Purpose: To qualitatively and quantitatively compare synthetic and conventional MRI sequences acquired on a 1.5-T system for patients with multiple sclerosis (MS).
Methods: Prospective study that involved twenty-seven consecutive relapsing-remitting MS patients scanned on a 1.5-T MRI scanner. The MRI protocol included 2D transverse conventional spin-echo sequences: proton density-weighted (PD), T2-weighted, T2-FLAIR, and T1-weighted. Synthetic images were generated using 2D transverse QRAPMASTER and SyMRI software with the same voxel size, repetition, echo, and inversion times as the conventional sequences. Four raters performed a crosstab qualitative analysis that involved evaluating global image quality, contrast, flow artefacts, and confidence in lesion assessment introducing the concepts of predominance, agreement, and disagreement. A quantitative analysis was also performed and included evaluating the number of lesions (periventricular, juxtacortical, brainstem, and cerebellum) and the contrast-to-noise ratio between regions (CSF, white matter, grey matter, lesions).
Results: The global image quality assessment showed predominance for better scores for conventional sequences over synthetic sequences, whereas contrast, confidence in lesion assessment, and flow artefacts showed predominance for agreement between sequences. There was predominance for disagreement between all pairs of raters in most of the evaluated qualitative parameters. Synthetic PD and T2-FLAIR images showed higher contrast-to-noise ratios than the corresponding conventional images for most comparison between regions. There were no significant differences in the number of lesions detected for most of the study regions between conventional and synthetic images.
Conclusion: Synthetic MRI can be potentially used as an alternative to conventional brain MRI sequences in the assessment of MS.
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http://dx.doi.org/10.1007/s00234-022-02973-2 | DOI Listing |
Mar Drugs
January 2025
Associate Laboratory i4HB, Institute for Health and Bioeconomy, NOVA Faculty of Sciences and Technology, NOVA University of Lisbon, Campus Caparica, 2829-516 Caparica, Portugal.
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Department of Food Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan.
Pneumonia, a leading cause of mortality in children under five, is usually diagnosed through chest X-ray (CXR) images due to its efficiency and cost-effectiveness. However, the shortage of radiologists in the Least Developed Countries (LDCs) emphasizes the need for automated pneumonia diagnostic systems. This article presents a Deep Learning model, Zero-Order Optimized Convolutional Neural Network (ZooCNN), a Zero-Order Optimization (Zoo)-based CNN model for classifying CXR images into three classes, Normal Lungs (NL), Bacterial Pneumonia (BP), and Viral Pneumonia (VP); this model utilizes the Adaptive Synthetic Sampling (ADASYN) approach to ensure class balance in the Kaggle CXR Images (Pneumonia) dataset.
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January 2025
Advanced Organ Bioengineering and Therapeutics, Faculty of Science and Technology, University of Twente, Zuidhorst 28, Drienerlolaan 5, 7522 NB Enschede, The Netherlands.
Hemodialysis (HD) is a critical treatment for patients with end-stage kidney disease (ESKD). The effectiveness of conventional dialyzers used there could be compromised during extended use due to limited blood compatibility of synthetic polymeric membranes and sub-optimal dialyzer design. In fact, blood flow in the hollow fiber (HF) membrane could trigger inflammatory responses and thrombus formation, leading to reduced filtration efficiency and limiting therapy duration, a consequence of flowing the patients' blood through the lumen of each fiber while the dialysate passes along the inter-fiber space (IOF, inside-out filtration).
View Article and Find Full Text PDFEntropy (Basel)
January 2025
National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, China.
Graph anomaly detection is crucial in many high-impact applications across diverse fields. In anomaly detection tasks, collecting plenty of annotated data tends to be costly and laborious. As a result, few-shot learning has been explored to address the issue by requiring only a few labeled samples to achieve good performance.
View Article and Find Full Text PDFFront Bioeng Biotechnol
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APESA Pôle valorisation, Montardon, France.
This study evaluated the growth performance of and microalgae cultivated in diluted liquid digestate supplemented with CO, comparing their efficiency to that of a conventional synthetic media. The presence of an initial concentration of ammonium of 125 mg N-NH .L combined with the continuous injection of 1% v/v CO enhanced the optimal growth responses and bioremediation potential for both strains in 200-mL cultures.
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