Intra-System Reliability Assessment of 2-Dimensional Shear Wave Elastography

The Relationship between Placental Shear Wave Elastography and Fetal Weight-A Prospective Study

Background/Objectives: The utility of shear wave elastography (SWE) as an adjunct to ultrasound biometry and Doppler velocimetry for the examination of placental dysfunction and suboptimal fetal growth is unclear. To date, limited data exist correlating the mechanical properties of placentae with fetal growth. This study aimed to investigate the relationship between placental shear wave velocity (SWV) and ultrasound estimated fetal weight (EFW), and to ascertain if placental SWV is a suitable proxy measure of placental function in the surveillance of small-for-gestational-age (SGA) pregnancies. Methods: This prospective, observational cohort study compared the difference in placental SWV between SGA and appropriate-for-gestational-age (AGA) pregnancies. There were 221 women with singleton pregnancies in the study cohort-136 (61.5%) AGA and 85 (38.5%) SGA. Fetal biometry, Doppler velocimetry, the deepest vertical pocket of amniotic fluid, and mean SWV were measured at 2-4-weekly intervals from recruitment to birth. Results: There was no difference in mean placental SWV in SGA pregnancies compared to AGA pregnancies, nor was there any relationship to EFW. Conclusions: Although other studies have shown some correlation between increased placental stiffness and SGA pregnancies, our investigation did not support this. The mechanical properties of placental tissue in SGA pregnancies do not result in placental SWVs that are apparently different from those of AGA controls. As this study did not differentiate between constitutionally or pathologically small fetuses, further studies in growth-restricted cohorts would be of benefit.

Shear wave velocity measurement of the placenta is not limited by placental location

INTRODUCTION

Ultrasound elastography shows diagnostic promise via the non-invasive determination of placental elastic properties. A limitation is a potential for inadequate measurements from posterior placentae. This study aimed to analyse placental position's influence on measures of shear wave elastography (SWV).

METHODS

SWV elastography measurements were obtained via ultrasound at 24, 28 and 36 weeks gestation from 238 pregnancies. . The placental position was labelled as either anterior, posterior or fundal/lateral. Average SWV measurements (m/s) and the corresponding standard deviations (SD) were used for data analysis.

RESULTS

There was a statistically significant difference between SWV recorded from anterior (1.33 ± 0.19)m/s and posterior (1.39 ± 0.18)m/s placentae (p < 0.001). However, the average sampling depth between these groups was significantly different (3.98 cm vs. 5.38 cm, p < 0.001). There was no statistically significant difference between SWV when measurements were compared at similar depths, regardless of placental location. The addition of placental position to a previously developed mixed-effects model confirmed placental position did not result in improved SWV measurements. In this model, sampling depth remained the best predictor for SWV.

CONCLUSIONS

This study showed that placental position does not influence the accuracy or reliability of SWV.

Variability of Biceps Muscle Stiffness Measured Using Shear Wave Elastography at Different Anatomical Locations With Different Ultrasound Machines

Shear wave elastography is an emerging diagnostic tool used to assess for changes in the stiffness of muscle. Each region of the muscle may have a different stiffness; therefore, the anatomical region should be carefully selected. Machine vendors each have unique methods for calculating the returned stiffness values and, consequently, a high level of agreement in measurement between machines (quantified using the intraclass correlation coefficient [ICC] and Bland-Altman analysis) will allow research findings to be translated to the clinic. This study assessed three locations within the biceps muscle (50% and 75% of the distance between the acromioclavicular joint and antecubital fossa, and superior to distal myotendinous junction [MTJ]) of 32 healthy volunteers with two different machines, the Canon Aplio i600 and SuperSonic Imagine Aixplorer (SSI), to compare the reported shear wave velocities and the variability by coefficient of variation (CV) and ICC. There was no difference in the CV between machines, but a significant difference in the CV at muscle regions, with the 75% location having a 40.2% reduction in CV. The 75% location had the highest ICC values with good posterior mean ICCs of 0.84 on the Canon and 0.83 on the SSI. The 50% and MTJ locations had poor ICC values. The 75% location provided the lowest CV and highest ICC and should be used for future stiffness assessments.

The application of artificial intelligence in the sonography profession: Professional and educational considerations

The integration of artificial intelligence (AI) technology within the health industry is increasing. This educational piece discusses the implementation of AI and its impact on sonography. The authors investigate how AI may influence the profession and provide examples of how ultrasound imaging may be enhanced and innovated by integrating AI technology. This article highlights challenges related to the application of AI and provides insight into how they could be addressed. The critical distinction between the role of a sonographer and the reporting specialist in the context of AI is highlighted as a key issue for those developing, researching, and evaluating AI systems. A key recommendation is for the sonography community to address ultrasound education, particularly how AI knowledge could be incorporated into university education. This is an important consideration that should be extended to practising professionals as they may be involved in evaluating the efficiency and methodologies used in new research that may incorporate AI technologies.

Shear Wave Elastography Implementation on a Portable Research Ultrasound System: Initial Results

Ultrasound shear wave elastography (SWE) has emerged as a promising technique that enables the quantitative estimation of soft tissue stiffness. However, its practical implementation is complicated and presents a number of engineering challenges, including high-energy burst transmission, high-frame rate data acquisition and high computational requirements to process huge datasets. Therefore, to date, SWE has only been available for high-end commercial systems or bulk and expensive research platforms. In this work, we present a low-cost, portable and fully configurable 256-channel research system that is able to implement various SWE techniques. We evaluated its transmit capabilities using various push beam patterns and developed algorithms for the reconstruction of tissue stiffness maps. Three different push beam generation methods were evaluated in both homogeneous and heterogeneous experiments using an industry-standard elastography phantom. The results showed that it is possible to implement the SWE modality using a portable and cost-optimized system without significant image quality losses.

Changes in placental elastography in the third trimester - Analysis using a linear mixed effect model

INTRODUCTION

Research into the role of ultrasound elastography to assess compromised placental tissue is ongoing. There is particular interest in evaluating its potential in the investigation of changes associated with uteroplacental dysfunction. To date, there is limited data on how different maternal and fetal considerations, such as advancing gestational age, amniotic fluid Index (AFI) and maternal body mass index (BMI) may influence shear wave velocity (SWV) measurements. This study aimed to evaluate longitudinal changes in SWV throughout gestation and model these changes with other developing fetal and maternal physiological and biological characteristics.

METHODS

The study utilised 238 singleton pregnancies and collected longitudinal data at repeated intervals in the 3rd trimester representing 629 individual data points. Linear mixed model regression analysis was used to identify significant predictors for SWV.

RESULTS

From a total of ten variables selected for modelling, only gestational age, AFI, BMI, and sample depth were found to be significant predictors of placental SWV, and gestational age and AFI were found to have only a minimal impact on SWV.

DISCUSSION

Sophisticated statistical modelling demonstrates that many of the expected maternal and fetal changes in the 3rd trimester have no or minimal impact on placental SWV. Understanding which factors influence placental SWV is essential to ascertain the technique's utility in managing pregnancies complicated by placental dysfunction in the future.