Effect of dual sintering with laser irradiation and thermal treatment on printed copper nanoparticle patterns.

The dual sintering of copper (Cu) nanoparticles (NPs) was introduced to produce conductive patterns suitable for flexible electronics applications. In this method, laser irradiation using a Nd:YAG laser with a wavelength of 1064 nm was performed at laser powers of 400, 600 and 800 mJ. The laser irradiation time was 15 and 30 s for each laser power. After laser irradiation, all of the Cu NP patterns were thermally sintered under formic acid vapors. The temperature and time for thermal treatment were selected as 260 °C and 15 min, respectively. The resultant physical, chemical, electrical and mechanical properties were evaluated and compared considering the six different dual sintering conditions. The Cu NP patterns sintered using 800 mJ for 30 s showed increased necking and coalescence compared to the other patterns and featured a microstructure with increased density. Despite being oxidized, the Cu NP patterns sintered with 800 mJ for 30 s showed the lowest electrical resistivity of 11.25µΩ cm. The surface of every sintered Cu pattern was oxidized, and mechanical hardness increased with increasing laser power. The Cu NP pattern sintered with 800 mJ for 30 s demonstrated the highest hardness of 48.64 N mm-2. After sintering using the six different conditions, the Cu NP patterns exhibited a weight loss of 0.02-3.87 wt%, and their roughness varied in the range of 26.15-74.08 nm. This can be attributed to the effective removal of organic residues and the degree of particle agglomeration. After performing folding tests up to 50 cycles, Cu NP patterns showed an upward trend in resistance with increasing laser power and time. The highest and lowest resistance ratios were observed as 3.97 and 17.24 for the patterns sintered at 400 mJ for 15 s and 800 mJ for 30 s, respectively.

Vespa: Integrated applications for RF pulse design, spectral simulation and MRS data analysis.

PURPOSE: The Vespa package (Versatile Simulation, Pulses, and Analysis) is described and demonstrated. It provides workflows for developing and optimizing linear combination modeling (LCM) fitting for 1 H MRS data using intuitive graphical user interface interfaces for RF pulse design, spectral simulation, and MRS data analysis. Command line interfaces for embedding workflows in MR manufacturer platforms and utilities for synthetic dataset creation are included. Complete provenance is maintained for all steps in workflows. THEORY AND METHODS: Vespa is written in Python for compatibility across operating systems. It embeds the PyGAMMA spectral simulation library for spectral simulation. Multiprocessing methods accelerate processing and visualization. Applications use the Vespa database for results storage and cross-application access. Three projects demonstrate pulse, sequence, simulation, and data analysis workflows: (1) short TE semi-LASER single-voxel spectroscopy (SVS) LCM fitting, (2) optimizing MEGA-PRESS (MEscher-GArwood Point RESolved Spectroscopy) flip angle and LCM fitting, and (3) creating a synthetic short TE dataset. RESULTS: The LCM workflows for in vivo basis set creation and spectral analysis showed reasonable results for both the short TE semi-LASER and MEGA-PRESS. Examples of pulses, simulations, and data fitting are shown in Vespa application interfaces for various steps to demonstrate the interactive workflow. CONCLUSION: Vespa provides an efficient and extensible platform for characterizing RF pulses, pulse design, spectral simulation optimization, and automated LCM fitting via an interactive platform. Modular design and command line interface make it easy to embed in other platforms. As open source, it is free to the MRS community for use and extension. Vespa source code and documentation are available through GitHub.

A Standardized and Regionalized Network of Care for Cardiogenic Shock.

