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1.
  • Bourgoin, M., et al. (författare)
  • Investigation of the small-scale statistics of turbulence in the Modane S1MA wind tunnel
  • 2018
  • Ingår i: CEAS Aeronautical Journal. - : Springer. - 1869-5582 .- 1869-5590. ; 9:2, s. 269-281
  • Tidskriftsartikel (refereegranskat)abstract
    • This article describes the planning, set-up, turbulence characterization and analysis of measurements of a passive grid turbulence experiment that was carried out in the S1MA wind-tunnel from ONERA in Modane, in the context of the ESWIRP European project. This experiment aims at a detailed investigation of the statistical properties of turbulent flows at large Reynolds numbers. The primary goal is to take advantage of the unequaled large-scale dimensions of the ONERA S1MA wind-tunnel facility, to make available to the broad turbulence community high-quality experimental turbulence data with unprecendented resolution (both spatial and temporal) and accuracy (in terms of statistical convergence). With this goal, we designed the largest grid-generated turbulence experiment planned and performed to date. Grid turbulence is a canonical flow known to produce almost perfectly homogeneous and isotropic turbulence (HIT) which remains a unique framework to investigate fundamental physics of turbulent flows. Here, we present a brief description of the measurements, in particular those based on hot-wire diagnosis. By comparing results from classical hot-wires and from a nano-fabricated wire (developed at Princeton University), we show that our goal of resolving down to the smallest dissipative scales of the flow has been achieved. We also present the full characterization of the turbulence here, in terms of turbulent energy dissipation rate, injection and dissipation scales (both spatial and temporal) and Reynolds number.
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2.
  • Etminani, Kobra, 1984-, et al. (författare)
  • A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimers disease, and mild cognitive impairment using brain 18F-FDG PET
  • 2022
  • Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - New York : Springer. - 1619-7070 .- 1619-7089. ; 49, s. 563-584
  • Tidskriftsartikel (refereegranskat)abstract
    • Purpose The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimers disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimers disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare models performance to that of multiple expert nuclear medicine physicians readers. Materials and methods Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimers disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The models performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. Results The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. Conclusion Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.
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