Development and Characterization of Hemoglobin Microbubbles for Acoustic Blood Oxygen Level Dependent Imaging.

Oxygen levels in tissues and organs are crucial for their normal functioning, and approaches to monitor them non-invasively have wide biological and clinical applications. In this study, we developed a method of acoustically detecting oxygenation using contrast-enhanced ultrasound (CEUS) imaging. Our approach involved the use of specially designed hemoglobin-based microbubbles (HbMBs) that reversibly bind to oxygen and alter the state-dependent acoustic response.

Assessment of Transarterial Chemoembolization Using Super-resolution Ultrasound Imaging and a Rat Model of Hepatocellular Carcinoma.

Objective: Hepatocellular carcinoma (HCC) is a highly prevalent form of liver cancer diagnosed annually in 600,000 people worldwide. A common treatment is transarterial chemoembolization (TACE), which interrupts the blood supply of oxygen and nutrients to the tumor mass. The need for repeat TACE treatments may be assessed in the weeks after therapy with contrast-enhanced ultrasound (CEUS) imaging.

Evaluation of Nonlinear Contrast Pulse Sequencing for Use in Super-Resolution Ultrasound Imaging.

The use of super-resolution ultrasound (SR-US) imaging greatly improves visualization of microvascular structures, but clinical adoption is limited by long imaging times. This method depends on detecting and localizing isolated microbubbles (MBs), forcing the use of a dilute contrast agent concentration. Contrast-enhanced ultrasound (CEUS) image acquisition times as long as minutes arise as the localization of thousands of MBs are acquired to form a complete SR-US image.

Deep Learning of Spatiotemporal Filtering for Fast Super-Resolution Ultrasound Imaging.

Super-resolution ultrasound (SR-US) imaging is a new technique that breaks the diffraction limit and allows visualization of microvascular structures down to tens of micrometers. The image processing methods for the spatiotemporal filtering needed in SR-US, such as singular value decomposition (SVD), are computationally burdensome and performed offline. Deep learning has been applied to many biomedical imaging problems, and trained neural networks have been shown to process an image in milliseconds.

Introducing handheld computers into home care.

In the fall of 2009, Bayshore Home Health (BHH) provided tablet computers to 75 home care nurses working in Barrie, Ont. The devices were equipped with an embedded evidence-based documentation system and loaded with decision-making supports such as drug reference databases. The technology was designed to facilitate client assessment, care planning and evaluation at the point of care.