Continuous Submicron Particle Separation Via Vortex-Enhanced Ionic Concentration Polarization: A Numerical Investigation

Separation and isolation of suspended submicron particles is fundamental to a wide range of applications, including desalination, chemical processing, and medical diagnostics. Ion concentration polarization (ICP), an electrokinetic phenomenon in micro-nano interfaces, has gained attention due to its unique ability to manipulate molecules or particles in suspension and solution. Less well understood, though, is the ability of this phenomenon to generate circulatory fluid flow, and how this enables and enhances continuous particle capture. Here, we perform a comprehensive study of a low-voltage ICP, demonstrating a new electrokinetic method for extracting submicron particles via flow-enhanced particle redirection. To do so, a 2D-FEM model solves the Poisson–Nernst–Planck equation coupled with the Navier–Stokes and continuity equations. Four distinct operational modes (Allowed, Blocked, Captured, and Dodged) were recognized as a function of the particle’s charges and sizes, resulting in the capture or release from ICP-induced vortices, with the critical particle dimensions determined by appropriately tuning inlet flow rates (200–800 [µm/s]) and applied voltages (0–2.5 [V]). It is found that vortices are generated above a non-dimensional ICP-induced velocity of U*=1, which represents an equilibrium between ICP velocity and lateral flow velocity. It was also found that in the case of multi-target separation, the surface charge of the particle, rather than a particle’s size, is the primary determinant of particle trajectory. These findings contribute to a better understanding of ICP-based particle separation and isolation, as well as laying the foundations for the rational design and optimization of ICP-based sorting systems

Radiomic Feature Robustness and Reproducibility in Quantitative Bone Radiography: A Study on Radiologic Parameter Changes

The purpose of this study was to investigate the robustness of different radiography radiomic features over different radiologic parameters including kV, mAs, filtration, tube angles, and source skin distance (SSD). A tibia bone phantom was prepared and all imaging studies was conducted on this phantom. Different radiologic parameters including kV, mAs, filtration, tube angles, and SSD were studied. A region of interest was drawn on the images and many features from different feature sets including histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet derived parameters were extracted. All radiomic features were categorized based on coefficient of variation (COV). Bland-Altman analysis also was used to evaluate the mean, standard deviation, and upper/lower reproducibility limits for radiomic features in response to variation in each testing parameters. Results on COV in all features showed that 22, 34, and 45 of features were most robust (COV � 5) against kV, mAs, and SSD respectively and there was no robust features against filtration and tube angle. Also, all features (100) and 76 of which showed large variations (COV > 20) against filtrations and tube angle respectively. Autoregressive model feature set has no robust features against all radiologic parameters. Features including sum-average, sum-entropy, correlation, mean, and percentile (50, 90, and 99) belong to co-occurrence matrix and histogram feature sets were found as most robust features. Bland-Altman analysis showed the high reproducibity of some feature sets against radiologic parameter changes. The results presented here indicated that radiologic parameters have great impacts on radiomic feature values and caution should be taken into account when work with these features. In quantitative bone studies, robust features with low COV can be selected for clinical or research applications. Reproducible features also can be obtained using Bland-Altman analysis. © 2018 The International Society for Clinical Densitometr

Radiomic Feature Robustness and Reproducibility in Quantitative Bone Radiography: A Study on Radiologic Parameter Changes

The purpose of this study was to investigate the robustness of different radiography radiomic features over different radiologic parameters including kV, mAs, filtration, tube angles, and source skin distance (SSD). A tibia bone phantom was prepared and all imaging studies was conducted on this phantom. Different radiologic parameters including kV, mAs, filtration, tube angles, and SSD were studied. A region of interest was drawn on the images and many features from different feature sets including histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet derived parameters were extracted. All radiomic features were categorized based on coefficient of variation (COV). Bland-Altman analysis also was used to evaluate the mean, standard deviation, and upper/lower reproducibility limits for radiomic features in response to variation in each testing parameters. Results on COV in all features showed that 22, 34, and 45 of features were most robust (COV � 5) against kV, mAs, and SSD respectively and there was no robust features against filtration and tube angle. Also, all features (100) and 76 of which showed large variations (COV > 20) against filtrations and tube angle respectively. Autoregressive model feature set has no robust features against all radiologic parameters. Features including sum-average, sum-entropy, correlation, mean, and percentile (50, 90, and 99) belong to co-occurrence matrix and histogram feature sets were found as most robust features. Bland-Altman analysis showed the high reproducibity of some feature sets against radiologic parameter changes. The results presented here indicated that radiologic parameters have great impacts on radiomic feature values and caution should be taken into account when work with these features. In quantitative bone studies, robust features with low COV can be selected for clinical or research applications. Reproducible features also can be obtained using Bland-Altman analysis. © 2018 The International Society for Clinical Densitometr