Algorithmic Venous Gas Emboli Detection and Diagnostics for Wearable Ultrasound

This research will develop venous gas emboli (VGE) detection algorithms and characterize the impact of signal degradation and motion artifacts on detector performance to establish the foundation for a wearable ultrasound extravehicular activity (EVA) VGE monitor. It is currently unknown how VGE form over time and relate to the onset of decompression sickness (DCS). Recent advances in ultrasound transducers could enable long-term, continuous monitoring, transforming our DCS understanding and providing a means to monitor astronaut VGE levels during EVA. This will be critical as future exploration missions emphasize increased EVA frequency, duration, and number of transitions between pressurized environments. The addressed research gaps will develop and validate sound-based and image-based ultrasound VGE detection algorithms and generate novel performance metrics for each, establish algorithmic means of synthesizing DCS data, characterize algorithm performance under degraded signal conditions, and assess how motion artifacts influence VGE detection abilities. The methods used to approach these objectives will include deep learning techniques to classify VGE severity, mathematical models to manually degrade images during post-processing, linear actuator systems to standardize probe movement, and a series of tests on simulated VGE within cardiovascular phantoms. The closure of these gaps will provide algorithmic methods for autonomously monitoring astronaut VGE via wearable ultrasound monitor. This would be a powerful complement to current DCS mitigation strategies by providing real-time diagnostics during EVA, informing increases in variable-suit operating pressure to alleviate symptoms. Beyond operations, VGE monitoring could more effectively inform pre-breathe exercise effects for decompression protocols. Autonomous detection of VGE levels in the circulatory system via wearable ultrasound would prove monumental in the foundational understanding of DCS and would serve as a direct strategy to predict and mitigate DCS during surface EVA, both top priorities of the Spacesuit Physiology capability area under the 2022 NASA Strategic Technology Framework's Advanced Habitation Systems designation.

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