Photoacoustic flow velocity imaging based on complex field decorrelation

Photoacoustic (PA) imaging can be used to monitor flowing blood inside the microvascular and capillary bed. Ultrasound speckle decorrelation based velocimetry imaging was previously shown to accurately estimate blood flow velocity in mouse brain (micro-)vasculature. Translating this method to photoacoustic imaging will allow simultaneous imaging of flow velocity and extracting functional parameters like blood oxygenation. In this study, we use a pulsed laser diode and a quantitative method based on normalized first order field autocorrelation function of PA field fluctuations to estimate flow velocities in an ink tube phantom and in the microvasculature of the chorioallantoic membrane of a chicken embryo. We demonstrate how the decorrelation time of signals acquired over frames are related to the flow speed and show that the PA flow analysis based on this approach is an angle independent flow velocity imaging method.

Photographic LVAD Driveline Wound Infection Recognition Using Deep Learning

The steady increase in the number of patients equipped with mechanical heart support implants, such as left ventricular assist devices (LVAD), along with virtually ubiquitous 24/7 internet connectivity coverage is motive to investigate and develop remote patient monitoring. In this study we explore machine learning approaches to infection severity recognition on driveline exit site images. We apply a U-net convolutional neural network (CNN) for driveline tube segmentation, resulting in a Dice score coefficient of 0.95. A classification CNN is trained to predict the membership of one out of three infection classes in photographs. The resulting accuracy of 67% in total is close to the measured expert level performance, which indicates that also for human experts there may not be enough information present in the photographs for accurate assessment. We suggest the inclusion of thermographic image data in order to better resolve mild and severe infections.

Compressive 3D ultrasound imaging using a single sensor

Three-dimensional ultrasound is a powerful imaging technique, but it requires thousands of sensors and complex hardware. Very recently, the discovery of compressive sensing has shown that the signal structure can be exploited to reduce the burden posed by traditional sensing requirements. In this spirit, we have designed a simple ultrasound imaging device that can perform three-dimensional imaging using just a single ultrasound sensor. Our device makes a compressed measurement of the spatial ultrasound field using a plastic aperture mask placed in front of the ultrasound sensor. The aperture mask ensures that every pixel in the image is uniquely identifiable in the compressed measurement. We demonstrate that this device can successfully image two structured objects placed in water. The need for just one sensor instead of thousands paves the way for cheaper, faster, simpler, and smaller sensing devices and possible new clinical applications.