Detecting Oxygenator Thrombosis in ECMO: A Review of Current Techniques and an Exploration of Future Directions.

Extracorporeal membrane oxygenation (ECMO) is a life-support technique used to treat cardiac and pulmonary failure, including severe cases of COVID-19 (coronavirus disease 2019) involving acute respiratory distress syndrome. Blood clot formation in the circuit is one of the most common complications in ECMO, having potentially harmful and even fatal consequences. It is therefore essential to regularly monitor for clots within the circuit and take appropriate measures to prevent or treat them. A review of the various methods used by hospital units for detecting blood clots is presented. The benefits and limitations of each method are discussed, specifically concerning detecting blood clots in the oxygenator, as it is concluded that this is the most critical and challenging ECMO component to assess. We investigate the feasibility of solutions proposed in the surrounding literature and explore two areas that hold promise for future research: the analysis of small-scale pressure fluctuations in the circuit, and real-time imaging of the oxygenator. It is concluded that the current methods of detecting blood clots cannot reliably predict clot volume, and their inability to predict clot location puts patients at risk of thromboembolism. It is posited that a more in-depth analysis of pressure readings using machine learning could better provide this information, and that purpose-built imaging could allow for accurate, real-time clotting analysis in ECMO components.

Characterization of Darai Limestone Composition and Porosity Using Data-Constrained Modeling and Comparison with Xenon K-Edge Subtraction Imaging.

Data-constrained modeling is a method that enables three-dimensional distribution of mineral phases and porosity in a sample to be modeled based on micro-computed tomography scans acquired at different X-ray energies. Here we describe an alternative method for measuring porosity, synchrotron K-edge subtraction using xenon gas as a contrast agent. Results from both methods applied to the same Darai limestone sample are compared. Reasonable agreement between the two methods and with other porosity measurements is obtained. The possibility of a combination of data-constrained modeling and K-edge subtraction methods for more accurate sample characterization is discussed.

Data-constrained microstructure characterization with multispectrum X-ray micro-CT.

Conventional X-ray microcomputed tomography (micro-CT) is not usually sufficient to determine microscopic compositional distributions as it is limited to measuring the X-ray attenuation of the sample, which for a given dataset can be similar for materials of different composition. In contrast, the present work enables three-dimensional compositional analysis with a data-constrained microstructure (DCM) modeling methodology, which uses two or more CT datasets acquired with different X-ray spectra and incorporates them as model constraints. For providing input data for DCM, we have also developed a method of micro-CT data collection that enables two datasets with different X-ray spectra to be acquired in parallel. Such data are used together with the DCM methodology to predict the distributions of corrosion inhibitor and filler in a polymer matrix. The DCM-predicted compositional microstructures have a reasonable agreement with energy dispersive X-ray images taken on the sample surface.