RPI presents a novel, DCL-Net system, and method based on a machine learning/DL technique to reconstruct 3D volumes from a series of 2D images. The developed technology does not require the use of sensor tracking hardware/devices (e.g., robotic arms, touchless position/pose trackers) to operate. The technology acquires multiple (two or more) consecutive, 2D US-generated image frames using a handheld imaging sensor or a sensor mounted on a motion restricted device used as inputs for estimating a US probe trajectory and tracking its motion.

Strokes are one of the primary sources of long-term disability with billions in annual direct and indirect costs to the United States healthcare system. Nearly one-third of all strokes occur in patients with clogged carotid arteries. Carotid artery imaging types include digital subtraction angiography (DSA), duplex ultrasonography (DUS), CT angiography, and MR angiography. These imaging techniques provide information on the carotid artery’s shape, localized blood flow, and plaque composition.

Researchers at Rensselaer Polytechnic Institute are developing a non-invasive and user-friendly wearable

device for monitoring blood pressure, blood glucose, and biomarkers, which could improve quality of life, 

decrease healthcare expenditure, and allow for early intervention for potentially serious diseases. 

Currently, a major area of interest within the medical wearable device industry is the real-time monitoring

of blood pressure. More than 100 million adults in the United States and a third of the worldwide population