Publications by authors named "David Q Le"

Contrast-enhanced ultrasound, microvascular imaging, elastography, and fat quantification have varying degrees of utility, with some applications in the pediatric setting mirroring that in adults and having unique uses when applied to children in others. This review will present novel ultrasound technologies and the clinical context in which they are applied to the pediatric abdomen. New ultrasound technologies have a broad range of applications in clinical practice and represent a powerful diagnostic tool with the potential to replace other imaging modalities, such as magnetic resonance imaging and computed tomography, in specific cases.

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Article Synopsis
  • Doppler ultrasound (DU) is used to detect venous gas emboli (VGE) after diving, but existing automated methods for assessment have limitations due to small datasets and lack of objective evaluation.
  • The authors developed a method to create synthetic DU data based on real signals, allowing variability in bubbling to match established grading systems.
  • This work includes baseline recordings and code for synthetic data generation, aiming to advance research in VGE analysis and improve signal processing methodologies.
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Background: Doppler ultrasound is used currently in decompression research for the evaluation of venous gas emboli (VGE). Estimation of heart rate from post-dive Doppler ultrasound recordings can provide a tool for the evaluation of physiological changes from decompression stress, as well as aid in the development of automated VGE detection algorithms that relate VGE presence to cardiac activity.

Method: An algorithm based on short-term autocorrelation was developed in MATLAB to estimate the heart rate in post-dive precordial Doppler ultrasound.

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Objective: Doppler ultrasound (DU) is used to detect venous gas emboli (VGE) post dive as a marker of decompression stress for diving physiology research as well as new decompression procedure validation to minimize decompression sickness risk. In this article, we propose the first deep learning model for VGE grading in DU audio recordings.

Methods: A database of real-world data was assembled and labeled for the purpose of developing the algorithm, totaling 274 recordings comprising both subclavian and precordial measurements.

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Ultrasound monitoring, both in the form of Doppler and 2D echocardiography, has been used post-dive to detect decompression bubbles circulating in the bloodstream. With large variability in both bubble time course and loads, it has been hypothesised that shorter periods between imaging, or even continuous imaging, could provide more accurate post-dive assessments. However, while considering applications of ultrasound imaging post-decompression, it may also be prudent to consider the possibility of ultrasound-induced bioeffects.

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Microbubble contrast agents are commonly used for therapeutic and diagnostic imaging applications. Under certain conditions, these contrast agents can coalesce on ultrasound application and form larger bubbles than the initial population. The formation of large microbubbles potentially influences therapeutic outcomes and imaging quality.

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Venous gas emboli (VGE) are often quantified as a marker of decompression stress on echocardiograms. Bubble-counting has been proposed as an easy to learn method, but remains time-consuming, rendering large dataset analysis impractical. Computer automation of VGE counting following this method has therefore been suggested as a means to eliminate rater bias and save time.

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It is widely accepted that bubbles are a necessary but insufficient condition for the development of decompression sickness. However, open questions remain regarding the precise formation and behavior of these bubbles after an ambient pressure reduction (decompression), primarily due to the inherent difficulty of directly observing this phenomenon in vivo. In decompression research, information about these bubbles after a decompression is gathered via means of ultrasound acquisitions.

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