Stampede Supercomputer Drives
Real-time MRI Analysis
February 20, 2017
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
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
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."
For their demonstration project, staff at UTHSC performed MRI scans on a
patient with a cartilage disorder to assess the state of the disease.
Data from the MRI was passed through a proxy server to Stampede where it
ran the GRAPE (GRAphical Pipelines Environment) analysis tool. Created
by researchers at UTHSC, GRAPE characterizes the scanned tissue and
returns pertinent information that can be used to do adaptive scanning -
essentially telling a clinician to look more closely at a region of
interest, thus accelerating the discovery of pathologies.
The researchers demonstrated the system's effectiveness using a T1
mapping process, which converts raw data to useful imagery. The
transformation involves computationally-intensive data analyses and is
therefore a reasonable demonstration of a typical workflow for
real-time, quantitative MRI.
A full circuit, from MRI scan to supercomputer and back, took
approximately five minutes to complete and was accomplished without any
additional inputs or interventions. The system is designed to alert the
scanner operator to redo a corrupted scan if the patient moves, or
initiate additional scans as needed, while adding only minimal time to
the overall scanning process.
"We are very excited by this fruitful collaboration with TACC," said
Refaat Gabr, an assistant professor of Diagnostic and Interventional
Imaging at UTHSC and the lead researcher on the project. "By integrating
the computational power of TACC, we plan to build a completely adaptive
scan environment to study multiple sclerosis and other diseases."
Ponnada Narayana, Gabr's co-principal investigator and the director of
Magnetic Resonance Research at The University of Texas Medical School at
potential of this technology is the extraction of quantitative,
information-based texture analysis of MRI," he said. "There are a few
thousand textures that can be quantified on MRI. These textures can be
combined using appropriate mathematical models for radiomics. Combining
radiomics with genetic profiles, referred to as radiogenomics, has the
potential to predict outcomes in a number diseases, including cancer,
and is a cornerstone of precision medicine."
According to Allen, "science as a service" platforms like Agave will
enable doctors to capture many kinds of biomedical data in real time and
turn them into actionable insights.
"Here, we demonstrated this is possible for MRI. But this same idea
could be extended to virtually any medical device that gathers patient
data," he said. "In a world of big health data and an almost limitless
capacity to compute, there is little reason not to leverage
high-performance computing resources in the clinic."