BACKGROUND: The benefits of standardized care for cardiogenic shock (CS) across regional care networks are poorly understood. OBJECTIVES: The authors compared the management and outcomes of CS patients initially presenting to hub versus spoke hospitals within a regional care network. METHODS: The authors stratified consecutive patients enrolled in their CS registry (January 2017 to December 2019) by presentation to a spoke versus the hub hospital. The primary endpoint was 30-day mortality. Secondary endpoints included bleeding, stroke, or major adverse cardiovascular and cerebrovascular events. RESULTS: Of 520 CS patients, 286 (55%) initially presented to 34 spoke hospitals. No difference in mean age (62 years vs 61 years; P = 0.38), sex (25% vs 32% women; P = 0.10), and race (54% vs 52% white; P = 0.82) between spoke and hub patients was noted. Spoke patients more often presented with acute myocardial infarction (50% vs 32%; P < 0.01), received vasopressors (74% vs 66%; P = 0.04), and intra-aortic balloon pumps (88% vs 37%; P < 0.01). Hub patients were more often supported with percutaneous ventricular assist devices (44% vs 11%; P < 0.01) and veno-arterial extracorporeal membrane oxygenation (13% vs 0%; P < 0.01). Initial presentation to a spoke was not associated with increased risk-adjusted 30-day mortality (adjusted OR: 0.87 [95% CI: 0.49-1.55]; P = 0.64), bleeding (adjusted OR: 0.89 [95% CI: 0.49-1.62]; P = 0.70), stroke (adjusted OR: 0.74 [95% CI: 0.31-1.75]; P = 0.49), or major adverse cardiovascular and cerebrovascular events (adjusted OR 0.83 [95% CI: 0.50-1.35]; P = 0.44). CONCLUSIONS: Spoke and hub patients experienced similar short-term outcomes within a regionalized CS network. The optimal strategy to promote standardized care and improved outcomes across regional CS networks merits further investigation.

Cardiogenic Shock From Heart Failure Versus Acute Myocardial Infarction: Clinical Characteristics, Hospital Course, and 1-Year Outcomes.

BACKGROUND: Little is known about clinical characteristics, hospital course, and longitudinal outcomes of patients with cardiogenic shock (CS) related to heart failure (HF-CS) compared to acute myocardial infarction (AMI; CS related to AMI [AMI-CS]). METHODS: We examined in-hospital and 1-year outcomes of 520 (219 AMI-CS, 301 HF-CS) consecutive patients with CS (January 3, 2017-December 31, 2019) in a single-center registry. RESULTS: Mean age was 61.5±13.5 years, 71% were male, 22% were Black patients, and 63% had chronic kidney disease. The HF-CS cohort was younger (58.5 versus 65.6 years, P<0.001), had fewer cardiac arrests (15.9% versus 35.2%, P<0.001), less vasopressor utilization (61.8% versus 82.2%, P<0.001), higher pulmonary artery pulsatility index (2.14 versus 1.51, P<0.01), lower cardiac power output (0.64 versus 0.77 W, P<0.01) and higher pulmonary capillary wedge pressure (25.4 versus 22.2 mm Hg, P<0.001) than patients with AMI-CS. Patients with HF-CS received less temporary mechanical circulatory support (34.9% versus 76.3% P<0.001) and experienced lower rates of major bleeding (17.3% versus 26.0%, P=0.02) and in-hospital mortality (23.9% versus 39.3%, P<0.001). Postdischarge, 133 AMI-CS and 229 patients with HF-CS experienced similar rates of 30-day readmission (19.5% versus 24.5%, P=0.30) and major adverse cardiac and cerebrovascular events (23.3% versus 28.8%, P=0.45). Patients with HF-CS had lower 1-year mortality (n=123, 42.6%) compared to the patients with AMI-CS (n=110, 52.9%, P=0.03). Cumulative 1-year mortality was also lower in patients with HF-CS (log-rank test, P=0.04). CONCLUSIONS: Patients with HF-CS were younger, and despite lower cardiac power output and higher pulmonary capillary wedge pressure, less likely to receive vasopressors or temporary mechanical circulatory support. Although patients with HF-CS had lower in-hospital and 1-year mortality, both cohorts experienced similarly high rates of postdischarge major adverse cardiovascular and cerebrovascular events and 30-day readmission, highlighting that both cohorts warrant careful long-term follow-up. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT03378739.

