Accelerated partial separable model using dimension-reduced optimization technique for ultra-fast cardiac MRI

Objective. Imaging dynamic object with high temporal resolution is challenging in magnetic resonance imaging (MRI). Partial separable (PS) model was proposed to improve the imaging quality by reducing the degrees of freedom of the inverse problem. However, PS model still suffers from long acquisition time and even longer reconstruction time. The main objective of this study is to accelerate the PS model, shorten the time required for acquisition and reconstruction, and maintain good image quality simultaneously. Approach. We proposed to fully exploit the dimension reduction property of the PS model, which means implementing the optimization algorithm in subspace. We optimized the data consistency term, and used a Tikhonov regularization term based on the Frobenius norm of temporal difference. The proposed dimension-reduced optimization technique was validated in free-running cardiac MRI. We have performed both retrospective experiments on public dataset and prospective experiments on in-vivo data. The proposed method was compared with four competing algorithms based on PS model, and two non-PS model methods. Main results. The proposed method has robust performance against shortened acquisition time or suboptimal hyper-parameter settings, and achieves superior image quality over all other competing algorithms. The proposed method is 20-fold faster than the widely accepted PS+Sparse method, enabling image reconstruction to be finished in just a few seconds. Significance. Accelerated PS model has the potential to save much time for clinical dynamic MRI examination, and is promising for real-time MRI applications.Comment: 23 pages, 11 figures. Accepted as manuscript on Physics in Medicine & Biolog

Compound Attention and Neighbor Matching Network for Multi-contrast MRI Super-resolution

Multi-contrast magnetic resonance imaging (MRI) reflects information about human tissue from different perspectives and has many clinical applications. By utilizing the complementary information among different modalities, multi-contrast super-resolution (SR) of MRI can achieve better results than single-image super-resolution. However, existing methods of multi-contrast MRI SR have the following shortcomings that may limit their performance: First, existing methods either simply concatenate the reference and degraded features or exploit global feature-matching between them, which are unsuitable for multi-contrast MRI SR. Second, although many recent methods employ transformers to capture long-range dependencies in the spatial dimension, they neglect that self-attention in the channel dimension is also important for low-level vision tasks. To address these shortcomings, we proposed a novel network architecture with compound-attention and neighbor matching (CANM-Net) for multi-contrast MRI SR: The compound self-attention mechanism effectively captures the dependencies in both spatial and channel dimension; the neighborhood-based feature-matching modules are exploited to match degraded features and adjacent reference features and then fuse them to obtain the high-quality images. We conduct experiments of SR tasks on the IXI, fastMRI, and real-world scanning datasets. The CANM-Net outperforms state-of-the-art approaches in both retrospective and prospective experiments. Moreover, the robustness study in our work shows that the CANM-Net still achieves good performance when the reference and degraded images are imperfectly registered, proving good potential in clinical applications.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Using FBGS to estimate the horizontal response of a monopile in a geotechnical centrifuge

A circular aluminium tube 50 mm in diameter was instrumented with two optical fibres, each of which consists of 13 fibre Bragg grating sensors (FBGS). The performance of the FBGS was evaluated by applying a series of increasing transversal loads at 1g level and comparing the strains measured by FBGS with those calculated from the Euler−Bernoulli beam theory. A centrifuge test was then conducted at 100g to estimate the transversal response of the calibrated model pile that had been jacked 450 mm into saturated sand and horizontally loaded at 500 mm above the ground. The profiles of the normal strain, bending moment, soil reaction and pile deflection were measured or determined, enabling the plotting of the soil reaction-pile deflection (P-y) curves. The results confirmed the reliability of FBGS at 100g by yielding satisfactory measurements on bending moments and coherent measurements on shear force at the ground level.Peer reviewe

Hydro-mechanical behavior from small strain to failure of tuffs amended with dune sand - application to pavements design in Saharan areas

In the context of valorizing local materials at Saharan region for road constructions, a mixture composed of 65% tuff and 35% dune sand (named 65T35DS) was previously studied. The 65T35DS was shown to have the optimum geotechnical and mechanical properties. As an extension, this study consists in investigating the behavior of the 65T35DS mixture under coupled hydro-mechanical loadings. Suction-controlled drying-wetting tests were performed on the 65T35DS statically compacted at the modified Proctor optimum (MPO) state. A series of small-strain cyclic triaxial tests under constant water content condition was then followed. Key findings of this paper include i) the 65T35DS prepared at MPO state is able to resist severe drought condition without inducing significant shrinkage. However, it swells during wetting and the swelling indices are quantified by correlations; ii) the elastic modulus under small-strains is governed by suction and the applied confining stress. The effect of confining stress becomes dominantat lower suction level and negligible when suction increases; iii) at failure, the apparent cohesion drops quickly on the dry side of the MPO, on wet side, it decreases a little. The apparent friction angle decreases for the specimens with water content larger than 5.5%, i.e. wMPO - 5. The studied mixture satisfies the design standards of Saharan pavements and seems to be a good compromise for the valorization of local materials.Peer reviewe

Comment une pollution par le plomb influence-t-elle les propriétés d’une argile ?

