Optoacoustics is similar in some respects to ultrasound imaging. In the latter, a probe sends ultrasonic waves into the body, which the tissue reflects. Sensors in the probe detect the returning sound waves and generate a picture of the inside of the body. Optoacoustic imaging instead sends very short laser pulses into the tissue, where they're absorbed and converted into ultrasonic waves. Similarly to ultrasound imaging, researchers can then detect the waves convert them into images.
While there is much hype around machine learning and its uses in healthcare, a recent survey indicates that machine learning is not just a buzzword, as 84 percent of medical imaging professionals view the technology as being either important or extremely important in medical imaging. What's more, about 20 percent of medical imaging professionals say they have already adopted machine learning, and about one-third say they will adopt it by 2020.
This post was authored by Elizabeth Fernandez, senior public information representative with UCSF News. "Intelligent Imaging" Hub will harness computational tools in medical imaging to improve patient care. UC San Francisco is launching a new center to accelerate the application of artificial intelligence (AI) technology to radiology, leveraging advanced computational techniques and industry collaborations to improve patient diagnoses and care. The Center for Intelligent Imaging, or ci2, will develop and apply AI to devise powerful new ways to look inside the body and to evaluate health and disease. Investigators in ci2 will team with Santa Clara, California-based NVIDIA Corp., an industry leader in AI computing, to build infrastructure and tools focused on enabling the translation of AI into clinical practice.
This is a fundamental question that drives many biological research programs. Imaging experiments have been trending toward higher-content approaches in order to delve deeper into the mechanism and increase data fidelity. In this digital supplement, we highlight several recent studies from researchers that not only use high-throughput methods but combine them with novel engineering techniques, whether at the specimen or platform level, to gain more from their imaging experiments. This special supplement brought to you by the Science/AAAS Custom Publishing Office.
Modern smartphones are essentially pocket-sized mini-computers, capable of dealing with many tasks that not very long ago would have been processed on desktop or laptop computers. The camera module is just one component of many, but more and more consumers are carefully considering camera performance in their buying decision. Manufacturers have been well aware for quite some time and are investing heavily to make sure the cameras on their devices can compete with the best. The device division of mobile communication pioneer Motorola for example, which was taken over by Chinese PC makers Lenovo in 2014, first assembled a dedicated camera and imaging team in 2013 when it was still part of Google. Since then the brand has launched a number of new devices in its Moto range with a clear focus on imaging performance and features.