Complication of venovenous extracorporeal membrane oxygenation cannulation - the significance of an inferior vena cava anomaly.

Physicians should be aware of possible anatomical variants during cannulation for extracorporeal membrane oxygenation (ECMO). Particular attention to ensure continual visualization of the guidewire before proceeding to final positioning of the ECMO cannulae should be paid. Alternative imaging modalities should be contemplated when uncertainties arise to minimize the risk of inadvertent vascular injuries.

Millimeter-wave broadband antireflection coatings using laser ablation of subwavelength structures.

We report on the first use of laser ablation to make submillimeter, broadband, antireflection coatings (ARCs) based on subwavelength structures (SWSs) on alumina and sapphire. We used a 515 nm laser to produce pyramid-shaped structures with a pitch of about 320 µm and a total height of near 800 µm. Transmission measurements between 70 and 140 GHz are in agreement with simulations using electromagnetic propagation software. The simulations indicate that SWS-ARCs with the fabricated shape should have a fractional bandwidth response of Δν/νcenter=0.55 centered on 235 GHz for which reflections are below 3%. Extension of the bandwidth to both lower and higher frequencies, between a few tens of gigahertz and a few terahertz, should be straightforward with appropriate adjustment of laser ablation parameters.

Quantitative framework for prospective motion correction evaluation.

PURPOSE: Establishing a framework to evaluate performances of prospective motion correction (PMC) MRI considering motion variability between MRI scans. METHODS: A framework was developed to obtain quantitative comparisons between different motion correction setups, considering that varying intrinsic motion patterns between acquisitions can induce bias. Intrinsic motion was considered by replaying in a phantom experiment the recorded motion trajectories from subjects. T1-weighted MRI on five volunteers and two different marker fixations (mouth guard and nose bridge fixations) were used to test the framework. Two metrics were investigated to quantify the improvement of the image quality with PMC. RESULTS: Motion patterns vary between subjects as well as between repeated scans within a subject. This variability can be approximated by replaying the motion in a distinct phantom experiment and used as a covariate in models comparing motion corrections. We show that considering the intrinsic motion alters the statistical significance in comparing marker fixations. As an example, two marker fixations, a mouth guard and a nose bridge, were evaluated in terms of their effectiveness for PMC. A mouth guard achieved better PMC performance. CONCLUSION: Intrinsic motion patterns can bias comparisons between PMC configurations and must be considered for robust evaluations. A framework for evaluating intrinsic motion patterns in PMC is presented.

Directed network motifs in Alzheimer's disease and mild cognitive impairment.

Directed network motifs are the building blocks of complex networks, such as human brain networks, and capture deep connectivity information that is not contained in standard network measures. In this paper we present the first application of directed network motifs in vivo to human brain networks, utilizing recently developed directed progression networks which are built upon rates of cortical thickness changes between brain regions. This is in contrast to previous studies which have relied on simulations and in vitro analysis of non-human brains. We show that frequencies of specific directed network motifs can be used to distinguish between patients with Alzheimer's disease (AD) and normal control (NC) subjects. Especially interesting from a clinical standpoint, these motif frequencies can also distinguish between subjects with mild cognitive impairment who remained stable over three years (MCI) and those who converted to AD (CONV). Furthermore, we find that the entropy of the distribution of directed network motifs increased from MCI to CONV to AD, implying that the distribution of pathology is more structured in MCI but becomes less so as it progresses to CONV and further to AD. Thus, directed network motifs frequencies and distributional properties provide new insights into the progression of Alzheimer's disease as well as new imaging markers for distinguishing between normal controls, stable mild cognitive impairment, MCI converters and Alzheimer's disease.

Directed progression brain networks in Alzheimer's disease: properties and classification.

