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.
Intramembrane proteolytic cleavage is an important process in a number of signaling pathways and pathologies. One of the best-known is that of Alzheimer’s Disease (AD), where the gamma-secretase enzyme cleaves amyloid precursor protein (APP) to create free amyloid. This free amyloid accumulates to form amyloid plaques during the later stages of the disease. New drugs are urgently needed to address AD and the disclosed compound represents such potential drug. This technology is a compound that can bind and covalently modify the transmembrane (TM) domain of the amyloid precursor protein.
Hybrid imaging combines different imaging modalities to obtain information from both systems, such as anatomy and physiology through MRI while leveraging tools available for X-ray fluoroscopy. Hybrid image systems could offer the benefits of increased diagnostic accuracy, faster examinations, and a better understanding of different medical professions. Current medical imaging research leverages hybrid imaging, which is of particular value to interventional applications as additional information is provided with MRI and soft-tissue contrast.
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
"Researchers at RPI developed an optical reservoir computer (ORC) with commercial off-the-shelf components to predict lung tumor motion during radiotherapy. The technology could improve radiation therapy outcomes and yield applications for other imaging modalities. The ORC shows comparable motion prediction accuracy and error rates to traditional neural networks (long short-term memory (LSTM), Multi-Layer Perceptron Neural Network (MLP-NN), and Adaptive Boosting and Multi-Layer Perceptron Neural Network (ABMLP-NN)).
Using raw materials (thermoplastic pellets and rolls of fiber tows), this invention will continuously impregnate fiber tows with molten thermoplastic resin for fabrication of custom composite shapes, unlike current methods, which do not use raw materials and are extremely expensive processes. The ‘In Situ’ process can be used to either directly “print” composite parts in an additive manufacturing approach or to manufacture pre-impregnated (prepreg) composite material for use in other manufacturing technologies.
Nonphotochemical quenching (NPQ) is a response mechanism in plants and algae that allows them to process and dissipate excess excitation energy as heat safely. Collecting fluorescence data from these plants and algae in surface water environments can incur errors from NPQ, ultimately leading to inaccurate calculations of chlorophyll concentration for environmental and industrial water quality monitoring. Rensselaer inventors developed a novel approach to correcting NPQ-skewed fluorescence data by employing trained machine-learning modules that can be applied to fluorescence detection system
Researcher Ge Wang and team created imaging systems and methods using excited nanoparticles coupled between CT and MRI to provide faster localization information for targeted, high resolution imaging. The study of biological systems is a complex pursuit that requires sufficient models and tools to measure responses to controlled changes in the system, however, there has been a lack of appropriate microscopy allowing insight into deep 3D models of molecular and cellular function due to the diffusive properties of optical light. Wang and his team overcame limitations in the field by using nan
Researchers at Rensselaer Polytechnic Institute (RPI) created a 3D computer simulation tool to assess the behavior/interaction of a hydrophobic membrane material with waste/feed water particles to assist membrane manufacturers/end-users in identifying a high performing membrane filtration/separation system. This simulation protocol could represent a viable, more cost-effective technique for membrane system designers within the wastewater treatment, desalination, food processing, pharmaceutical biotech, and oil/gas industries.