Reliable off-resonance correction in high-field cardiac MRI using autonomous cardiac B0 segmentation with dual-modality deep neural networks

B0 field inhomogeneity is a long-lasting issue for Cardiac MRI (CMR) in high-field (3T and above) scanners. The inhomogeneous B0 fields can lead to corrupted image quality, prolonged scan time, and false diagnosis. B0 shimming is the most straightforward way to improve the B0 homogeneity. However, today’s standard cardiac shimming protocol requires manual selection of a shim volume, which often falsely includes regions with large B0 deviation (e.g., liver, fat, and chest wall). The flawed shim field compromises the reliability of high-field CMR protocols, which significantly reduces the scan efficiency and hinders its wider clinical adoption. This study aims to develop a dual-channel deep learning model that can reliably contour the cardiac region for B0 shim without human interaction and under variable imaging protocols. By utilizing both the magnitude and phase information, the model achieved a high segmentation accuracy in the B0 field maps compared to the conventional single-channel methods (Dice score: 2D-mag = 0.866, 3D-mag = 0.907, and 3D-mag-phase = 0.938, all p < 0.05). Furthermore, it shows better generalizability against the common variations in MRI imaging parameters and enables significantly improved B0 shim compared to the standard method (SD(B0Shim): Proposed = 15 ± 11% vs. Standard = 6 ± 12%, p < 0.05). The proposed autonomous model can boost the reliability of cardiac shimming at 3T and serve as the foundation for more reliable and efficient high-field CMR imaging in clinical routines

Robust and Collision-Free Formation Control of Multi-Agent Systems with Limited Information

OnlinePublThis article investigates the collision-free cooperative formation control problem for second-order multiagent systems with unknown velocity, dynamics uncertainties, and limited reference information. An observer-based sliding mode control law is proposed to ensure both the convergence of the system’s tracking error and the boundedness of the relative distance between each pair of agents. First, two new finite-time neural-based observer designs are introduced to estimate both the agent velocity and the system uncertainty. The sliding mode differentiator is then employed for every agent to approximate the unknown derivatives of the formation reference to further construct the limited-information-based sliding mode controller. To ensure that the system is collision-free, artificial potential fields are introduced along with a time-varying topology. An example of a multiple omnidirectional robot system is used to conduct numerical simulations, and necessary comparisons are made to justify the effectiveness of the proposed limited information-based control scheme.Yang Fei, Peng Shi, and Cheng-Chew Li

Bulk substrate porosity verification by applying Monte Carlo modeling and Castaing’s formula using energy-dispersive x-rays

The leadframe fabrication process normally involves additional thin-metal layer plating on the bulk copper substrate surface for wire bonding purposes. Silver, tin, and copper flakes are commonly adopted as plating materials. It is critical to assess the density of the plated metal layer, and in particular to look for porosity or voids underneath the layer, which may reduce the reliability during high-temperature stress. A fast, reliable inspection technique is needed to assess the porosity or void weakness. To this end, the characteristics of x-rays generated from bulk samples were examined using an energy-dispersive x-ray (EDX) detector to examine the porosity percentage. Monte Carlo modeling was integrated with Castaing's formula to verify the integrity of the experimental data. Samples with different porosity percentages were considered to test the correlation between the intensity of the collected x-ray signal and the material density. To further verify the integrity of the model, conventional cross-sectional samples were also taken to observe the porosity percentage using Image J software measurement. A breakthrough in bulk substrate assessment was achieved by applying EDX for the first time to nonelemental analysis. The experimental data showed that the EDX features were not only useful for elemental analysis, but also applicable to thin-film metal layer thickness measurement and bulk material density determination. A detailed experiment was conducted using EDX to assess the plating metal layer and bulk material porosity

Stable isotopic characteristic of Taiwan's precipitation: A case study of western Pacific monsoon region

The stable oxygen and hydrogen isotopic features of precipitation in Taiwan, an island located at the western Pacific monsoon area, are presented from nearly 3,500 samples collected during the past decade for 20 stations. Results demonstrate that moisture sources from diverse air masses with different isotopic signals are the main parameter in controlling the precipitation's isotope characteristics. The air mass from polar continental (Pc) region contributes the precipitation with high deuterium excess values (up to 23%.) and relatively enriched isotope compositions (e.g., -3.2 parts per thousand for delta(18)O) during the winter with prevailing northeasterly monsoon. By contrast, air masses from equatorial maritime (Em) and tropical maritime (Tm) supply the precipitation with low deuterium excess values (as low as about 7 parts per thousand) and more depleted isotope values (e.g., -8.9 parts per thousand and -6.0 parts per thousand for delta(18)O of Tm and Em, respectively) during the summer with prevailing southwesterly monsoon. Thus seasonal differences in terms of delta(18)O, delta D, and deuterium excess values are primarily influenced by the interactions among various precipitation sources. While these various air masses travel through Taiwan, secondary evaporation effects further modify the isotope characteristics of the inland precipitation, such as raindrop evaporation (reduces the deuterium excess of winter precipitation) and Moisture recycling (increases the deuterium excess of summer precipitation). The semi-quantitative estimations in terms of evaluation for changes in the deuterium excess suggest that the raindrop evaporation fractions for winter precipitation range 7% to 15% and the proportions of recycling moisture in summer precipitation are less than 5%. Additionally, the isotopic altitude gradient in terms of delta(18)O for summer precipitation is -0.22 parts per thousand/100 m, greater than -0.17 parts per thousand/100 m of winter precipitation. The greater isotopic gradient in summer can be attributed to a higher temperature vs. altitude gradient relative to winter. The observed spatial and seasonal stable isotopic characteristics in Taiwan's precipitation not only contribute valuable information for regional monsoon research crossing the continent-ocean interface of East Asia, but also can serve as very useful database for local water resources management. (C) 2009 Elsevier B.V. All rights reserved

IGA-Reuse-NET: A deep-learning-based isogeometric analysis-reuse approach with topology-consistent parameterization[Formula presented]

In this paper, a deep learning framework combined with isogeometric analysis (IGA for short) called IGA-Reuse-Net is proposed for efficient reuse of numerical simulation on a set of topology-consistent models. Compared with previous data-driven numerical simulation methods only for simple computational domains, our method can predict high-accuracy PDE solutions over topology-consistent geometries with complex boundaries. UNet3+ architecture with interlaced sparse self-attention (ISSA) module is used to enhance the performance of the network. In addition, we propose a new loss function that combines a coefficients loss and a numerical solution loss. Several training datasets with topology-consistent models are constructed for the proposed framework. To verify the effectiveness of our approach, two different types of Poisson equations with different source functions are solved on three datasets with different topologies. Our framework can achieve a good trade-off between accuracy and efficiency. It outperforms the physics-informed neural network (PINN for short) model and yields promising results of prediction.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Materials and Manufacturin