This article introduces a new approach in brain connectomics aimed at characterizing the temporal spread in the brain of pathologies like Alzheimer's disease (AD). The main instrument is the development of "directed progression networks" (DPNets), wherein one constructs directed edges between nodes based on (weakly) inferred directions of the temporal spreading of the pathology. This stands in contrast to many previously studied brain networks where edges represent correlations, physical connections, or functional progressions. In addition, this is one of a few studies showing the value of using directed networks in the study of AD. This article focuses on the construction of DPNets for AD using longitudinal cortical thickness measurements from magnetic resonance imaging data. The network properties are then characterized, providing new insights into AD progression, as well as novel markers for differentiating normal cognition (NC) and AD at the group level. It also demonstrates the important role of nodal variations for network classification (i.e., the significance of standard deviations, not just mean values of nodal properties). Finally, the DPNets are utilized to classify subjects based on their global network measures using a variety of data-mining methodologies. In contrast to most brain networks, these DPNets do not show high clustering and small-world properties.

A two-level multimodality imaging Bayesian network approach for classification of partial epilepsy: preliminary data.

BACKGROUND: Quantitative neuroimaging analyses have demonstrated gray and white matter abnormalities in group comparisons of different types of non-lesional partial epilepsy. It is unknown to what degree these type-specific patterns exist in individual patients and if they could be exploited for diagnostic purposes. In this study, a two-level multi-modality imaging Bayesian network approach is proposed that uses information about individual gray matter volume loss and white matter integrity to classify non-lesional temporal lobe epilepsy with (TLE-MTS) and without (TLE-no) mesial-temporal sclerosis and frontal lobe epilepsy (FLE). METHODS: 25 controls, 19 TLE-MTS, 22 TLE-no and 14 FLE were studied on a 4T MRI and T1 weighted structural and DTI images acquired. Spatially normalized gray matter (GM) and fractional anisotropy (FA) abnormality maps (binary maps with voxels 1 SD below control mean) were calculated for each subject. At the first level, each group's abnormality maps were compared with those from all the other groups using Graphical-Model-based Morphometric Analysis (GAMMA). GAMMA uses a Bayesian network and a Markov random field based contextual clustering method to produce maps of voxels that provide the maximal distinction between two groups and calculates a probability distribution and a group assignment based on this information. The information was then combined in a second level Bayesian network and the probability of each subject to belong to one of the three epilepsy types calculated. RESULTS: The specificities of the two level Bayesian network to distinguish between the three patient groups were 0.87 for TLE-MTS and TLE-no and 0.86 for FLE, the corresponding sensitivities were 0.84 for TLE-MTS, 0.72 for TLE-no and 0.64 for FLE. CONCLUSION: The two-level multi-modality Bayesian network approach was able to distinguish between the three epilepsy types with a reasonably high accuracy even though the majority of the images were completely normal on visual inspection.

Improved pseudo-continuous arterial spin labeling for mapping brain perfusion.

PURPOSE: To investigate arterial spin labeling (ASL) methods for improved brain perfusion mapping. Previously, pseudo-continuous ASL (pCASL) was developed to overcome limitations inherent with conventional continuous ASL (CASL), but the control scan (null pulse) in the original method for pCASL perturbs the equilibrium magnetization, diminishing the ASL signal. Here, a new modification of pCASL, termed mpCASL is reported, in which the perturbation caused by the null pulse is reduced and perfusion mapping improved. MATERIALS AND METHODS: improvements with mpCASL are demonstrated using numerical simulations and experiments. ASL signal intensity as well as contrast and reproducibility of in vivo brain perfusion images were measured in four volunteers who had MRI scans at 4 Tesla and the data compared across the labeling methods. RESULTS: Perfusion maps with mpCASL showed, on average, higher ASL signal intensity and higher image contrast than those from CASL or pCASL. Furthermore, mpCASL yielded better reproducibility in repeat scans than the other methods. CONCLUSION: The experimental results are consistent with the hypothesis that the new null pulse of mpCASL leads to improved brain perfusion images.

