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Virtual network sampling method using LinkedIn.

ABSTRACT: Surveying people via the Internet presents both opportunities and challenges. The opportunities include quick access to respondents all over the world enabling fast realization of surveys at low costs. The challenges include a lack of control over both the sampling process and research implementation, as well as low response rates. This article describes a method of obtaining an appropriate sample of people for survey research utilising the power of the Internet, while simultaneously allowing the researcher to minimise risk thanks to enhanced control over the sampling process and the implementation of the survey. The proposed method could be used mainly in economic and social surveys; it allows researchers to reach selected groups of respondents and to conduct surveys on a global scale.•The proposed sampling method uses LinkedIn's network structure to quickly reach a dispersed population•Creating a list of units belonging to the study population resembles the snowball method, though the units are selected for the sample by the researcher and not indicated by the respondents.

SUBMITTER: Kozlowski A

PROVIDER: S-EPMC8374516 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Virtual network sampling method using LinkedIn.

Kozłowski Arkadiusz A Kaliszewski Adam A Dąbrowski Janusz J Klimek Hanna H

MethodsX 20210521

Surveying people via the Internet presents both opportunities and challenges. The opportunities include quick access to respondents all over the world enabling fast realization of surveys at low costs. The challenges include a lack of control over both the sampling process and research implementation, as well as low response rates. This article describes a method of obtaining an appropriate sample of people for survey research utilising the power of the Internet, while simultaneously allowing th ...[more]

PMID: 34430289

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Project description:PurposeRecently, deep learning-based methods have been established to denoise the low-count positron emission tomography (PET) images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan time, and improve image quality for equivalent lesion detectability and clinical diagnosis. In clinical settings, the majority scans are still acquired using standard injection dose with standard scan time. In this work, we applied a 3D U-Net network to reduce the noise of standard-count PET images to obtain the virtual-high-count (VHC) PET images for identifying the potential benefits of the obtained VHC PET images.MethodsThe training datasets, including down-sampled standard-count PET images as the network input and high-count images as the desired network output, were derived from 27 whole-body PET datasets, which were acquired using 90-min dynamic scan. The down-sampled standard-count PET images were rebinned with matched noise level of 195 clinical static PET datasets, by matching the normalized standard derivation (NSTD) inside 3D liver region of interests (ROIs). Cross-validation was performed on 27 PET datasets. Normalized mean square error (NMSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and standard uptake value (SUV) bias of lesions were used for evaluation on standard-count and VHC PET images, with real-high-count PET image of 90 min as the gold standard. In addition, the network trained with 27 dynamic PET datasets was applied to 195 clinical static datasets to obtain VHC PET images. The NSTD and mean/max SUV of hypermetabolic lesions in standard-count and VHC PET images were evaluated. Three experienced nuclear medicine physicians evaluated the overall image quality of randomly selected 50 out of 195 patients' standard-count and VHC images and conducted 5-score ranking. A Wilcoxon signed-rank test was used to compare differences in the grading of standard-count and VHC images.ResultsThe cross-validation results showed that VHC PET images had improved quantitative metrics scores than the standard-count PET images. The mean/max SUVs of 35 lesions in the standard-count and true-high-count PET images did not show significantly statistical difference. Similarly, the mean/max SUVs of VHC and true-high-count PET images did not show significantly statistical difference. For the 195 clinical data, the VHC PET images had a significantly lower NSTD than the standard-count images. The mean/max SUVs of 215 hypermetabolic lesions in the VHC and standard-count images showed no statistically significant difference. In the image quality evaluation by three experienced nuclear medicine physicians, standard-count images and VHC images received scores with mean and standard deviation of 3.34±0.80 and 4.26 ± 0.72 from Physician 1, 3.02 ± 0.87 and 3.96 ± 0.73 from Physician 2, and 3.74 ± 1.10 and 4.58 ± 0.57 from Physician 3, respectively. The VHC images were consistently ranked higher than the standard-count images. The Wilcoxon signed-rank test also indicated that the image quality evaluation between standard-count and VHC images had significant difference.ConclusionsA DL method was proposed to convert the standard-count images to the VHC images. The VHC images had reduced noise level. No significant difference in mean/max SUV to the standard-count images was observed. VHC images improved image quality for better lesion detectability and clinical diagnosis.

Dataset Information

Virtual network sampling method using LinkedIn.

Publications

Virtual network sampling method using LinkedIn.

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