Dataset Information

Suppression of Shear Banding and Transition to Necking and Homogeneous Flow in Nanoglass Nanopillars.

ABSTRACT: In order to improve the properties of metallic glasses (MG) a new type of MG structure, composed of nanoscale grains, referred to as nanoglass (NG), has been recently proposed. Here, we use large-scale molecular dynamics (MD) simulations of tensile loading to investigate the deformation and failure mechanisms of Cu64Zr36 NG nanopillars with large, experimentally accessible, 50?nm diameter. Our results reveal NG ductility and failure by necking below the average glassy grain size of 20?nm, in contrast to brittle failure by shear band propagation in MG nanopillars. Moreover, the results predict substantially larger ductility in NG nanopillars compared with previous predictions of MD simulations of bulk NG models with columnar grains. The results, in excellent agreement with experimental data, highlight the substantial enhancement of plasticity induced in experimentally relevant MG samples by the use of nanoglass architectures and point out to exciting novel applications of these materials.

SUBMITTER: Adibi S

PROVIDER: S-EPMC4621512 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Suppression of Shear Banding and Transition to Necking and Homogeneous Flow in Nanoglass Nanopillars.

Adibi Sara S Branicio Paulo S PS Joshi Shailendra P SP

Scientific reports 20151027

In order to improve the properties of metallic glasses (MG) a new type of MG structure, composed of nanoscale grains, referred to as nanoglass (NG), has been recently proposed. Here, we use large-scale molecular dynamics (MD) simulations of tensile loading to investigate the deformation and failure mechanisms of Cu64Zr36 NG nanopillars with large, experimentally accessible, 50 nm diameter. Our results reveal NG ductility and failure by necking below the average glassy grain size of 20 nm, in con ...[more]

PMID: 26503114

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Project description:BackgroundCardiac balanced steady state free precession (bSSFP) cine imaging suffers from banding and flow artifacts induced by off-resonance. The work aimed to develop a twofold phase cycling sequence with a neural network-based reconstruction (2P-SSFP+Network) for a joint suppression of banding and flow artifacts in cardiac cine imaging.MethodsA dual-encoder neural network was trained on 1620 pairs of phase-cycled left ventricular (LV) cine images collected from 18 healthy subjects. Twenty healthy subjects and 25 patients were prospectively scanned using the proposed 2P-SSFP sequence. bSSFP cine of a single RF phase increment (1P-SSFP), bSSFP cine of a single radiofrequency (RF) phase increment with a network-based artifact reduction (1P-SSFP+Network), the averaging of the two phase-cycled images (2P-SSFP+Average), and the proposed method were mutually compared, in terms of artifact suppression performance in the LV, generalizability over altered scan parameters and scanners, suppression of large-area banding artifacts in the left atrium (LA), and accuracy of downstream segmentation tasks.ResultsIn the healthy subjects, 2P-SSFP+Network showed robust suppressions of artifacts across a range of phase combinations. Compared with 1P-SSFP and 2P-SSFP+Average, 2P-SSFP+Network improved banding artifacts (3.85 ± 0.67 and 4.50 ± 0.45 vs 5.00 ± 0.00, P < 0.01 and P = 0.02, respectively), flow artifacts (3.35 ± 0.78 and 2.10 ± 0.77 vs 4.90 ± 0.20, both P < 0.01), and overall image quality (3.25 ± 0.51 and 2.30 ± 0.60 vs 4.75 ± 0.25, both P < 0.01). 1P-SSFP+Network and 2P-SSFP+Network achieved a similar artifact suppression performance, yet the latter had fewer hallucinations (two-chamber, 4.25 ± 0.51 vs 4.85 ± 0.45, P = 0.04; four-chamber, 3.45 ± 1.21 vs 4.65 ± 0.50, P = 0.03; and left atrium (LA), 3.35 ± 1.00 vs 4.65 ± 0.45, P < 0.01). Furthermore, in the pulmonary veins and LA, 1P-SSFP+Network could not eliminate banding artifacts since they occupied a large area, whereas 2P-SSFP+Network reliably suppressed the artifacts. In the downstream automated myocardial segmentation task, 2P-SSFP+Network achieved more accurate segmentations than 1P-SSFP with different phase increments.Conclusions2P-SSFP+Network jointly suppresses banding and flow artifacts while manifesting a good generalizability against variations of anatomy and scan parameters. It provides a feasible solution for robust suppression of the two types of artifacts in bSSFP cine imaging.

Project description:PurposeTo develop a partially interpretable neural network for joint suppression of banding and flow artifacts in non-phase-cycled bSSFP cine imaging.MethodsA dual-stage neural network consisting of a voxel-identification (VI) sub-network and artifact-suppression (AS) sub-network is proposed. The VI sub-network provides identification of artifacts, which guides artifact suppression and improves interpretability. The AS sub-network reduces banding and flow artifacts. Short-axis cine images of 12 frequency offsets from 28 healthy subjects were used to train and test the dual-stage network. An additional 77 patients were retrospectively enrolled to evaluate its clinical generalizability. For healthy subjects, artifact suppression performance was analyzed by comparison with traditional phase cycling. The partial interpretability provided by the VI sub-network was analyzed via correlation analysis. Generalizability was evaluated for cine obtained with different sequence parameters and scanners. For patients, artifact suppression performance and partial interpretability of the network were qualitatively evaluated by 3 clinicians. Cardiac function before and after artifact suppression was assessed via left ventricular ejection fraction (LVEF).ResultsFor the healthy subjects, visual inspection and quantitative analysis found a considerable reduction of banding and flow artifacts by the proposed network. Compared with traditional phase cycling, the proposed network improved flow artifact scores (4.57 ± 0.23 vs 3.40 ± 0.38, P = 0.002) and overall image quality (4.33 ± 0.22 vs 3.60 ± 0.38, P = 0.002). The VI sub-network well identified the location of banding and flow artifacts in the original movie and significantly correlated with the change of signal intensities in these regions. Changes of imaging parameters or the scanner did not cause a significant change of overall image quality relative to the baseline dataset, suggesting a good generalizability. For the patients, qualitative analysis showed a significant improvement of banding artifacts (4.01 ± 0.50 vs 2.77 ± 0.40, P < 0.001), flow artifacts (4.22 ± 0.38 vs 2.97 ± 0.57, P < 0.001), and image quality (3.91 ± 0.45 vs 2.60 ± 0.43, P < 0.001) relative to the original cine. The artifact suppression slightly reduced the LVEF (mean bias = -1.25%, P = 0.01).ConclusionsThe dual-stage network simultaneously reduces banding and flow artifacts in bSSFP cine imaging with a partial interpretability, sparing the need for sequence modification. The method can be easily deployed in a clinical setting to identify artifacts and improve cine image quality.

Dataset Information

Suppression of Shear Banding and Transition to Necking and Homogeneous Flow in Nanoglass Nanopillars.

Publications

Suppression of Shear Banding and Transition to Necking and Homogeneous Flow in Nanoglass Nanopillars.

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