Bayesian k -space-time reconstruction of MR spectroscopic imaging for enhanced resolution.

A k-space-time Bayesian statistical reconstruction method (K-Bayes) is proposed for the reconstruction of metabolite images of the brain from proton (1H) magnetic resonance (MR) spectroscopic imaging (MRSI) data. K-Bayes performs full spectral fitting of the data while incorporating structural (anatomical) spatial information through the prior distribution. K-Bayes provides increased spatial resolution over conventional discrete Fourier transform (DFT) based methods by incorporating structural information from higher resolution coregistered and segmented structural MR images. The structural information is incorporated via a Markov random field (MRF) model that allows for differential levels of expected smoothness in metabolite levels within homogeneous tissue regions and across tissue boundaries. By further combining the structural prior model with a k -space-time MRSI signal and noise model (for a specific set of metabolites and based on knowledge from prior spectral simulations of metabolite signals), the impact of artifacts generated by low-resolution sampling is also reduced. The posterior-mode estimates are used to define the metabolite map reconstructions, obtained via a generalized expectation-maximization algorithm. K-Bayes was tested using simulated and real MRSI datasets consisting of sets of k-space-time-series (the recorded free induction decays). The results demonstrated that K-Bayes provided qualitative and quantitative improvement over DFT methods.

In-vivo investigation of the human cingulum bundle using the optimization of MR diffusion spectrum imaging.

Diffusion spectrum imaging (DSI) is a generalization of diffusion tensor imaging to map fibrous structure of white matter and potentially very sensitive to alterations of the cingulum bundles in dementia. In this in-vivo 4T study, DSI parameters especially spatial resolution and diffusion encoding bandwidth were optimized on humans to segment the cingulum bundles for tract level measurements of diffusion. The careful tailoring of the DSI acquisitions in conjunction with fiber tracking provided an optimal DSI setting for a reliable quantification of the cingulum bundle tracts. The optimization of tracking the cingulum bundle was verified using fiber tract quantifications, including coefficients of variability of DSI measurements along the fibers between and within healthy subjects in back-to-back studies and variogram analysis of spatial correlations between diffusion orientation distribution functions (ODF) along the cingulum bundle tracts. The results demonstrate that the identification of the cingulum bundle in human brain is reproducible using an optimized DSI parameter for maximum b-value and high spatial resolution of the DSI acquisition with a feasible acquisition time of whole brain in clinical practice. This optimized DSI setting should be useful for detecting alterations along the cingulum bundle in Alzheimer disease and related neurodegenerative disorders.

K-Bayes reconstruction for perfusion MRI. I: concepts and application.

Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities, such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. In this study, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach (described in detail in Part II: Modeling and Technical Development) combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT.

K-Bayes reconstruction for perfusion MRI II: modeling and technical development.

Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. Here, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study, described in Part I (Concepts and Applications), was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT.

Multivariate statistical mapping of spectroscopic imaging data.

For magnetic resonance spectroscopic imaging studies of the brain, it is important to measure the distribution of metabolites in a regionally unbiased way; that is, without restrictions to a priori defined regions of interest. Since magnetic resonance spectroscopic imaging provides measures of multiple metabolites simultaneously at each voxel, there is furthermore great interest in utilizing the multidimensional nature of magnetic resonance spectroscopic imaging for gains in statistical power. Voxelwise multivariate statistical mapping is expected to address both of these issues, but it has not been previously employed for spectroscopic imaging (SI) studies of brain. The aims of this study were to (1) develop and validate multivariate voxel-based statistical mapping for magnetic resonance spectroscopic imaging and (2) demonstrate that multivariate tests can be more powerful than univariate tests in identifying patterns of altered brain metabolism. Specifically, we compared multivariate to univariate tests in identifying known regional patterns in simulated data and regional patterns of metabolite alterations due to amyotrophic lateral sclerosis, a devastating brain disease of the motor neurons.