Automated Real-Time Quantitative Magnetic Resonance Imaging

William Allen1, Refaat Gabr2, Getaneh Tefera2, Amol Pednekar3, Si Liu4, Hang Liu4, Matthew Vaughn4, Ponnada Narayana5

  • 1University of Texas at Austin
  • 2The University of Texas Health Science Center at Houston
  • 3Philips Healthcare
  • 4Texas Advanced Computing Center
  • 5University of Texas Medical School at Houston



1-Page Late Breaking Paper (Poster)


09:05 - 09:55 | Thu 16 Feb | Ballroom D | ThRAF

Rapid Fire Session 01: Imaging Informatics


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.

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