Dr. Thomas Vaughan and his colleagues at Columbia are creating a scalable network of distributed research and diagnostics nodes to improve efficiency and scientific collaboration. Collecting, storing, and processing biomedical data in Google Cloud , in coordination with (Flywheel’s)[https://flywheel.io] innovative biomedical informatics platform, offers the potential for increasing accessibility, such as linking large networks of remotely located points-of-care via satellite. One day, magnetic resonance imaging could reach and serve global populations that are currently beyond reach.
As Professor of Biomedical Engineering, Radiology, and Applied Physics at Columbia University, and a principal investigator at Columbia’s Mortimer B. Zuckerman Mind Brain Behavior Institute, Vaughan has been a pioneer in developing advanced magnetic resonance imaging (MRI) systems and applications for biomedical research and diagnostics. He is currently heading the Columbia Magnetic Resonance Research Center (CMRRC) by connecting five Center nodes—Columbia’s Zuckerman Institute, Columbia University Irving Medical Center, Columbia's School of Engineering and Applied Sciences, the Nathan Kline Institute for Psychiatric Research, and the New York State Psychiatric Institute—to make the CMRRC the first MRI research center in the world to automatically collect, process, share, and store all data in the cloud. Vaughan and his team are also working with delegations in China, India, and with other collaborators around the world to explore whether this model of efficient, cost-effective research and diagnostics can be expanded on a global scale.
MR Imaging (MRI), MR Spectroscopy (MRS), and functional MRI (fMRI) are powerful tools for non-invasive acquisition of high-resolution and high-contrast structural, metabolic, and physiological data for research and diagnostics of the human body in health, disease, and therapeutic intervention. Unlike complementary imaging modalities using ionizing radiation (CT), MRI can be used to acquire images safely over the lifetime of a patient or volunteer. According to the World Health Organization, however, 90% of the world does not have access to MR systems or their diagnostics benefit. By centralizing talent and technology resources into a central hub and extending this resource support to multiple, minimally staffed and equipped points-of-care, experts argue that the cost, inefficiencies, and redundancies of healthcare diagnostics and scientific data collection can be conserved. The tools harnessed here by Vaughan and his colleagues represent an important step toward that vision.
In the future, Vaughan says, “we could conceivably collect data from infancy on. We could provide point-of-care diagnostics. We could scale this to the size of the planet. That’s where we want to get to. Not just with the cloud and technology, but with overall diagnostics and therapy.” He believes that collecting more data from more diverse populations and processing those datasets more efficiently will improve patient care and accelerate biomedical research.