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Background Computed Tomography (CT) ventilation imaging (CTVI) is an emerging ventilation imaging technique. CTVI implementations have been widely validated against alternative ventilation imaging techniques but have been limited to clinical research only. The first CTVI commercial product, CT LVAS (4DMedical, Melbourne, Australia), was recently released enabling its use in clinical practice. This study quantitatively compares ventilation images from CT LVAS and previously validated research CTVI algorithms to Galligas PET ventilation. Methods 16 patients with Galligas PET and paired inhale/exhale breath-hold CT images were taken from a publicly available dataset on The Cancer Imaging Archive. Ventilation images were produced using CT LVAS and two previously published algorithms: (1) utilising the Hounsfield Unit difference (CTVI_HU); and (2) utilising the Jacobian determinant (CTVI_Jac). CTVI images were compared to the reference standard Galligas PET using Bland-Altman analysis of lobar ventilation, voxel-wise Spearman correlation, and Dice similarity coefficient (DSC) of regions of interest representing the top 85% and 15% of ventilation function. Results Bland-Altman analysis showed overall bias of < 0.01% for all CTVI methods (95% confidence interval: ±7.4% for CT LVAS, ± 9.1% for CTVI_HU, ± 7.9% for CTVI_Jac). The mean Spearman correlation between CTVI and Galligas PET was 0.61 ± 0.14 ( p  < 0.01) for CT LVAS, 0.68 ± 0.10 ( p  < 0.01) for CTVI_HU, and 0.57 ± 0.15 ( p  < 0.01) for CTVI_Jac. The mean DSC for the top 85% was 0.91 ± 0.03 for CT LVAS, 0.92 ± 0.02 for CTVI_HU, and 0.91 ± 0.03 for CTVI_Jac, with the DSC for CTVI_HU significantly higher than the other two CTVI methods. The DSC for the top 15% was 0.47 ± 0.17 for CT LVAS, 0.53 ± 0.16 for CTVI_HU, and 0.47 ± 0.18 for CTVI_Jac. Conclusions In a comparison to Galligas PET ventilation imaging, CT LVAS performs similarly to previous CTVI methods. Bland-Altman analysis for quantification of lobar ventilation demonstrates negligible bias. Mean voxel-wise Spearman correlations are moderate to good. DSC of functionally thresholded lung regions are similar for all CTVI methods. These results warrant further investigation of CT LVAS as a readily available ventilation imaging tool in disease characterisation, lung health assessment, and surgical and targeted treatment planning. Trial registration Australian New Zealand Clinical Trials Registry (ANZCTR) registration number ACTRN12612000775819, registered on 23/07/2012.

Background Small airways disease is a feature of many respiratory conditions. Currently available methods of diagnosing small airways lack sensitivity and/or cannot evaluate spatial heterogeneity. New diagnostic strategies for diagnosing small airways disease are needed. Methods We determined the regional displacement of lung tissue calculated from fluoroscopic lung images acquired at multiple angles over sequential time points as a surrogate of ventilation. We applied this technique, which we call X-ray velocimetry (XV), to patients with chronic obstructive pulmonary disease (COPD) and impaired spirometry and veterans with deployment-related constrictive bronchiolitis (DR-CB) but preserved spirometry to determine XV-derived biomarkers specific for each condition. Results We identified disease- and stage-specific XV biomarkers for COPD patients that correlated with airflow obstruction on spirometry. Further, we identified a set of XV-derived biomarkers that could distinguish veterans with DR-CB from controls despite normal spirometry in most patients from both groups. Conclusions XV may provide a safe and widely-available strategy for diagnosing small airways disease while preserving spatial information. Future studies are required to validate the biomarkers described here in larger patient cohorts. Trial registration Not required for this study. However, participants enrolled at VUMC were enrolled under ClinicalTrials.gov study NCT04489758 (submitted 07/23/2020).

Microscale blood flow is characterised by heterogeneous distributions of hematocrit, viscosity and velocity. In microvascular bifurcations, cells are unevenly distributed between the branches, and this effect can be amplified in subsequent branches depending on a number of parameters. We propose an approach to infer hematocrit profiles of human blood flowing through a bifurcating microchannel. The influence of aggregation, induced by the addition of Dextran 2000 to the samples, is also considered. Averaged values indicate plasma skimming, particularly in the presence of red blood cell (RBC) aggregation. Using an empirical model, the hematocrit profiles are used to estimate local relative viscosity distributions. Simulations are used to predict how the non-uniform viscosity influences the velocity profiles. Comparing these data to velocity profiles of RBCs measured using particle image velocimetry provides validation of the model. It is observed that aggregation blunts velocity profiles after a long straight section of channel. Downstream of the bifurcation, skewing of the velocity profiles is detected, which is enhanced by aggregation. The proposed methodology is capable of providing hitherto unreported information on important aspects of microscale blood rheology.

Simultaneous measurement of erythrocyte (RBC) velocity fields and aggregation properties has been successfully performed using an optical shearing microscope and Particle Image Velocimetry (PIV). Blood at 45% haematocrit was sheared at rates of 5.4⩽γ˙⩽252s-1 and imaged using a high speed camera. The images were then processed to yield aggregation indices and flow velocities. Negligible levels of aggregation were observed for γ˙⩾54.0s-1, while high levels of aggregation and network formation occurred for γ˙⩽11.7s-1. The results illustrate that the velocity measurements are dependent on the extent of RBC aggregation. High levels of network formation cause the velocities at γ˙⩾5.4s-1 to deviate markedly from the expected solid body rotation profile. The effect of aggregation level on the PIV accuracy was assessed by monitoring the two-dimensional (2D) correlation coefficients. Lower levels of aggregation result in poorer image correlation, from which it can be inferred that PIV accuracy is reduced. Moreover, aggregation is time-dependent, and consequently PIV accuracy may decrease during recording as the cells break up. It is therefore recommended that aggregation and its effects are taken into account in future when undertaking blood flow studies using PIV. The simplicity of the technique, which requires no lasers, filters, or special pretreatments, demonstrates the potential wide-spread applicability of the data acquisition system for accurate blood flow PIV and aggregation measurement.