Automated Real-Time Quantitative Magnetic Resonance Imaging

William Allen • Refaat Gabr • Getaneh Tefera • Amol Pednekar • Si Liu • Hang Liu • Matthew Vaughn • Ponnada Narayana

09:05 - 09:55 | Thursday 16 February 2017 | Ballroom D


Magnetic resonance imaging (MRI) is invaluable in the detection of certain pathologies, but suffers from a lack of real-time quantitative analysis. Here, we present a platform that uses software automation and high performance computing (HPC) resources to achieve real-time analysis of MRI data. In this example use case, the Agave API facilitates data transfers between an MRI scanner and the Stampede supercomputer, then executes a graphical pipeline tool called GRAPE to perform T1 fitting of MRI scans from seven different inversion times. Same-session image processing will enable adaptive scanning and real-time quality control, potentially accelerating the discovery of pathologies and minimizing patient callbacks.