International audienceIn presence of water, the permeability of a carbonated clay is equal to 7.1 10-11 ms-1. In presence of lead at concentrations equal to 0.01 and 1 M, it becomes equal to 2.4 10-11 and 8.8 10-11 ms-1. Mercury Intrusion porosimetry shows that in the presence of lead, a reorganization of the pores distribution occurs. Chemical analysis show that, for the concentration 0.01 M, lead precipitates with carbonate whereas, for the concentration 1 M, the acidic pH of the samples leads to the carbonate dissolution.La perméabilité d’une argile carbonatée saturée à l’eau est égale à 7,1 10-11 ms-1. En présence du plomb à 0,01 et 1 M, elle passe respectivement à 2,4 10-11 et 8,8 10-11 ms-1.La porosimétrie au mercure montre que la présence de plomb se traduit par une réorganisation de la distribution des pores. Les analyses montrent que, pour la concentration 0,01 M, le plomb précipite sous forme de précipités de carbonate alors que, pour la concentration 1 M, le pH acide des échantillons favorise la dissolution du carbonate

Radar Spectrum Image Classification Based on Deep Learning

With the continuous development and progress of science and technology, the increasingly complex electromagnetic environment and the research and development of new radar systems have led to the emergence of various radar signals. Traditional methods of radar emitter identification cannot meet the needs of current practical applications. For the purpose of classification and recognition of radar emitter signals, this paper proposes an improved EfficientNetv2-s classification method based on deep learning for more precise classification and recognition of radar radiation source signals. Using 16 different types of radar signal parameters from the signal parameter setting table, the proposed method generates random data sets consisting of spectrum images with varying amplitude. The proposed method replaces two-dimensional convolution in EfficientNetV2 with one-dimensional convolution. Additionally, the channel attention mechanism of the EfficientNetv2-s is optimized and modified to obtain attention weights without dimensional reduction, resulting in superior accuracy. Compared with other deep-learning image-classification methods, the test results of this method have better classification accuracy on the test set: the top1 accuracy reaches 98.12%, which is 0.17~3.12% higher than other methods. Furthermore, the proposed method has lower complexity compared to most methods

Four-Dimensionally Printed Continuous Carbon Fiber-Reinforced Shape Memory Polymer Composites with Diverse Deformation Based on an Inhomogeneous Temperature Field

Four-dimensionally printed continuous carbon fiber-reinforced shape memory polymer composite (CFSMPC) is a smart material with the ability to bear loads and undergo deformation. The deformation of CFSMPC can be driven by the electrothermal effect of carbon fibers. In this study, the effect of temperature on the shape memory recovery performance of polylactic acid (PLA) was first studied experimentally. Continuous carbon fibers were incorporated into PLA to design CFSMPCs with thickness gradients and hand-shaped structures, respectively. The distribution strategy of the carbon fibers was determined based on simulations of the electrically driven shape recovery process of the aforementioned structures. Both the simulations and experiments demonstrated that the electrification of the CFSMPC structures resulted in an inhomogeneous temperature field, leading to distinct deformation recovery processes. Eventually, a precise unfolding was achieved for the thickness gradient structure and the five fingers in the hand-shaped structure by utilizing a safe voltage of 6 V. This demonstrates that the 4D-printed CFSMPC with diverse deformations based on an inhomogeneous temperature field has potential applications in actuators, reconfigurable devices, and other fields

an empirically-based process to improve the practice of requirement review

Int Software Proc Asso c, Inst Software, Chinese Acad Sci, ISCAS Lab Internet Software TechnolRequirement quality serves as the basis of the whole software development. How to improve and assure the quality of requirements is one of the most difficult issues. Aiming to improve requirement review in a software company, we propose a rol

Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation

Deep neural networks (DNNs) achieve promising performance in visual recognition under the independent and identically distributed (IID) hypothesis. In contrast, the IID hypothesis is not universally guaranteed in numerous real-world applications, especially in medical image analysis. Medical image segmentation is typically formulated as a pixel-wise classification task in which each pixel is classified into a category. However, this formulation ignores the hard-to-classified pixels, e.g., some pixels near the boundary area, as they usually confuse DNNs. In this paper, we first explore that hard-to-classified pixels are associated with high uncertainty. Based on this, we propose a novel framework that utilizes uncertainty estimation to highlight hard-to-classified pixels for DNNs, thereby improving its generalization. We evaluate our method on two popular benchmarks: prostate and fundus datasets. The results of the experiment demonstrate that our method outperforms state-of-the-art methods.Comment: 11 pages, 3 figure