Feb. 17 — One of the main tools doctors use to detect diseases and injuries in cases ranging from multiple sclerosis to broken bones is magnetic resonance imaging (MRI). However, the results of an MRI scan take hours or days to interpret and analyze. This means that if a more detailed investigation is needed, or there is a problem with the scan, the patient needs to return for a follow-up.
A new, supercomputing-powered, real-time analysis system may change that.
Researchers from the Texas Advanced Computing Center (TACC), The University of Texas Health Science Center (UTHSC) and Philips Healthcare, have developed a new, automated platform capable of returning in-depth analyses of MRI scans in minutes, thereby minimizing patient callbacks, saving millions of dollars annually, and advancing precision medicine.
The team presented a proof-of-concept demonstration of the platform at the International Conference on Biomedical and Health Informatics this week in Orlando, Florida.
The platform they developed combines the imaging capabilities of the Philips MRI scanner with the processing power of the Stampede supercomputer – one of the fastest in the world – using the TACC-developed Agave API Platform infrastructure to facilitate communication, data transfer, and job control between the two.
An API, or Application Program Interface, is a set of protocols and tools that specify how software components should interact. Agave manages the execution of the computing jobs and handles the flow of data from site to site. It has been used for a range of problems, from plant genomics to molecular simulations, and allows researchers to access cyberinfrastructure resources like Stampede via the web.
“The Agave Platform brings the power of high-performance computing into the clinic,” said William (Joe) Allen, a life science researcher for TACC and lead author on the paper. “This gives radiologists and other clinical staff the means to provide real-time quality control, precision medicine, and overall better care to the patient.”
The entire article can be found here.
Source: Aaron Dubrow, TACC