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This technology includes a new technique to improve the fidelity of time-varying signals acquired in the dynamic contrast enhanced (DCE) imaging. This technique enhances the time-varying signals in a given DCE image series through deep convolutional neural networks (CNN) to learn the relationship of signal versus contrast concentration from other series of different contrast doses.

This technology includes a technique to improves detection of myocardial scar compared with conventional bright-blood late gadolinium enhancement (LGE) techniques. Dark-blood late gadolinium enhancement (DB-LGE) improves tissue delineation with signal suppression of the blood pool based on T2-preparation pulse that is relatively independent from the blood flow velocities and improves scar detection in patients with known or suspected coronary artery disease.

This CDC developed auscultatory training apparatus includes a database of prerecorded physiological sounds (e.g., lung, bowel, or heart sounds) stored on a computer for playback. Current teaching tools, which utilize previously recorded sounds, suffer from the disadvantage that playback environments cause considerable distortion and errors in sound reproduction. For example, to those trainees using such systems, the reproduced respiratory sounds do not “sound” as if they are being generated by a live patient.

This invention pertains to a CDC developed sonic aerosol generator that provides a controllable, stable concentration of particulate aerosol over a long period of time for aerosol exposure studies. Specifically, in situ testing data indicate uniform aerosol stability can be maintainable for greater than 30 hours at concentrations of 15 mg/m³ or more. Additionally, the technology was specifically developed for, and validated in, animal studies assessing exposure to airborne multi-walled carbon nanotubes (MWCNT).

This CDC-developed technology describes an automated system for monitoring worker hazard exposures by recording data about where and when hazards occur in a workplace or other environment. This allows the hazards to be avoided and harmful exposures and risks reduced. This field-tested technology consists of an integrated, hand-held electronics instrument and software system that will precisely correlate multiple exposure levels with position coordinates of the user and features real-time data acquisition.

This CDC-developed technology is an aerosol preconcentration unit (APU) designed for use with spectroscopic detection techniques, including emission, Raman, or infrared spectroscopies. Most existing pulsed microplasma techniques, such as laser-induced breakdown, for aerosols rely mainly on filter-based collection and suffer from poor accuracy, precision, and detection limits and require long sample collection times.

CDC researchers have developed a novel acoustic whole body plethysmograph (AWBP) that allows measurement of tidal volume in lab animals, independent of gas compression in the lung. This system provides particular advantages over the traditional whole body plethysmograph (WBP) when measuring model animals with increased gas compression due to increased airway resistance or increased acceleration in the breathing pattern.

This CDC-developed technology entails a system for monitoring and assessing the risk of auditory damage from exposure to impulse noise, such as noise created by construction machinery and firearms. Noise dosimeters have been used extensively over the past two decades to document personal exposure to noise and assure workplaces comply with permissible noise exposure levels. However, due to older methods of calculating "noise dose," current noise dosimeters often inaccurately determine the risk of an impulse event.

CDC researchers have developed the Portable Exposure Assessment System (PEAS), a field-based, remotely deployed tool to monitor and provide early warning of working conditions that have a high likelihood of musculoskeletal injury. PEAS is a noninvasive, real-time, instrument-based system. Sensor technology simultaneously measures and collects data regarding the body loads and awkward postures imposed by package handling as well as driving-related, low-frequency vibrations.

Summary:

The National Eye Institute (NEI) seeks research co-development partners and/or licensees for an automatic deep learning-based algorithm to detect and quantitate ellipsoid zone (EZ) loss in Spectral Domain Optical Coherence Tomography (SD-OCT) images.