applied to three sets of MPM raw data before the quantification of maps of proton density, magnetization transfer saturation, R1, and R2*. The regional SNR gain for high-resolution MPM was investigated as well as reproducibility gains for clinically optimized protocols with moderate and high acceleration factors at different image resolutions. Substantial noise reduction in raw data was achieved, resulting in reduced noise for quantitative mapping up to sixfold without introducing bias of mean values (below 1%). Scan-rescan fluctuations were reduced up to threefold. Denoising allowed to decrease the voxel volume fourfold at the same scan time or reduce the scan time twofold at same voxel volume without loss of sensitivity. tMPPCA denoising can (a) improve of fine spatial and temporal patterns, (b) considerably reduce scan time for clinical applications, or (c) increase resolution to potentially push cutting-edge MPM protocols from the upper to the lower limit of the mesoscopic scale.

Comparison of a Novel AFPSO-SFS & PSO-SFS Approaches for Islanding Protection of Inverter-Based DGs

The most significant of the active islanding detection techniques is “Sandia Frequency Shift” (SFS), and this employs frequency drifting to identify extra Nondetection Zone. Inappropriate tuning parameters, such as the acceleration factor and chopping factor, result in frequency drifting, which degrades the quality of the power and causes the continuous power mode to fail. The failure can be overcome by considering optimized parameters to identify the nondetection zone (NDZ) in small DG system (120 kVA) at constant power/current mode. This article discusses, a new Adaptive Fuzzy Particle Swarm Optimization-SFS (AFPSO-SFS) technique is recommended to attain optimal parameters of SFS anti-islanding detection technique for validation of the proposed algorithm Particle Swarm Optimization–SFS (PSO-SFS) is also implemented. Furthermore, the ability of this method is to identify an islanding situation which deteriorates the major scientific issues for small DG system. Thus, the optimized parameter helps to attain an anti-islanding as well as less impact on power quality. Moreover, this article concerns serious issue of the grid interconnected DG model in an islanding condition, when the utility line would be unintentionally isolated. The grid detachment is generally considered as a fault. When an islanding occurs, the DG protection system must be inactive to detect the fault. For the investigation of power mismatches, the output of considered system using the proposed methods is contrast with results obtained from PSO-SFS and conventional SFS. The recommended strategy analyzes islands more effectively and diagnoses nondetection zones more accurately. The recommended method’s reliability is calculated quantitatively and emulated with MATLAB/Simulink.

Underwater terrain-matching algorithm based on improved iterative closest contour point algorithm

Although an autonomous underwater vehicle (AUV) is noted for its good autonomy, concealment and anti-interference ability, errors in its inertial navigation system (INS) inevitably increase over time, leading to positional failure during long-term voyages. Terrain-assisted navigation can help the INS to correct its position. The traditional iterative closest contour point (ICCP) achieves high matching accuracy when the initial position error of the INS is small, but is prone to mismatching when the initial error is large. This study combines ICCP with particle swarm optimization (PSO) to overcome this problem. First, the global optimization ability of PSO is improved by changing the acceleration factor and introducing an artificial bee colony (ABC) onlooker bee greedy search (ABC- ωAPSO). Second, the Euclidean distance of ICCP is replaced by the Mahalanobis distance to abate the influence of system error on the matching accuracy. Finally, the initial position error is reduced by rough matching using the ABC- ωAPSO, which has global optimization capability. Next, fine matching is performed by ICCP. This two-step process resolves the sensitivity problem of ICCP to the initial position error. The experimental results revealed a good matching effect after the double-matching procedure. When the initial INS errors were 0.55′ to the east and 0.55′ to the north, the matching error was reduced to 89.3 m, suggesting that the approach can realize autonomous passive navigation of AUVs.

Multi-average high-acceleration modified volumetric interpolated breath-hold examination (VIBE) for free-breathing multiphase contrast-enhanced liver MRI: a comparative study with breath-hold VIBE.

Breath-hold volumetric interpolated breath-hold examination (BH-VIBE) of multiphase contrast-enhanced liver magnetic resonance imaging (MPCE-LMRI) requires good cooperative individuals to comply with multiple breath-holds. To develop a free-breathing modified VIBE (FB-mVIBE) as a substitute of BH-VIBE in MPCE-LMRI. We modified VIBE with a high acceleration factor (2 × 2) and four averages to produce the mVIBE scan. A total of 90 individuals (40 men; mean age = 54.6 ± 10.0 years) who had received MPCE-LMRI as part of a voluntary health check-up for oncology survey were enrolled. Each participant was scanned in four phases (pre-contrast, arterial phase, venous phase, and delay phase), and each phase had two sequential scans. To encounter the timing effect of contrast enhancement, three scan orders were designed: BH-VIBE and FB-mVIBE (group A, n = 30); BH-VIBE and FB-VIBE (group B, n = 30); and FB-mVIBE and BH-VIBE (group C, n = 30). The comparisons included the objective measurements and 25 visual-score by two abdominal radiologists independently. Consistency between raters was observed for all three sequences (intraclass correlation coefficient [ICC] = 0.741-0.829). For rater 1, the mean scores of FB-mVIBE (23.67 ± 1.32) were equal to those of BH-VIBE (23.83 ± 1.98) in groups C and B (P = 0.852). The mean scores of FB-mVIBE (22.07 ± 3.02), but significantly higher than those of FB-VIBE (14.7 ± 3.41) in groups A and B (P <0.001). Similar scores were found for rater 2. The objective measurement of FB-mVIBE were equal to or higher than BH-VIBE and markedly superior to FB-VIBE. FB-mVIBE is a practical alternative to BH-VIBE for individuals who cannot cooperate with multiple breath-holds for MPCE-LMRI.

JUST-Net: Jointly unrolled cross-domain optimization based spatio-temporal reconstruction network for accelerated 3D myelin water imaging.

We introduced a novel reconstruction network, jointly unrolled cross-domain optimization-based spatio-temporal reconstruction network (JUST-Net), aimed at accelerating 3D multi-echo gradient-echo (mGRE) data acquisition and improving the quality of resulting myelin water imaging (MWI) maps. An unrolled cross-domain spatio-temporal reconstruction network was designed. The main idea is to combine frequency and spatio-temporal image feature representations and to sequentially implement convolution layers in both domains. The k-space subnetwork utilizes shared information from adjacent frames, whereas the image subnetwork applies separate convolutions in both spatial and temporal dimensions. The proposed reconstruction network was evaluated for both retrospectively and prospectively accelerated acquisition. Furthermore, it was assessed in simulation studies and real-world cases with k-space corruptions to evaluate its potential for motion artifact reduction. The proposed JUST-Net enabled highly reproducible and accelerated 3D mGRE acquisition for whole-brain MWI, reducing the acquisition time from fully sampled 15:23 to 2:22 min within a 3-min reconstruction time. The normalized root mean squared error of the reconstructed mGRE images increased by less than 4.0%, and the correlation coefficients for MWI showed a value of over 0.68 when compared to the fully sampled reference. Additionally, the proposed method demonstrated a mitigating effect on both simulated and clinical motion-corrupted cases. The proposed JUST-Net has demonstrated the capability to achieve high acceleration factors for 3D mGRE-based MWI, which is expected to facilitate widespread clinical applications of MWI.