The Chandra X-Ray Observatory has gathered over 1.3 million detections in 27 years. Breakthroughs, discoveries, and DIY tips sent six days a week. NASA's Chandra X-Ray Observatory is considered one of the agency's greatest achievements, but it's not necessarily as recognizable as siblings like the James Webb and Hubble Space Telescopes . However, since 1999, the powerful spacecraft has peered deep into the cosmos to provide astronomers with never-before-seen glimpses of the Milky Way galaxy . As the observatory nears its 27th anniversary, NASA is highlighting its Chandra Source Catalog (CSC), an absolutely massive archive of visualization data collected over the years.

Lately, artificial intelligence and machine learning are being intensively adopted by the healthcare industry. In fact, since the beginning of the pandemic, the medical imaging space has been leveraging technologies for the rapid detection of COVID-19 among patients. Even before the unprecedented times set in, algorithms had been developed to detect chest-related conditions, including tuberculosis and lung cancer. However, the capabilities of these technologies and algorithms were all limited to general clinical settings -- at times where there could be chances of multiple abnormalities. For instance, a system meant to detect pneumothorax may not be expected to highlight nodules suggestive of cancer.

Researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory have developed a new AI-based framework that can produce X-ray images in 3D. The team, which includes members from three divisions at Argonne, has developed a method to create 3D visualizations from X-ray data. Their efforts were meant to allow them to better use the Advanced Photon Source (APS) at their lab, but potential applications of this technology range from astronomy to electron microscopy. Lab tests showed that the new approach, called 3D-CDI-NN, can create 3D visualizations from data hundreds of times faster than existing technology. "In order to make full use of what the upgraded APS will be capable of, we have to reinvent data analytics. Our current methods are not enough to keep up. Machine learning can make full use and go beyond what is currently possible," says Mathew Cherukara of the Argonne National Laboratory, corresponding author of the paper.

Computers have been able to quickly process 2D images for some time. Your cell phone can snap digital photographs and manipulate them in a number of ways. Much more difficult, however, is processing an image in three dimensions, and doing it in a timely manner. The mathematics are more complex, and crunching those numbers, even on a supercomputer, takes time. That's the challenge a group of scientists from the U.S. Department of Energy's (DOE) Argonne National Laboratory is working to overcome.