Application of validated ultrasound indices to investigate erythrocyte aggregation in pigs. Preliminary in vivo results

Ultrasound simulation of blood with different red blood cell aggregations and concentrations

BACKGROUND

Considerable progress of ultrasound simulation on blood has enhanced the characterizing of red blood cell (RBC) aggregation.

OBJECTIVE

A novel simulation method aims at modeling the blood with different RBC aggregations and concentrations is proposed.

METHODS

The modeling process is as follows: (i) A three-dimensional scatterer model is first built by a mapping with a Hilbert space-filling curve from the one-dimensional scatterer distribution. (ii) To illustrate the relationship between the model parameters and the RBC aggregation level, a variety of blood samples are prepared and scanned to acquire their radiofrequency signals in-vitro. (iii) The model parameters are determined by matching the Nakagami-distribution characteristics of envelope signals simulated from the model with those measured from the blood samples.

RESULTS

Nakagami metrics m estimated from 15 kinds of blood samples (hematocrits of 20%, 40%, 60% and plasma concentrations of 15%, 30%, 45%, 60%, 75%) are compared with metrics estimated by their corresponding models (each with different eligible parameters). Results show that for the three hematocrit levels, the mean and standard deviation of the root-mean-squared deviations of m are 0.27 ± 0.0026, 0.16 ± 0.0021, 0.12 ± 0.0018 respectively.

CONCLUSION

The proposed simulation model provides a viable data source to evaluate the performance of the ultrasound-based methods for quantifying RBC aggregation.

Classification of red blood cell aggregation using empirical wavelet transform analysis of ultrasonic radiofrequency echo signals

Grading red blood cell (RBC) aggregation is important for the early diagnosis and prevention of related diseases such as ischemic cardio-cerebrovascular disease, type II diabetes, deep vein thrombosis, and sickle cell disease. In this study, a machine learning technique based on an adaptive analysis of ultrasonic radiofrequency (RF) echo signals in blood is proposed, and its feasibility for classifying RBC aggregation is explored. Using an adaptive empirical wavelet transform (EWT) analysis, the ultrasonic RF signals are decomposed into a series of empirical mode functions (EMFs); then, dominant empirical mode functions (DEMFs) are selected from the series. Six statistical characteristics, including the mean, variance, median, kurtosis, root mean square (RMS), and skewness are calculated for the locally normalized DEMFs, aiming to form primary feature vectors. Random forest (RDF) and support vector machine (SVM) classifiers are trained with the given feature vectors to obtain prediction models for RBC classification. Ultrasonic RF echo signals are acquired from five groups of six types of porcine blood samples with average numbers of aggregated RBCs of 1.04, 1.20, 1.83, 2.31, 2.72, and 4.28, respectively, to test the classification performance of the proposed method. The best subset with regard to the variance, kurtosis, and RMS is determined according to the maximum accuracy based on the RDF and SVM classifiers. The classification accuracies are 84.03 ± 3.13% for the RDF classifier, and 85.88 ± 2.99% for the SVM classifier. The mean classification accuracy of the SVM classifier is 1.85% better than that of the RDF classifier. In conclusion, the machine learning method is useful for the discrimination of varying degrees of RBC aggregation, and has potential for use in characterizing and monitoring the RBC aggregation in vessels.

Quantitative Measurement of Erythrocyte Aggregation as a Systemic Inflammatory Marker by Ultrasound Imaging: A Systematic Review

This systematic review is aimed at answering two questions: (i) Is erythrocyte aggregation a useful biomarker in assessing systemic inflammation? (ii) Does quantitative ultrasound imaging provide the non-invasive option to measure erythrocyte aggregation in real time? The search was executed through bibliographic electronic databases CINAHL, EMB Review, EMBASE, MEDLINE, PubMed and the grey literature. The majority of studies correlated elevated erythrocyte aggregation with inflammatory blood markers for several pathologic states. Some studies used "erythrocyte aggregation" as an established marker of systemic inflammation. There were limited but promising articles regarding the use of quantitative ultrasound spectroscopy to monitor erythrocyte aggregation. Similarly, there were limited studies that used other ultrasound techniques to measure systemic inflammation. The quantitative measurement of erythrocyte aggregation has the potential to be a routine clinical marker of inflammation as it can reflect the cumulative inflammatory dynamics in vivo, is relatively simple to measure, is cost-effective and has a rapid turnaround time. Technologies like quantitative ultrasound spectroscopy that can measure erythrocyte aggregation non-invasively and in real time may offer the advantage of continuous monitoring of the inflammation state and, thus, may help in rapid decision making in a critical care setup.

Protocol for Robust In Vivo Measurements of Erythrocyte Aggregation Using Ultrasound Spectroscopy

Erythrocyte aggregation is a non-specific marker of acute and chronic inflammation. Although it is usual to evaluate this phenomenon from blood samples analyzed in laboratory instruments, in vivo real-time assessment of aggregation is possible with spectral ultrasound techniques. However, variable blood flow can affect the interpretation of acoustic measures. Therefore, flow standardization is required. Two techniques of flow standardization were evaluated with porcine and equine blood samples in Couette flow. These techniques consisted in either stopping the flow or reducing it. Then, the sensibility and repeatability of the retained method were evaluated in 11 human volunteers. We observed that stopping the flow compromised interpretation and repeatability. Conversely, maintaining a low flow provided repeatable measures and could distinguish between normal and high extents of erythrocyte aggregation. Agreement was observed between in vivo and ex vivo measures of the phenomenon (R² = 82.7%, p value < 0.0001). These results support the feasibility of assessing in vivo erythrocyte aggregation in humans by quantitative ultrasound means.